Patents
Literature
53 results about "Vine copula" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
A vine is a graphical tool for labeling constraints in high-dimensional probability distributions. A regular vine is a special case for which all constraints are two-dimensional or conditional two-dimensional. Regular vines generalize trees, and are themselves specializations of Cantor trees. Combined with bivariate copulas, regular vines have proven to be a flexible tool in high-dimensional dependence modeling. Copulas are multivariate distributions with uniform univariate margins. Representing a joint distribution as univariate margins plus copulas allows the separation of the problems of estimating univariate distributions from the problems of estimating dependence. This is handy in as much as univariate distributions in many cases can be adequately estimated from data, whereas dependence information is rough known, involving summary indicators and judgment. Although the number of parametric multivariate copula families with flexible dependence is limited, there are many parametric families of bivariate copulas. Regular vines owe their increasing popularity to the fact that they leverage from bivariate copulas and enable extensions to arbitrary dimensions. Sampling theory and estimation theory for regular vines are well developed and model inference has left the post . Regular vines have proven useful in other problems such as (constrained) sampling of correlation matrices, building non-parametric continuous Bayesian networks.
Wind-solar-water multi-energy system short-term scheduling decision-making method considering source load uncertainty
The invention provides a wind-light-water multi-energy system short-term scheduling decision-making method considering source load uncertainty. The method comprises the following steps: acquiring a source load scene set with relevance by applying a hierarchical normalized flow vine Copula generator based on an hour-by-hour source load time sequence; carrying out scene reduction on the generated source load scene set by adopting a density sensitive synchronous reduction algorithm, and extracting a typical source load scene subset; inputting the typical source load scene subset into a rigid-elastic polymorphic constraint transfer double-layer scheduling model, and solving the model by adopting a double-channel elite iterative algorithm to obtain a multi-target solution; and carrying out risk feature decoupling and risk feature tensor construction on the multi-target solution, thereby obtaining an optimal scheduling scheme by adopting an entropy-risk topological mapping decision model. According to the method, the economical efficiency, the safety and the decision-making credibility of short-term scheduling of the system are improved.
Owner:HOHAI UNIV
Cooperative transaction method, system and device for multiple virtual power plants and readable medium
The invention relates to a multi-virtual power plant collaborative transaction method, system and device and a readable medium, and the method comprises the steps: constructing a multi-energy coupling model containing a gas turbine, an energy storage device, biomass power generation and a new energy vehicle, and generating a multi-source data set to quantify the carbon emission cost and charge and discharge constraints; a wind and light output joint probability distribution model is generated based on an F-vine Copula distribution function, and a severe scene set is screened through random sampling and scene closeness evaluation; designing a renewable energy source-load correlation coefficient and a green energy consumption compensation mechanism, and correcting a virtual power plant scheduling objective function; establishing a double-layer dynamic transaction mechanism, and iteratively optimizing an upper-layer dynamic electricity price and a lower-layer multi-source transaction electricity quantity; a multi-stage robust optimization algorithm is adopted to process the uncertainty of wind, light and electric vehicles, and a transaction strategy among multiple virtual power plants in a severe scene is generated, so that the problems of low-carbon excitation deficiency, insufficient multi-source uncertainty coping and the like are solved, and unification of economy, low-carbon property and robustness of collaborative scheduling transaction of the multiple virtual power plants is realized.
Owner:FUZHOU ONE SUN POWER CONSULTING
Mechanical structure reliability evaluation method based on hybrid R-Vine Copula model
PendingCN120234949AGeometric CADDesign optimisation/simulationExpectation–maximization algorithmTree pattern
The invention discloses a mechanical structure reliability evaluation method based on a hybrid R-Vine Copula model, and the method comprises the steps: firstly, selecting an optimal R-Vine tree type according to sample data by employing an AIC criterion and the basic definition of an R-Vine tree type, employing a hybrid Pair-Copula function for the first layer of R-Vine tree type to improve the sample adaptability, employing Clayton, Gumbel and Frank Copula functions to construct a hybrid Copula function, and employing a Pair-Copula function to construct a Copula model; calculating a weight coefficient and a dependent parameter of the mixed Pair-Copula function by using an expectation maximization algorithm; a subsequent tree type is constructed by adopting a single Pair-Copula function, so that the optimal balance between the calculation efficiency and the calculation precision is achieved, and the optimal construction based on the mixed R-Vine Copula model is completed; and finally, solving the reliability index and the failure probability of the structure based on a mixed R-Vine Copula model in combination with an improved first-order second-order moment method.
