96 results about "Density distribution function" patented technology

Disclosed is a microgrid optimal scheduling method taking into consideration a system operation risk and user satisfaction, comprising: generating a power prediction curve of wind power and photovoltaic new energy output in a microgrid, using a Monte Carlo simulation method to obtain a prediction error range to generate a typical scene set, and by means of a data fitting method, acquiring a wind-photovoltaic prediction error probability density distribution function; using value-at-risk and conditional value-at-risk theories to quantify a microgrid operation risk which is increased by slightly high or low output caused by wind power and photovoltaics; obtaining the scheduling cost of an adjustable load in a microgrid system; taking the minimum sum of conditional value-at-risk cost and microgrid system operation cost as an objective function and a user satisfaction level under a demand-side response as a constraint, establishing a microgrid optimal scheduling model taking into consideration the operation risk and the user satisfaction; and using a genetic algorithm to solve the model, and obtaining an optimal result of grid-connected microgrid system optimal scheduling. The safety, economic efficiency, stability and reliability of the microgrid are enhanced.

The invention belongs to the technical field of material structure optimization, and discloses an enhanced structure design method and system for a three-period minimal curved surface, and the method comprises the steps: modeling a geometric model of the three-period minimal curved surface, and exporting the model; expanding a design domain of the geometric model to obtain a corresponding expanded design space of the three-period minimal curved surface; a design space file is converted into an analysis data structure, a general expression mode is constructed for the three-period minimum curved surface, an equal geometric element stiffness matrix is solved by using a Gaussian integral method and the elastic performance of each point in a design domain and assembled into a global stiffness matrix, and the structural response of the model is solved; constructing a local density distribution function in the Bayesian unit, and assembling all LDDFs to obtain a global density distribution function for presenting structural topology; and constructing a stiffness maximization topological optimization mathematical model for the TPMS, calculating an objective function and analyzing sensitivity, and carrying out iterative updating on design variables in the optimization model based on an optimal criterion method to obtain a reinforced structure configuration.

The invention discloses a method and a system suitable for identifying bird echoes by a Chinese S-band dual-polarization weather radar. The method comprises the following steps: preprocessing observation parameter characteristics of Chinese S-band dual-polarization weather radar base data; establishing a membership function and a weight parameter of each parameter of the weather radar to form a fuzzy logic model; and through comprehensive judgment of statistical bird flock membership function aggregation values, accurate distinguishing of bird flock and rainfall data is realized. Based on four observation parameters and three derivations of the dual-polarization radar, probability density distribution function characteristics of bird flock and rainfall data are analyzed, a fuzzy logic algorithm process and weight parameters suitable for S-band dual-polarization radar bird flock recognition in China are constructed, the accuracy of bird flock and rainfall echo distinguishing is improved, and the probability density distribution function characteristics of the bird flock and rainfall data are analyzed. Through individual verification under different weather conditions such as bird separation, rainfall and clear sky, the effectiveness of the method is verified, and a new technical path is provided for future weather radar biological echo recognition and ecological monitoring.

The invention discloses a sea area monitoring method and system based on a satellite remote sensing technology, and relates to the technical field of remote sensing, and the method comprises the steps: firstly, generating multi-stage and multi-band remote sensing raster data through a resource set, and secondly, obtaining joint sample vectors at different times through monitoring external environment data in a target monitoring region, a multivariable kernel density estimation algorithm is adopted to obtain a four-dimensional joint probability density distribution function, and an abnormal coverage area is obtained in combination with multi-stage and multi-band remote sensing raster data; then, based on the abnormal coverage area, identifying an enhanced abnormal unit, and performing dynamic correction operation; and finally, after dynamic correction, obtaining an abnormal connected region, analyzing an abnormal unit with an unstable form, determining an abnormal evolution sensitive region, and realizing a local adaptive optimization means based on the change of the abnormal evolution sensitive region.

The invention discloses a high-precision surface rainfall deviation correction method based on space-time association. The method comprises the steps of evaluating satellite remote sensing rainfall data estimation precision and modeling a space-time association relationship; and space-time association relationship modeling: analyzing space-time error distribution of the long-time-sequence satellite remote sensing rainfall data, performing uncertainty modeling on a satellite rainfall estimation result by using a probability density distribution function, and providing data uncertainty information for rainfall deviation correction. And space-time association relationship modeling: researching a modeling method of the space-time association relationship of the satellite-ground data, and constructing space-time association models between the ground observation sites and the corresponding remote sensing pixels and between the pixels. Meanwhile, environment variables, precipitation deviation time sequence change characteristics and uncertainty information are introduced, and the surface precipitation deviation correction precision is improved. Through the research, the estimation error of the satellite remote sensing surface rainfall data can be effectively reduced, and the accuracy of deviation correction is improved.

