121 results about "Uncertainty model" patented technology

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 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 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.

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 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 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 discloses a differential transformer measurement precision uncertainty determination method and system, and the method comprises the steps: calculating a driving signal frequency influence quantity, determining a displacement-voltage conversion slope deviation caused by the coil mutual inductance coefficient change due to the signal frequency drift of a sensor driving coil, calculating a waveform transmission efficiency influence quantity, and determining the measurement precision uncertainty of a differential transformer. In consideration of differential transformer iron core eddy-current loss difference caused by non-sinusoidal waveform harmonic components and energy loss caused by a sampling system bandwidth threshold, full-travel interpolation errors caused by interpolation calculation of non-equally-divided calibration points are quantized by calculating a finite position linearity influence quantity, and the total-travel interpolation error is calculated by multi-aspect component estimation. And determining the field measurement uncertainty of the differential transformer principle sensor in combination with a multi-factor uncertainty model. Therefore, the uncertainty value of the physical quantity measured by the corresponding sensor under the field condition is obtained, and effective data support is provided for determining the precision of the sensor and the test and control precision.

This application discloses a method for calculating the thrust envelope of a solid rocket motor based on servo uncertainty, relating to the field of solid rocket motor technology. This method focuses on the gas valve servo system of a throat-operated solid rocket motor, performing uncertainty modeling of the motor drive system and transmission system, analyzing the main influencing parameters of servo system uncertainty, including transmission clearance, eccentric shaft friction coefficient, and other uncertainty parameters and their probability distributions; generating random parameter samples based on the uncertainty model, and using the servo system control model to solve for the actual throat displacement response under random samples, obtaining the engine thrust time history under single-valve and multi-valve cooperative working modes; and fitting the thrust uncertainty envelope by calculating the mean and standard deviation of the thrust response through Monte Carlo simulation. This application quantitatively characterizes the degree of influence of internal factors of the servo mechanism on thrust performance, providing a reference for ensuring precise thrust control of solid rocket motors.

The invention provides a carbon emission reduction accounting method and system based on multi-modal uncertainty quantization, and relates to the technical field of carbon emission reduction accounting, and the method comprises the following steps: collecting and preprocessing the multi-modal data of a reconstructed building, and obtaining a preprocessed multi-modal data set; uncertainty modeling is carried out on the preprocessed multi-modal data set, and uncertainty distribution is obtained; based on the preprocessed multi-modal data set and uncertainty distribution, performing inversion by adopting a Bayesian framework to obtain a multi-modal data posterior probability; and performing Monte Carlo simulation on the posterior probability of the multi-modal data to obtain an uncertainty weight, and calculating the carbon emission reduction based on the uncertainty weight. According to the method, quantitative modeling is carried out on the uncertainty of the multi-modal data, the probability transmission and fusion of the uncertainty are realized by using the Bayesian framework, and finally the carbon emission reduction amount accounting result with the confidence interval is output, so that the scientificity and the credibility of the accounting are remarkably improved.

The invention discloses a vehicle trajectory tracking control method and system capable of resisting parameter fluctuation and data packet loss, which are used for solving the problem that the control performance is reduced under the conditions of kinetic parameter fluctuation and data packet loss in the prior art. According to the method, a kinetic parameter uncertainty model is established through a bounded time-varying matrix, and a multi-modal system model comprising four sub-systems is constructed based on packet loss characteristics of a control instruction and state information. Dynamic behaviors of the system in different network states are uniformly described by adopting a Markov chain, the asymptotic stability of the system under the conditions of parameter disturbance and data packet loss is ensured by combining a Lyapunov stability theory and H-infinity control constraint, and the trajectory tracking precision and the driving stability are remarkably improved. In order to facilitate engineering realization, an original non-convex control problem is converted into a convex optimization problem which can be solved on line through a cone compensation linearization method. According to the method, the trajectory tracking robustness of the vehicle under the complex working condition is effectively improved, and the method has important engineering application value.

The invention discloses an attention mechanism-based ore target detection method under a weak vision identification condition, which comprises the following steps of: firstly, acquiring an ore conveyor belt image, performing standardized preprocessing, inputting the image into a trained ore target detection model, and acquiring a detection frame result of ore in a specified size range; the ore target detection model comprises a weak contrast feature extractor, a shadow feature extraction module, a multi-scale feature fusion module, an uncertainty modeling module and a non-maximum suppression ore bounding box prediction module. According to the method, the weak contrast feature extractor is constructed, shadow feature extraction, multi-scale feature fusion and texture feature extraction are comprehensively utilized, ore features and context details are effectively captured, and a non-maximum value is utilized to suppress an ore bounding box prediction module; according to the ore features extracted in the early stage of the model, the confidence score of the bounding boxes and the overlapping degree between the bounding boxes, the accurate ore target detection box is output, and the accuracy and reliability of ore target detection are remarkably improved.