Owner:CHANGSHA UNIVERSITY OF SCIENCE AND TECHNOLOGY
Method for generating typical scene of watershed water, wind and light based on improved C-vine Copula
ActiveCN120493692ADesign optimisation/simulationComplex mathematical operationsStreaming dataPower station
The invention relates to an improved C-vine Copula-based basin water-wind-light typical scene generation method, which comprises the following steps of: firstly, based on a long-term multi-energy complementary demand, screening runoff data of a certain power station for many years and output data of a wind-light power station in a basin, removing abnormal values, filling missing values and finishing data preprocessing; thirdly, setting runoff and wind and light output as random variables, and obtaining an edge distribution function of each variable by applying nonparametric kernel density estimation; and then an improved C-vine Copula model is used to accurately describe the spatial-temporal correlation between water, wind and light, and joint probability distribution is obtained. Then, Latin hypercube sampling is adopted, uniform random samples are collected in a layered mode, and a typical scene is generated through K-means clustering; finally, the scene effectiveness is evaluated from the aspects of time and space correlation, randomness and the like. The technology can effectively deal with randomness and complexity of water, wind and light resources, generates a typical scene fitting reality, and provides a valuable reference basis for planning, scheduling and other work of a watershed water, wind and light multi-energy complementary system.
Owner:CHINA YANGTZE POWER
Water conservancy equipment service life prediction and fault monitoring method
The invention discloses a water conservancy equipment life prediction and fault monitoring method. The method comprises the following steps: multi-modal data acquisition and dynamic preprocessing: constructing an acquisition frequency adaptive model; multi-domain fusion feature extraction and adversarial dimension reduction: multi-domain feature extraction is carried out, then a generator of a Wasserstein GAN architecture is constructed to reconstruct original features, a discriminator calculates a Wasserstein distance, the generator is forced to reconstruct interlayer features of the discriminator through feature matching loss, and high-correlation features are screened in combination with a maximum information coefficient and a Pearson's correlation coefficient; constructing and training a gated attention fusion network; and constructing a fault monitoring model based on a Copula theory: capturing a normal operation mode of equipment, then estimating feature edge distribution through an empirical distribution function corrected by KDE, constructing a D-vine Copula structure to hierarchically capture nonlinear dependency, calculating an AnmallyScore index integrating edge anomaly and correlation sudden change penalty, and triggering an alarm when the AnmallyScore index is greater than a threshold value.
Owner:ZHEJIANG UNIV OF WATER RESOURCES & ELECTRIC POWER
Tunnel defect detection spatial resolution enhancement method and device based on Vine Copula multivariable dependence modeling, computer readable medium and computer program product
PendingCN120632317AMathematical modelsComplex mathematical operationsConditional random fieldStatistical correlation
The invention belongs to the field of tunnel engineering, and relates to a tunnel defect detection data spatial resolution enhancement method and device based on a Vine Copula dependent structure and a conditional random field, a computer readable medium and a computer program product. The method comprises the following steps: firstly, separating an overall trend and random fluctuation from original tunnel defect monitoring data; then establishing a Vine Copula multivariable dependence model to represent a statistical correlation relationship among the plurality of defect indexes; and then generating defect distribution data with high spatial resolution by using a conditional random field interpolation simulation technology in combination with a spatial autocorrelation analysis result. According to the invention, while the accuracy of the existing measuring point data is ensured, the spatial correlation structure and multivariable joint distribution characteristics of the defect data are maintained, and the precision of spatial interpolation prediction is improved.