The invention relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses an automobile collision dummy abdomen safety test device and method. The testing device comprises a human body feature modeling module which is used for establishing a target population abdomen parameterized model based on statistical analysis, determining a key size design domain covering target population percentile and constructing a target population metrology database; the abdominal mechanical property simulation module is used for obtaining a human tissue relative density distribution function based on the hyperelastic constitutive model, taking the density distribution function as an input parameter for manufacturing a gradient honeycomb structure and simulating human abdominal mechanical properties through relative density gradient distribution in the thickness direction; the multi-mode sensor integration module adopts a distributed intelligent sensing network architecture and is composed of a sensing layer, a transmission layer and a processing layer. By integrating pressure, deformation, acceleration and tissue stress sensors and combining a real-time feedback algorithm, collision parameters are dynamically optimized, and the test precision and the biological fidelity are improved.

The invention discloses a passive target speed estimation algorithm based on a sparse Bayesian learning cross spectrum, and belongs to the technical field of array signal processing. The method comprises the following steps: constructing a target cross-correlation field model, carrying out cross-spectrum on sound pressure and a delayed sound pressure channel to obtain cross-spectrum sound intensity flow, constructing a dictionary matrix according to target cross-spectrum frequency point features, establishing a joint probability density distribution function by virtue of a sparse Bayesian learning model, and finally estimating signal power according to maximum logarithm Bayesian evidence. And carrying out spectrum peak search on the signal power estimation value to realize speed estimation of the target. According to the method, the cross-spectrum sound intensity flow is used as input, all cross-spectrum frequency points are compensated, alignment of cross-spectrum results of different frequency points is achieved, the background noise suppression effect is achieved, and multi-frequency-point processing results are fused. Meanwhile, the method has good speed estimation precision, can maintain good performance under the condition of low signal-to-noise ratio, and has high engineering application value.

The invention discloses a full-life power design method for a steel-concrete composite beam bridge under the action of an automobile load, and relates to the technical field of bridge life evaluation. The method comprises the following steps: establishing a bridge finite element model according to a design drawing, simulating a random traffic flow, performing nonlinear time-history analysis by taking a contact position of a midspan section web and a lower flange plate as a stress attention part, selecting equivalent stress amplitude and a stress cycle index as random vectors, determining a probability density distribution function, selecting an initial point set, and reforming the point set. And determining a bridge fatigue damage performance function, solving to obtain a time-varying probability density function considering uncertainty, calculating reliability, and obtaining a bridge reliability curve graph under the automobile load according to the reliability under continuous time. By establishing a bridge finite element model, bridge nonlinear dynamic time history analysis under the consideration of vehicle action uncertainty is carried out, the structural reliability of a bridge along with the change of the service life under the action of an automobile load can be quickly and accurately calculated, and the service life prediction of the bridge is realized.

The invention relates to the technical field of formation data acquisition, in particular to a logging formation information intelligent analysis system which comprises a response model construction module, a dynamic weight generation module, a composite evidence distribution module and a multi-source information fusion module. According to the method, core calibration data are obtained, a probability density distribution function of basic lithologic logging response is constructed through a kernel density estimation algorithm, dependence on a fixed plate and a static cut-off value is eliminated, dynamic probabilistic description of stratum response characteristics is achieved, and then the initial reliability and sensitivity weight of response numerical values are calculated based on distribution; the method is used for adjusting evidence distribution, then calling an evidence theory combination rule to fuse adjusted multi-source information, determining the stratum state according to the highest cumulative reliability, effectively solving the problem of misjudgment caused by response overlapping, and remarkably improving the accuracy and objectivity of stratum lithology and fluid property identification.