The invention provides an electrochemical energy storage power station planning operation collaborative optimization method considering new energy power generation and load demand uncertainty, and relates to the field of electrochemical energy storage planning and operation. Performing abnormal data processing on the measured value; calculating a relative error value between the standardized source load data and the predicted value, and optimizing mixed parameters of a pre-configured Gaussian mixture model in combination with an adaptive momentum regularization expectation maximization algorithm; constructing an electrochemical energy storage power station configuration optimization model in combination with the source load uncertainty model; and based on an electrochemical energy storage power station configuration optimization model result, combining an electrochemical energy storage power station power balanced distribution strategy of a health degree attenuation effect to realize energy storage power station power distribution. According to the method, the source-load probability distribution is converted into a deterministic power system flexibility requirement by setting the confidence level, a premise is provided for the optimization configuration of a subsequent electrochemical energy storage power station, and the planning reasonability of the energy storage power station is ensured.

The application discloses a power and electricity balance method based on fuzzy opportunity constraint conversion and a computer device, and the method comprises the following steps: constructing a tolerance degree based on power shortage risk, taking a comprehensive objective function of conventional power generation cost, standby capacity cost, new energy abandoned electricity cost and uncertainty penalty term, taking system constraints, unit constraints, unit electricity constraints, cross-section safety constraints and energy storage constraints as constraint conditions, and improving the accuracy of a medium and long term power and electricity balance model under the consideration of single modeling of new energy uncertainty, thereby improving the economy and safety of the power system. Through the establishment of the uncertainty modeling mode of long-period power and electricity balance, the fuzzy opportunity constraint and the gradient penalty coefficient, the economy and reliability can be flexibly balanced, the redundant cost of standby capacity is reduced by converting the fuzzy boundary into a deterministic boundary, and through risk quantification, the decision transparency can be supported and the interpretability can be ensured.

The application discloses a kind of laboratory safety risk intelligent early warning method, system, medium and product, it is related to risk early warning field.The method comprises the following steps: calibrating laboratory digital twin model based on real-time monitoring data, generating initial probability distribution based on laboratory digital twin model and fusing preset equipment failure probability model and personnel behavior uncertainty model;The probability density distribution change of initial probability distribution at a plurality of time points in the future is deduced by numerical algorithm, and the corresponding state prediction probability distribution is obtained;The probability value of each dangerous state developed at each time point in the future is obtained based on the state prediction probability distribution of each future time point;When the probability value of any dangerous state evaluated exceeds the preset threshold value, the optimal control operator is solved reversely by optimization algorithm, and the intervention action corresponding to the optimal control operator is executed.The above technical scheme can predict laboratory safety risk in advance.

This invention discloses a method for optimizing the dynamic transmission error distribution characteristics of a harmonic reducer, comprising: establishing a static transmission error probability model to obtain the probability distribution of the overall static transmission error; constructing a dynamic transmission error mathematical model considering static transmission error and dynamic parameters; constructing a high-precision surrogate model of dynamic transmission error including the probability distribution of static transmission error and the range of dynamic parameters; obtaining the probability distribution of dynamic transmission error; and dynamically adjusting the range of dynamic parameters based on a particle swarm optimization strategy to find the parameter range that optimizes the dynamic transmission error distribution. By introducing static transmission error into the dynamic model of the harmonic reducer, considering the influence of the processing and assembly of the harmonic reducer on the transmission error, a probability-range hybrid uncertainty model is used to mathematically describe the probability distribution characteristics of static transmission error and the range of dynamic parameters, and a Chebyshev polynomial method is used to construct an approximate model of dynamic transmission error.

This invention discloses a structural topology optimization design method considering the uncertainty of multi-peak loads, belonging to the field of uncertain structural optimization design. It mainly includes three parts: establishing a multi-peak load uncertainty model, solving for the random response, and solving for the optimization formula. This invention describes load uncertainty using a Gaussian mixture model, solves for the Gaussian mixture model coefficients based on the EM algorithm, and establishes a multi-peak load distribution probability model. By decorrelating random variables, sparse grid technology is used to solve for the mean, standard deviation, and sensitivity of the response, thereby solving for the topology optimization model considering the multi-peak load uncertainty. This method addresses the problem of low structural product reliability that may occur in structural designs considering multi-peak load uncertainty by establishing an accurate probabilistic model of multi-peak load uncertainty. This method is simple and easy to implement, readily applicable in engineering, and can improve the design efficiency of structural designers considering complex load conditions.