Owner:HUAZHONG UNIV OF SCI & TECH
Flap mechanism dynamic global sensitivity analysis method based on Vine Copula
The invention discloses a dynamic global sensitivity analysis method for a flap mechanism based on Vine Copula. The method comprises the following steps: firstly, performing rigid-flexible coupling simulation on a movement mechanism, identifying a stress dangerous area, a deformation dangerous area and a clamping stagnation dangerous area of the movement mechanism through a stress distribution condition, and providing a new structure failure criterion according to a dynamic multi-parameter coupling idea, namely a time-varying stress-strain-driving force combined criterion; random parameters are definitely input according to the motion characteristics of the flap mechanism; secondly, establishing a time-varying correlation model considering autocorrelation and cross-correlation based on a Vine Copula theory and a random process model, generating a condition sample by using the model, and generating a required input random variable through inverse transformation; dividing the dynamic sample into a plurality of subintervals by using a space division method, and performing Sobol index solving, so as to solve the importance measure of each input parameter in the movement process of the flap mechanism;
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Intelligent flood forecasting method for coupling error correction and joint modeling
PendingCN121390431AMathematical modelsWeather condition predictionExpectation–maximization algorithmFlood forecast
The invention discloses an intelligent flood forecasting method for coupling error correction and joint modeling, and relates to a deep learning and uncertainty modeling technology. At the input end, constructing a future random rainfall scene through hourly dynamic normal disturbance; the method comprises the following steps: at a model end, introducing a multi-structure and multi-objective function combination based on Kolmogorov-Arnold Networks and Transform, and forming a multi-member ensemble forecast; at an error end, a probabilistic modeling method based on a numerable asymmetric Laplacian mixed density network is provided, and fine error correction is realized; a Vine copula function is adopted to construct high-dimensional joint distribution, and a Bayesian model averaging and expectation maximization algorithm is combined to realize weighted fusion of multi-member posterior results; according to the method, uncertainty in flood forecasting can be comprehensively described, the stability and adaptability of a forecasting system are improved, and the method is suitable for a basin-level real-time flood ensemble forecasting scene.
Owner:HOHAI UNIV +2
Multi-element source-load coupled distributed wind power access capacity calculation method
PendingCN121529774AForecastingSingle network parallel feeding arrangementsElectric power systemSimulation
The invention discloses a multi-element source-load coupled distributed wind power access capacity calculation method, which belongs to the technical field of power systems, and comprises the following steps: constructing a multi-element source-load probability model, and quantifying the probability distribution of wind power, photovoltaic and flexible loads; a dynamic vine Copula correlation model is constructed, and wind power-photovoltaic-flexible load joint probability distribution is obtained; generating a multi-element source load operation simulation scene set based on wind power-photovoltaic-flexible load joint probability distribution, and reducing simulation scenes; constructing a distributed wind power access capacity optimization model objective function; and solving the objective function by adopting a Benders decomposition algorithm to obtain an optimal distributed wind power capacity configuration scheme. By adopting the method, the nonlinear and time-varying correlation between the photovoltaic load and the flexible load can be accurately captured, meanwhile, the stochastic chance constraint programming model considering the uncertainty of the source load is constructed, the economic optimization is realized, and the calculation efficiency is greatly improved.
Owner:STATE GRID SHANXI ELECTRIC POWER CO ECONOMIC & TECH RES INST
Cascade water-wind-light multi-power system modeling method based on time-space correlation
PendingCN121073311AData processing applicationsSingle network parallel feeding arrangementsHeteroscedastic modelEngineering
The invention discloses a cascade water-wind-light multi-power system modeling method based on time-space correlation, and the method comprises the steps: building an autoregression integral moving average-generalized autoregression condition heterovariance model of wind power photovoltaic power and cascade hydropower station runoff, building an R-vine, C-vine and D-vine Copula function spatial correlation model through employing a generalized autoregression condition heterovariance value, and carrying out the modeling of a cascade water-wind-light multi-power system. And selecting an optimal spatial correlation model according to a Bayesian information criterion, an akaike information criterion and a logarithm likelihood value goodness of fit index. Based on the optimal spatial correlation model, constructing a scene generation method of Latin hypercube-Copula function sampling, performing scene generation on the wind power photovoltaic power and the runoff volume of the cascade hydropower station, and constructing a scene reduction method of contour coefficient optimization Kmeans clustering to reduce the generated scene. The invention provides a time-space correlation cascade water-wind-light multi-power system modeling method, and aims to analyze water-wind-light multi-energy complementary operation characteristics and provide reference for a scheduling mode among water-wind-light multi-energy.