The spatial obstacle identification method based on Gaussian mixture model and depth-color image disclosed by the present invention belongs to the field of aerospace technology. The implementation method of the present invention is as follows: based on the depth image and color image of the spatial environment, a three-dimensional point cloud model of the spatial environment is generated; the noise of the point cloud model is removed by statistical filtering; the three-dimensional coordinates of the point cloud of the spatial environment are clustered and analyzed using the Gaussian mixture model to obtain the Gaussian probability density distribution function of the spatial environment point cloud for determining whether it is a spatial obstacle; an iterative method is used to select and set the spatial obstacle identification threshold; the coordinates of the three-dimensional point in the path space to be determined during the movement of the robotic arm are input into the point cloud Gaussian probability density distribution function to obtain the corresponding Gaussian probability density value; according to the comparison between the Gaussian probability density value corresponding to the three-dimensional point in the path space to be determined and the spatial obstacle identification threshold, the path space point to be determined is identified, and the estimation and division of the spatial obstacle area and the manipulator's operable area are realized.

In order to solve the technical problem of the difficulty in achieving precise and efficient modeling in existing simulations of gamma radiation atmospheric transport under the influence of shock waves, the present invention provides a method for adaptive modeling of gamma radiation atmospheric transport under the influence of shock waves. Specifically, the method comprises the following steps: first, a model is established based on the atmospheric density distribution function and a uniform coarse grid is divided with a given step size; then, the grid is refined and interpolated according to the relative degree of change in atmospheric density; second, nodes with little change in atmospheric density are found in the refined grid and merged; finally, a precise and efficient adaptive grid model of gamma radiation atmospheric transport is obtained, and then a Monte Carlo program is used to carry out computational simulation. The present invention is suitable for adaptive grid division of atmospheric density distribution, and is also suitable for adaptive grid division of time, energy, etc. It is a new method for constructing a Monte Carlo simulation model of gamma radiation atmospheric transport under the influence of shock waves.

The invention discloses a method for simulating sound propagation in a porous medium based on a QSGS-LBM, and the method comprises the steps: building a sound propagation physical model of an anisotropic porous medium based on the QSGS-LBM, and obtaining the density, pressure, speed and temperature distribution of a flow field during sound wave propagation; obtaining a density distribution function evolution equation and a temperature distribution function evolution equation based on the D2Q9 discrete velocity space; setting QSGS method parameters in the flow field area, and generating a porous medium model; obtaining a distribution function of each grid point after collision and migration, and applying boundary constraint conditions to the solid-liquid area based on the distribution function; and obtaining macroscopic variables on the grid points. According to the method, the iterative calculation time for multiple groups of porosity and structure parameters is remarkably shortened, the attenuation law of sound waves, sound pressure pulsation and temperature change caused by energy dissipation can be effectively captured, and the goodness of fit between a simulation result and a theoretical analytical solution is higher.

The present application relates to the technical field of position estimation, in particular to a sea mobile target motion prediction method and device based on limited prior information. The method comprises the following steps: acquiring prior information; constructing a tangent plane coordinate system based on the prior information; calculating a grid side length; determining a unit time step; constructing a voyage transfer probability density distribution function and a heading transfer probability density distribution function; determining a task area, performing grid processing on the task area based on the grid side length, performing discrete point processing on each grid, and acquiring a discrete point one-dimensional vector of each grid; constructing a transfer distance matrix based on a starting grid and a target grid, constructing a transfer angle matrix based on the starting grid, the target grid and a destination point, and acquiring a transfer probability matrix based on the transfer distance matrix and the transfer angle matrix. In this way, the target key area can be quickly searched through observation under limited prior information, thereby improving the target search perception efficiency.

The invention discloses a double-Beta distribution vector pulse width modulation method and system introducing an overlapping factor, and the method comprises the steps: constructing double-Beta symmetrical distribution introducing the overlapping factor, and constructing a probability density distribution function of a double-Beta symmetrical distribution random carrier period strategy; constructing a power spectral density function of the expanded random carrier switching period signal based on the random carrier signal; and carrying out power spectral density analysis on the power spectral density function of the expanded random carrier switching period signal based on the probability density distribution function of the double-Beta symmetric distribution random carrier period strategy to realize random carrier switching period permanent magnet synchronous motor control. According to the invention, local energy accumulation can be weakened, smooth spectrum transition can be realized, and adjustable control of a sideband voltage harmonic peak value can be realized. The overlapping factor-introduced double-Beta distribution vector pulse width modulation method and system can be widely applied to the technical field of pulse width modulation driving.