This invention provides a method for predicting the dominant frequency of blasting vibration by integrating parameter fluctuation processing and the Dream Optimization Algorithm (DOA), belonging to the field of underground engineering safety control technology. The system includes a data acquisition and uncertainty modeling module, a data preprocessing and feature construction module, a hyperparameter optimization module, and a prediction model training and result output module. The method obtains relevant parameters through on-site investigation and monitoring, and generates an extended sample set using a combination of probabilistic perturbation modeling and fuzzy triangular modeling. The data is preprocessed and features are selected. The DOA algorithm is used to globally search the hyperparameters of the Support Vector Regression (SVR) model, and the optimal hyperparameter combination is selected by combining a robustness fitness function. The SVR model is trained, and prediction results and uncertainty prediction intervals are generated. This invention can explicitly characterize the uncertainty of geological parameters, achieve efficient global optimization of hyperparameters, improve prediction accuracy, robustness, and generalization ability, and is applicable to different geological conditions and blasting scenarios. It can be extended to various blasting dynamic response prediction tasks.

The present disclosure relates to the field of iris recognition technology and provides an iris recognition method, apparatus, medium, and device based on uncertainty modeling, comprising: obtaining iris image data and inputting it into a pre-built iris recognition model; performing pixel-level modeling on the image data using an uncertainty modeling module to extract the mean and variance features of each pixel, and generating an uncertainty feature representation and an iris feature template based on the extraction results and a re-parameter sampling strategy; generating a variance scaling map based on a dynamic mask generation module and image data, and generating a dynamic feature mask using the variance scaling map and a dynamic threshold judgment strategy; and determining target identity information based on a preset iris template database, the iris feature template, and the dynamic feature mask to complete identity recognition. By combining the iris feature template and the dynamic feature mask of the iris image, the present disclosure improves the accuracy and robustness of iris recognition, maintaining high recognition accuracy under varying conditions such as different lighting conditions.

The invention discloses an uncertainty model order reduction and damage identification method based on a neural network and a substructure, and belongs to the field of civil engineering, and the method comprises the steps: obtaining physical parameters of the substructure of a finite element model, obtaining an uncertainty main mode of the substructure through uncertainty analysis, building an uncertainty substructure price reduction model, and carrying out the calculation of the uncertainty main mode of the substructure; training the uncertainty substructure reduced-order model under the structure parameters with different mean values and standard deviations through a neural network to obtain a parameterized uncertainty substructure reduced-order model; constructing a parameterized uncertainty overall structure reduced-order model, and obtaining an overall structure dynamic response statistical moment according to the parameterized uncertainty overall structure reduced-order model; and correcting the finite element model according to the dynamic response statistical moment, and identifying the uncertain structural damage in the finite element model based on the dynamic response statistical moment to obtain a damage identification result so as to realize rapid and effective analysis and damage identification of the uncertain factors of the civil engineering structure.

This application provides a stochastic optimization scheduling method and apparatus for renewable energy bases considering the uncertainties of wind-solar combined power generation, relating to the field of energy system optimization scheduling technology. The method includes: inputting meteorological forecast data into a pre-constructed wind-solar output uncertainty model to generate multiple wind-solar combined power generation scenarios; the uncertainty model is used to generate wind-solar output scenarios with multi-timescale characteristics based on the input meteorological conditions; constructing a stochastic optimization scheduling model containing power generation operation constraints with the objective of maximizing the operating benefits of the renewable energy base; inputting multiple wind-solar combined power generation scenarios into the stochastic optimization scheduling model and solving it to obtain an optimized scheduling scheme coordinating wind, solar, and solar thermal power generation. Thus, by combining empirical mode decomposition with conditional generative adversarial networks, high-quality combined scenarios that retain multi-timescale characteristics and dynamic correlations of wind and solar output are generated, achieving synergistic optimization of economic benefits and operational safety while ensuring power supply reliability.

The invention discloses a power distribution network consumption capability evaluation method and system based on an SARIMA-DCC-GARCH photovoltaic model. The method comprises the following steps: constructing a photovoltaic output uncertainty model considering dynamic spatial correlation; establishing an uncertainty model of the charging load of the electric vehicle based on probability statistics and a Monte Carlo simulation method, and forming a typical load disturbance scene; a power flow constraint model based on DistFlow linearization and affine transformation is constructed, linear influence of source load disturbance on node injection power and voltage amplitude is expressed, collaborative optimization scheduling of flexible resources is considered at the same time, economy and safety of system operation are considered, a photovoltaic consumption capability evaluation model is established under uncertainty disturbance, and an evaluation result is obtained. According to the method, the operation safety of the system is guaranteed, the photovoltaic consumption capability is evaluated under the uncertain disturbance of the system, and a theoretical basis and a technical support are provided for optimal scheduling of the active power distribution network.