Owner:SDIC GANSU XIAOSANXIA POWER CO LTD +1
Power system operation risk key feature extraction method, system, equipment and medium
The invention discloses a power system operation risk key feature extraction method, system and device and a medium, and the method comprises the steps: giving a historical random variable, and carrying out the first processing, and obtaining low-dimensional clustering data; constructing a conventional graphic vine model, determining optimal parameters of the conventional graphic vine model based on the low-dimensional clustering data, and generating a sampling sample set; performing simulation before and after a fault based on the sampling sample set, and extracting features and labels of the training set and the test set by using an encoder; and constructing a power system safety rule according to the extracted features, and performing performance evaluation on the power system safety rule. According to the method, the R-vine Copula model and the depth automatic encoder are combined, so that the accuracy and robustness of risk assessment of the power system are remarkably improved. The method not only solves the problems of data imbalance and complex dependence caused by high-proportion new energy access, but also is superior to a traditional method in key indexes such as precision and F1 score, and provides more reliable decision support for safe operation of a power system.
Owner:GUIZHOU POWER GRID CO LTD +2
A method for generating typical scenes of water and scenery in a basin based on improved C-vine Copula
ActiveCN120493692BBalance complexityBalanced fitting accuracyDesign optimisation/simulationComplex mathematical operationsComplete dataPower station
A method for generating typical watershed hydro-wind-solar hybrid scenarios based on an improved C-vine Copula model is proposed. First, based on long-term multi-energy complementarity requirements, multi-year runoff data from a power station and power output data from wind and solar power stations within the watershed are selected, outliers are removed, and missing values ​​are filled, completing data preprocessing. Next, runoff and wind / solar output are set as random variables, and nonparametric kernel density estimation is used to obtain the marginal distribution functions of each variable. Then, the improved C-vine Copula model is used to accurately characterize the spatiotemporal correlation between water, wind, and solar resources, deriving the joint probability distribution. Then, Latin hypercube sampling is used to collect uniformly random samples stratified, and K-means clustering is used to generate typical scenarios. Finally, the effectiveness of the scenarios is evaluated from the perspectives of temporal and spatial correlation and randomness. This technology can effectively address the randomness and complexity of water, wind, and solar resources, generating realistic typical scenarios, providing valuable reference for the planning and scheduling of watershed hydro-wind-solar hybrid systems.
Owner:CHINA YANGTZE POWER
A distribution robust frequency modulation capacity evaluation method and system based on vine copula and spatiotemporal graph network
PendingCN122512417Asafe and stable operationAccurate dynamic frequency evolution trajectoryEngineeringTerm memory
This disclosure relates to a method and system for assessing frequency regulation demand capacity based on Vine Copula and a spatiotemporal graph network. The method includes: acquiring historical power and prediction error data of multi-dimensional source-load nodes in the power system to generate a typical power deficit scenario set; transforming the power system into a graph data structure, constructing and training a graph neural network-long short-term memory network spatiotemporal proxy model; integrating dynamic load shedding mechanisms and virtual synchronization control, and combining the prediction and verification results of the maximum frequency change rate and the minimum maximum frequency drop of each node by the graph neural network-long short-term memory network spatiotemporal proxy model to establish a dynamic frequency safety boundary; and based on the dynamic frequency safety boundary and the typical power deficit scenario set, establishing a two-stage sub-Bluerstein bar optimization model based on Wasserstein distance to solve and determine the optimal frequency regulation demand capacity configuration scheme for each node in the system.
Owner:PINGHU GENERAL ELECTRIC INSTALL CO LTD +1
Financial data generation method, system and device based on vine copula and storage medium
The application provides a financial data generation method, system and device based on vine copula and a storage medium, and comprises the following steps: obtaining financial data, converting the financial data into data conforming to the definition domain of a vine copula model, and saving corresponding conversion rules; determining the structure of the vine copula model and estimating corresponding parameters by using the maximum likelihood estimation method based on the converted financial data; generating model data based on the determined vine copula model; and converting the model data into data consistent with the original data scale by reversely applying the conversion rules, so as to obtain financial data based on vine copula. In order to solve the problems in the prior art, the method is a new data generation technology, and is used for solving the problem that a financial institution cannot establish a model or the effect of the model is poor due to data shortage. The technology is based on a probability statistical model, and can mine the dependency relationship between data features while generating new data.