The invention provides a multiphase flow simulation method and system based on heterogeneous mixing precision acceleration, and relates to the field of multiphase flow simulation, and the method comprises the steps: obtaining to-be-simulated target object information; according to the method, target object information is utilized, a fluid mechanics model is established through a lattice Boltzmann method, continuous fluid macroscopic behaviors are discretized into tiny grids and time steps, and the fluid macroscopic behaviors are simulated through collision and migration of a plurality of particles in a grid fluid area; on the basis of the initial density distribution function, time step iteration is carried out on the fluid mechanics model, parallel fluid macroscopic behavior simulation is carried out on all particles in each time step until the iteration stopping condition is met, and a multiphase flow simulation result is obtained; according to the method, through dynamic threshold identification and multi-level collaborative optimization, a feasible scheme considering efficiency and precision is provided for industrial-grade multiphase flow interface dynamics simulation.

The application provides a turbine blade thermal barrier coating force-thermal coupling reliability evaluation method, which comprises the following steps: measuring and constructing a density distribution function of a thermal barrier coating microstructure and thermodynamic parameters, completing macroscopic complex turbine blade thermal barrier coating temperature field and strain field simulation, constructing a Monte Carlo sample space of random parameters at different positions of the blade, establishing a micro-scale force-thermal coupling finite element model and obtaining a life data set, building and training a deep learning agent model, and completing turbine blade thermal barrier coating reliability evaluation and sensitivity analysis. The reliability evaluation method provided by the application comprehensively considers the influence of the thermal barrier coating microstructure factor and the force-thermal coupling failure mechanism, solves the problems of high dimension, high nonlinear calculation cost and the like in the reliability evaluation of the complex blade thermal barrier coating, improves the accuracy and efficiency of the reliability evaluation, and provides an evaluation means for the safe application and optimized design of the thermal barrier coating.

The present invention provides a method for constructing a refined random fractured cave tunnel numerical model, including establishing a tunnel and stratum model according to the scope of the project, the distribution of the stratum, and the size of the tunnel; generating random numbers using the Monte Carlo method, establishing a discrete fracture network DFN template according to the fracture distribution characteristics and the probability density distribution function, cutting the tunnel and stratum model and converting it into a bonded block model; generating random numbers using the Monte Carlo method, determining the position and radius of the cave according to the cave distribution characteristics and establishing a random ball model, importing the random ball model into the bonded block model for cutting, and generating a random fractured cave model based on the difference set of Boolean operations. In the modeling process, the present invention comprehensively considers the distribution of fractures and caves in the surrounding rocks of the karst tunnel during the construction process, better simulates and analyzes the engineering mechanical behavior and stability in the karst stratum in the tunnel, thereby improving the safety and reliability of tunnel excavation construction.

The disclosure provides a gamma adjustment method, a computer readable storage medium and a computer program product. The gamma adjustment method comprises the following steps: obtaining a model of a to-be-tested module, determining a target model corresponding to the model and an error probability density distribution function according to the model, the target model being used to represent a mapping relationship between a gamma register value and a target influence factor, and the error probability density distribution function being used to represent an error distribution between a predicted gamma register value output by the target model and an actual gamma register value; obtaining a target influence factor value when the to-be-tested module displays a target gray scale picture, and inputting the target influence factor value into the target model to obtain a first gamma register value; detecting whether a display picture of the to-be-tested module meets a requirement when the to-be-tested module performs gamma adjustment by using the first gamma register value; and when the display picture does not meet the requirement, adjusting the first gamma register value by using the error probability density distribution function until the display picture meets the requirement.

The present application relates to the technical field of biomechanical detection and automobile safety evaluation, and discloses a device and method for testing the abdomen safety of a car crash dummy. The testing device comprises a human feature modeling module, a parameterized model of the abdomen of a target population is established based on statistical analysis, a key size design domain covering the target population percentile is determined, and an anthropometry database of the target population is constructed; an abdomen mechanical property simulation module, a human tissue relative density distribution function is obtained based on a super-elastic constitutive model, the density distribution function is taken as an input parameter for manufacturing a gradient honeycomb structure, and the abdomen mechanical properties of the human body are simulated through the relative density gradient distribution in the thickness direction; and a multi-modal sensor integration module, a distributed intelligent sensing network architecture is adopted, and the multi-modal sensor integration module is composed of a perception layer, a transmission layer and a processing layer. Through integration of pressure, deformation, acceleration and tissue stress sensors, and in combination with a real-time feedback algorithm, collision parameters are dynamically optimized, and the testing precision and biological fidelity are improved.