Owner:INST FOR INTERDISCIPLINARY INFORMATION CORE TECH XIAN CO LTD
New energy output power random generation method based on hybrid distribution function and high-dimensional vine Copula function
PendingCN120528023AData processing applicationsSingle network parallel feeding arrangementsElectricity systemAlgorithm
The invention discloses a new energy output power random generation method based on a hybrid distribution function and a high-dimensional vine Copula function, and the method comprises the steps: collecting wind and light natural resource data and engineering data, calculating the hour-scale wind and light output power, and building a wind and light output feature descriptive index system; defining a discrete-continuous hybrid marginal distribution function, and describing intermittent characteristics of hour-scale wind and light output power; constructing a high-dimensional mixed vine Copula function of a self-defined structure, and describing correlation characteristics between wind and light output power in each hour in a day; and generating a wind and light intra-day output power scene set through random simulation based on a discrete-continuous hybrid marginal distribution function and a high-dimensional vine Copula joint distribution function of a self-defined structure. The method can accurately simulate the hourly-scale wind and light output power value and maintain the intermittent and correlation characteristics, and has important significance for planning the operation of a novel power system taking new energy as a main body.
Owner:HOHAI UNIV
Joint uncertainty assessment method for wind power integration into power system based on time-varying fluctuation
ActiveCN120999749BGeneration forecast in ac networkData processing applicationsNew energyElectric power system
The application provides a wind power access power system uncertainty joint evaluation method based on time-varying fluctuation, belongs to the technical field of power system uncertainty evaluation and dispatching auxiliary decision-making, and solves the technical problem that the existing method causes the low coverage and unreasonable interval width of the interval prediction result of the combined output of wind and light due to the rough residual fitting and the insufficient correlation description. The application firstly obtains the edge distribution based on short-term prediction and error fluctuation modeling, then constructs a joint distribution by using R vine Copula, outputs the prediction interval of each station and value under the given confidence, and gives the coverage and interval width indexes, so that the day-ahead or day-in dispatching can be connected, and rolling update and online deployment are realized. The application significantly reduces the interval bandwidth while ensuring the coverage, improves the robustness of the fast changing period, and enhances the safety and economy of new energy consumption.
Owner:HUANENG POWER INT ENERGY DEV CO LTD +2
Station network optimization method based on high-dimensional Copula entropy and Kriging
ActiveCN114595556BData processing applicationsDesign optimisation/simulationHydrometryMultivariate mutual information
The present invention discloses a station network optimization method based on high-dimensional Copula entropy and Kriging, including: (1) constructing a hydrological C-Vine Copula tree structure; (2) estimating C-Vine Copula parameters by the maximum likelihood estimation method; (3) obtaining high-dimensional mutual information through the functional relationship between multivariate mutual information and C-Vine Copula density; (4) optimizing the dynamic rain gauge network by the standardized MiK-MiT-MaJ index and the sliding window method. The present invention uses C-Vine Copula to obtain the high-dimensional dependence structure among multiple stations, realizes the optimization of the total information quantity and total correlation quantity of the station network objective function; uses the Kriging standard error value to achieve the optimal estimation error of the rain gauge network and the optimal rain information; simplifies the multi-objective optimization to a single-objective optimization to improve the optimization efficiency, and considers the dynamic characteristics of the station network optimization result caused by the time-varying characteristics of the rainfall sequence.
Owner:YANGZHOU UNIV
Ramp demand node mapping method based on multi-source contribution degree allocation
The application discloses a method for mapping climbing demand nodes based on multi-source contribution degree distribution, and relates to the technical field of power system operation and control. The method comprises the following steps: obtaining initial weights of source and load nodes and system-level climbing demand; constructing a multi-dimensional joint distribution model reflecting the correlation of fluctuations of heterogeneous resources by using a Vine Copula function, and calculating dynamic contribution degree coefficients; performing spatial mapping calculation according to the dynamic contribution degree coefficients to obtain an expected power change component; constructing a cooperative game model, quantifying marginal contributions to network congestion elimination by using Shapley values, and distributing capacity relaxation; and finally generating a mapping scheme meeting node physical constraints. The application is used to solve the problems of mapping precision distortion under a multi-source random fluctuation environment and the difficulty of existing schemes in considering network congestion constraints.