The present invention discloses a pilot sequence design method and device for a non-orthogonal multi-carrier transmission system, the method comprising: initializing the non-orthogonal multi-carrier transmission system; generating a transmission signal for the initialized non-orthogonal multi-carrier transmission system; generating a reception signal taking into account the influence of noise and carrier frequency deviation on the transmission signal when passing through a wireless channel; calculating a joint probability density distribution function for the reception signal, and taking the natural logarithm of the joint probability density distribution function to generate a log-likelihood function; taking partial derivatives of variables in the log-likelihood function to calculate an information matrix; calculating a theoretical performance bound for frequency deviation estimation according to the information matrix, and constructing an objective function for pilot optimization based on the theoretical performance bound; solving the objective function to obtain a pilot sequence for the non-orthogonal multi-carrier transmission system; the pilot sequence proposed by the present invention can achieve better frequency deviation estimation performance than traditional M sequence, Gold sequence, and ZC sequence.

This invention discloses a design and manufacturing method for a uniform cross-section explosive load simulator based on gradient elastic metamaterials, relating to the field of impact loading. Addressing the problems of high cost per use and the susceptibility of variable cross-section elastic rods to local buckling instability under high-speed impact leading to waveform distortion in existing simulators, this invention, while maintaining the macroscopic uniform cross-section shape of the simulator, derives the ideal wave impedance gradient required to achieve the target explosive load based on the one-dimensional elastic wave propagation inverse design theory; establishes the mapping relationship between the relative density of a three-period minimal surface (TPMS) metamaterial unit cell and its equivalent mechanical parameters, and inversely solves the spatial relative density distribution function; extracts the waveform through dynamic impact finite element simulation, and introduces an impedance gradient correction factor to perform nonlinear iterative correction of the density function, obtaining a final three-dimensional metamaterial geometric model that accurately matches the waveform, and uses a flexible polymer material for additive manufacturing. This invention achieves the purely elastic, non-destructive, and reusable simulator, perfectly adapting to the stable firing of light gas cannons, and exhibiting excellent anti-instability capability and waveform fidelity under strong dynamic loads.

The present disclosure provides a multiphase flow simulation method and system based on heterogeneous mixed precision acceleration, which relates to the field of multiphase flow simulation, including: obtaining target object information to be simulated; using the target object information to establish a fluid mechanics model through the lattice Boltzmann method, discretizing the continuous fluid macroscopic behavior into tiny grids and time steps, and simulating the fluid macroscopic behavior by the collision and migration of several particles in the gridded fluid area; based on the initial density distribution function, iterating the fluid mechanics model in time steps, and simulating the fluid macroscopic behavior of all particles in parallel in each time step until the iteration stop condition is met, thereby obtaining a multiphase flow simulation result; the present disclosure provides a feasible solution that takes into account both efficiency and accuracy for industrial-grade multiphase flow interface dynamics simulation through dynamic threshold recognition and multi-level collaborative optimization.

The application discloses a sparse Bayesian learning vector acoustic direction finding method based on orthogonal acoustic intensity flow, and belongs to the technical field of array signal processing. First, an orthogonal acoustic intensity flow data model is constructed, an acoustic intensity flow is obtained by cross spectrum of sound pressure and vibration velocity channels, and acoustic energy flow density is obtained by acoustic pressure autospectrum. Then, a joint probability density distribution function is established by means of a sparse Bayesian learning model. Finally, according to maximum log-Bayes evidence estimation of signal power, spectral peak search is performed on the signal power estimation value, and the direction estimation of the target is realized. The method uses acoustic intensity and acoustic intensity flow as input, has the effect of suppressing isotropic noise and reduces the calculation amount. When the hyperparameters are updated for the first time, a deconvolution algorithm is introduced to avoid the judgment error of the number of signal sources and the direction of arrival caused by the proximity of two targets. Moreover, the method has good direction estimation accuracy and direction resolution capability, and can maintain good performance under the condition of low signal-to-noise ratio.