Owner:HEFEI POWER SUPPLY COMPANY OF STATE GRID ANHUI ELECTRIC POWER +2
Modeling Method, Device, Terminal and Medium for High-Temperature Scenario Simulation Model of Power System
The present application discloses a method, device, terminal and medium for modeling a high-temperature scenario simulation model of a power system, relating to the technical field of power system operation simulation. The solution provided by the present application characterizes the correlation characteristics between multi-dimensional meteorological factors through the R-vine copula function to generate a Markov multi-dimensional transition kernel; establishes a double-layer Markov chain weather model, where the upper-layer model describes the transition of typical weather states, the lower-layer model divides the 24 hours within a day into time periods, and establishes a state transition matrix within each time period, generates a non-stationary change time series of intra-day meteorological factors based on the multi-dimensional transition kernel, integrates the double-layer model to obtain a complete high-temperature scenario, realizes more accurate characterization of the non-linear dependence relationship between multiple variables in the high-temperature scenario, thereby obtaining a more accurate extreme high-temperature weather sequence, which helps to improve the accuracy of the reliability assessment of the high-temperature scenario of the power system.
Owner:ZHUHAI POWER SUPPLY BUREAU GUANGDONG POWER GIRD CO
Multi-sensor missing data restoration method and system based on optimal D-vine copula
The invention relates to the technical field of monitoring data restoration, and particularly discloses a multi-sensor missing data restoration method and system based on optimal D-vine copula, and the method comprises the following steps: S1, decomposing high-dimensional copula into a product of pairwise copula, organizing the product through a tree structure, and simplifying high-dimensional modeling; s2, determining an optimal D-vine copula structure: efficiently realizing optimal sorting of variables in a first D-vine copula tree by utilizing a shortest Hamiltonian path method, and considering the change of marginal probability density distribution of each variable along with time; s3, performing non-parametric Pair-Copula estimation: adopting a non-parametric method to improve the fitting precision of each two-dimensional Copula in the D-line Copula, and avoiding a fitting error possibly brought by a parametric method; and S4, establishing a joint conditional probability density function about the occurrence of missing sensor data at the same moment under the condition that other intact sensor data are known by utilizing the optimal D-vine Copula, and further solving the missing sensor data by maximizing the function by taking the conditional probability density function as a target, thereby realizing missing value restoration.
Owner:XIAMEN UNIV
Power system probabilistic load flow calculation method and system based on R-vine Copula
PendingCN120449381AGeometric CADSingle network parallel feeding arrangementsOperations researchIndustrial engineering
The invention relates to the technical field of power grids, in particular to an R-vine Copula-based probabilistic load flow calculation method and system for a power system. According to the method, wind power plant prediction error data including short-term, medium-term and long-term prediction errors and actual output data in a power system are obtained, and an R-vine Copula model is used for modeling to generate a wind power plant output scene; secondly, grouping the wind power plant output scenes by adopting a K-means grouping algorithm containing distance constraints to obtain a group center scene and corresponding group members, and performing probabilistic load flow calculation through a load flow approximation algorithm retaining nonlinear terms; and finally, performing optimal power flow calculation based on a wait-and-tree model, directly reflecting a wind power plant output scene in an economic dispatching model, and simultaneously satisfying constraint conditions such as a system power flow equation, line power, node voltage and the like by minimizing prediction scene fuel cost and the sum of fuel cost and punishment cost of each scene. The problem that a traditional Copula function is insufficient in flexibility when depicting a complex multi-dimensional correlation structure is effectively solved.
Owner:GUIZHOU POWER GRID CO LTD +2
A wind turbine operation linkage analysis method based on Vine Copula model
The present invention relates to a method for analyzing the operational linkage of wind turbines based on a Vine Copula model. The SCADA data of several wind turbines are obtained respectively, the SCADA data are cleaned and integrated, and an operational linkage analysis data set is generated. The Vine Copula model is used to generate three dependency structures of R-Vine, C-Vine, and D-Vine. The AIC value, BIC value, Loglik value, and Vuong test value of the dependency structure are calculated, and the optimal dependency structure is obtained by comparison. The rank correlation coefficient, upper tail correlation coefficient, and lower tail correlation coefficient between each two wind turbines are calculated. The operational linkage of several wind turbines is determined based on the optimal dependency structure and the rank correlation coefficient, upper tail correlation coefficient, and lower tail correlation coefficient. The method of the present invention introduces the Vine Copula model from the wind farm level to perform operational linkage analysis on multiple wind turbines, and macroscopically analyzes the output of each unit, so as to facilitate the monitoring and diagnosis of a single faulty wind turbine during the operation and maintenance of the wind turbine.
Owner:ZHEJIANG UNIV OF TECH
Large-span cable-supported bridge multi-disaster vulnerability analysis method and system
The invention provides a large-span cable-supported bridge multi-disaster vulnerability analysis method and system, and belongs to the technical field of bridge multi-disaster vulnerability analysis based on a mathematical model. According to the method, a large-span cable-supported bridge multi-disaster intensity-structure response sample is obtained based on nonlinear time-history dynamic analysis, a KDE technology is introduced to capture data distribution characteristics and accurately establish edge cumulative distribution of the data distribution characteristics, C-vine Copula is introduced to describe a complex correlation of the data distribution characteristics, and a multivariate joint probability distribution model is established; and solving the structural damage probability through inverse fitting of the joint probability distribution model. The method can effectively overcome the defect that an existing vulnerability analysis method cannot give consideration to both calculation accuracy and analysis efficiency, and provides a reliable and efficient technical means for multi-disaster damage assessment of the large-span cable-supported bridge.
Owner:BEIJING JIAOTONG UNIV
Multi-modal generation type artificial intelligence watermark embedding and tracing method
PendingCN122020619AImage data processing detailsProgram/content distribution protectionPattern recognitionIntellectual property
The invention discloses a multi-modal generation type artificial intelligence watermark embedding and tracing method. The method comprises the following steps: constructing a third-order nonlinear hybrid power system with fractional order Caputo derivative and time-varying Rikazi matrix closed-loop feedback at the same time; constructing a regular rattan structure which at least comprises R-Vine, C-Vine and D-Vine three-layer nesting and of which the total tree node number is not less than 4096; a finite time chaotic trajectory of a hybrid power system is subjected to spherical radial-vine Copula inverse mapping to generate an antagonistic watermark disturbance field, and meanwhile, the disturbance field is injected into an ultrahigh-dimensional singular value manifold of generative artificial intelligence output content in a nonlinear variational embedding mode. According to the method, the chaotic system and the rattan structure are combined, a watermark disturbance field with strong anti-interference is generated, effective watermark embedding and reliable traceability of the multi-mode AI content are achieved, and intellectual property ownership is guaranteed.
Owner:LINKER
Vegetation drought resistance index construction method and system
The invention discloses a vegetation drought resistance index construction method and system, and the method comprises the steps: obtaining multi-source time series data, unifying the space and time resolution, constructing a rainfall comprehensive index, a runoff comprehensive index, a soil humidity index and an underground water drought index, and employing an R-Vine Copula model for fusion to generate a vertical composite drought index VCDI; identifying a drought event and extracting the duration, severity and strength of the event; performing normalization processing on the kernel normalization difference vegetation index, the surface temperature and the VCDI, constructing a three-dimensional feature space, and defining and calculating a vegetation drought resistance index VDRI through an Euclidean distance; and calculating the vegetation drought recovery time and recovery force by using the VDRI abnormal value and the standardized abnormal value, and identifying the time-lag influence of drought on the vegetation ecological function through lagging cross-correlation analysis. According to the method, the drought process and the comprehensive influence of the drought process on vegetation are accurately described, the research blank in the field of drought-vegetation response comprehensive evaluation is filled up, and a scientific basis is provided for ecological restoration and drought adaptation management.
Owner:WUHAN UNIV
Runoff space dependency modeling and stochastic simulation method based on R-vine copula
The invention discloses a runoff spatial dependency modeling and stochastic simulation method based on R-vine copula, and relates to the technical field of hydrological sequence analysis. According to the method, a spatial relation constraint matrix is adopted to represent all possible vine structures, the marginal distribution linetype of observation data of a hydrometric station is estimated, goodness of fit test is carried out, an R-vine copula model is constructed, an optimal model is determined according to BIC indexes, standard dependency statistics and information gain are calculated, and a mean value fitting sequence and a random simulation sequence are generated. According to the method, the vine structure of the R-vine copula model is specified according to the intersection sequence of the main and branch flows and the spatial position relationship of the hydrometric station, a random model capable of accurately simulating a historical runoff sequence and estimating the future hydrological situation is constructed, the spatial dependency between the upstream and downstream runoff sequences and the main and branch runoff sequences is accurately described, the association state and strength of the main and branch runoff process are determined, and the real-time performance of the main and branch runoff process is improved. And the development trend and the evolution rule of the hydrological situation in the future in the changing environment can be further explored.
Owner:HUAIYIN INSTITUTE OF TECHNOLOGY
Wind power time sequence stochastic simulation method considering high-temperature heat wave event
The invention discloses a wind power time sequence stochastic simulation method considering a high-temperature heat wave event, and the method comprises the following steps: 1), defining a weather state type, and obtaining a historical typical weather state sequence; 2) based on the weather state type, establishing an upper-layer day-by-day weather state sequence; 3) establishing joint distribution for meteorological factors in each weather state in the historical typical weather state sequence by adopting an R-vine copula function, and constructing a meteorological factor joint probability density function; and 4) integrating the upper-layer day-by-day weather state sequence and the meteorological factor joint probability density function to generate a weather scene sequence, and inputting the weather scene sequence into the wind power generation physical model to generate a wind power time sequence. The wind power generated by the method can effectively capture the random change characteristics of weather and the duration of the heat wave event, and accurately reflect the heat wave events of different time scales.
Owner:GUANGXI UNIV
A network traffic anomaly detection method and system
The present invention discloses a network traffic anomaly detection method and system, which belongs to the field of network traffic anomaly detection technology. In view of the context dependency of network traffic and the correlation between different feature space dimensions, an adaptive sliding window mechanism is designed. When there are a large number of anomalies in the data, by maintaining the distribution of some historical normal samples, the missed alarm rate of anomaly detection can be effectively reduced. At the same time, the mechanism can dynamically update the joint distribution of normal network traffic features, thereby being able to fully capture the change pattern of normal network traffic and effectively reduce the false alarm rate of anomaly detection. In this process, considering that the Vine Copula function does not require the network traffic features to conform to a specific distribution type, the prior assumption is eliminated and the actual distribution of network traffic features is closer to the actual distribution. By introducing the VineCopula function to fit the distribution of normal samples in the historical window, the coupling relationship between different network traffic features can be accurately established, and network traffic anomaly detection can be performed efficiently and accurately.
Owner:HUAZHONG UNIV OF SCI & TECH
A method and apparatus for evaluating the multidimensional complementary characteristics of water, wind and light based on improved vine structures
This application relates to a method and apparatus for evaluating the multidimensional complementary characteristics of hydropower, wind power, and solar power based on an improved vine structure. The method includes: obtaining a vine Copula function model by combining mutual information theory with the improved vine Copula structure; constructing a multidimensional evaluation index to measure hydropower regulation capability; defining an output correlation index for the multidimensional complementary hydropower system; obtaining the combined output of hydropower, wind power, and solar power based on the vine Copula function model; calculating the combined fluctuation rate and independent fluctuation rate of different combinations of hydropower, wind power, and solar power; selecting output fluctuation rate, power fluctuation suppression, and output smoothness as evaluation indicators for the overall output stability and volatility of the hydropower system, thus obtaining a multidimensional complementary characteristic evaluation system at the level of different combinations of hydropower, wind power, and solar power, and at the overall system level. This solves the problems that traditional evaluation index systems often only apply to assessing the complementarity between two power generation systems, failing to simultaneously capture the fluctuation trend correlation and slope correlation between adjacent time periods throughout the evaluation period, and neglecting the influence of fluctuation amplitude.
Owner:YALONG RIVER HYDROPOWER DEV CO LTD +1
Probabilistic modeling method of wind pressure coherence for long-span roof structures based on Vine Copula
A Vine Copula-based probabilistic modeling method for wind pressure coherence of large-span roof structures is proposed. A wind pressure coherence function for large-span roof structures considering different wind directions is proposed. The marginal probability distribution of the coherence function parameters is estimated using a generalized extreme value distribution. The probabilistic dependence of the coherence function parameters is quantified using Vine Copula, enabling effective simulation of wind pressure coherence for large-span roof structures considering wind field uncertainty. The specific implementation process is as follows: A. Preprocessing the measured wind pressure data for large-span roof structures to establish a wind pressure dataset; B. Derivation of a wind pressure coherence function model that considers wind direction and fitting the coherence function of the measured wind pressure under different wind directions using a nonlinear least squares method; C. Estimate the marginal probability distribution of the coherence function parameters based on the parameter samples of the fitted coherence function; D. Constructing the probabilistic dependence between the parameters of different coherence functions using Vine Copula; E. Using conditional sampling to generate random parameter samples, these parameters are substituted into the proposed coherence function to obtain the simulated wind pressure coherence function for large-span roof structures.
Owner:ZHEJIANG UNIV