The invention relates to the technical field of target damage assessment, in particular to a building component explosion damage rapid calculation method based on uncertainty quantification, and the method comprises the steps: obtaining explosive power field parameters according to the proportional distance from the explosion center to the center of a component, the standard deviation of the shock wave overpressure peak value of an explosive and the standard deviation of the specific impulse; obtaining component size and material characteristic parameters according to the standard deviation of the size of the building component and the standard deviation of the material characteristic; obtaining an average damage result of the component according to the explosive power field parameter, the component size and the material characteristic parameter; and obtaining a building component damage probability density distribution function according to the average damage result. The method has the advantages that random factors such as explosive power fields, building structure sizes, material uncertainty and explosion source positions are quantified, building component explosion damage rapid calculation based on uncertainty quantification is achieved, and probability distribution of damage results can be given.

The invention provides a threat assessment method and device for power system assets, equipment and a medium, and the method comprises the steps: collecting target power system asset data, and extracting data features; the data features are calculated through a probability density distribution function based on a multivariate Gaussian mixture model, and the target posterior probability of the assets is obtained; carrying out clustering processing on all target posterior probabilities; and according to a clustering result, identifying a threat state of each asset so as to determine that the asset is in a safe state or faces an attack type. According to the method, asset data of different formats or structures can be processed through the probability density distribution function based on the multivariate Gaussian mixture model, so that the threat state of each asset is identified, and integration and unified analysis of different types of asset data are realized; the problem that information integration is difficult in the prior art is solved, the method can be used for threat identification of multi-source heterogeneous asset data, and the evaluation efficiency is effectively improved.

The application provides a simulation prediction method and system for slow positron source intensity in a research reactor, comprising the following steps: S1, reading arrangement information of components in a reactor core of the research reactor; S2, constructing a Monte Carlo model of the reactor core containing a slow positron source device, and obtaining neutron multi-group angular flux and photon multi-group angular flux information near the slow positron source generating device; S3, constructing a slow positron Monte Carlo model; S4, performing energy-angle double sampling to obtain an incident neutron energy probability density distribution function f n,ig,ia , an angle probability density distribution function f n,ia , an incident photon flux energy probability density distribution function f g,ig,ia , an angle probability density distribution function f g,ia , and a neutron-photon weight factor; S5, performing function sampling of particle types, energy and angle; and S6, obtaining slow positron distribution information generated in the slow positron generating device. The application accurately considers the whole life cycle of slow positron generation and transportation through multi-stage coupling, and realizes accurate prediction of slow positron source intensity.

The application relates to a radio frequency transmit field detection method and device, a radio frequency transmit field correction method, a magnetic resonance system, a storage medium and a computer program product. The method comprises: acquiring at least two groups of magnetic resonance echo signals. Each group of magnetic resonance echo signals is generated by exciting a detection object by a radio frequency transmit pulse with a different preset flip angle, and the different preset flip angles conform to a predetermined relationship. According to each group of magnetic resonance echo signals, a flip angle probability density distribution function corresponding to a radio frequency transmit field is determined. The flip angle probability density distribution function represents the probability density distribution of the flip angle of the radio frequency transmit field in the case that a characteristic quantity of the magnetic resonance echo signal is at a set value. According to the flip angle probability density distribution function and the magnetic resonance echo signal, the distribution of the radio frequency transmit field is determined. By using the method, the actual offset of the radio frequency transmit field is detected to determine the uniformity of the radio frequency transmit field.

The invention provides an X-ray free electron laser pulse transmission simulation method. The method comprises the steps that a three-dimensional light field of an X-ray free electron laser pulse light field generated through simulation is acquired; according to the type of the X-ray free electron laser pulse, different dimension reduction methods are selected, and a probability density distribution function of the X-ray free electron laser pulse in the six-dimensional phase space is determined; and selecting the random scatter points based on the probability density distribution function corresponding to the X-ray free electron laser pulse, and determining a target light source model of the X-ray free electron laser pulse light field in the six-dimensional phase space so as to evaluate the free electron laser pulse in the beam line. Six-dimensional phase space distribution is reduced to low dimension, six-dimensional phase space scatter points are obtained, transmission simulation of free electron laser pulses in a beam line is further carried out, the calculation speed is higher compared with a wave optics method, and rapid evaluation of transmission of XFEL pulses in the beam line is achieved.

This invention relates to a method and system for optimal distributed resource regulation considering multi-factor uncertainties, belonging to the field of distributed resource regulation technology. The method and system include an uncertainty assessment model considering variable correlations. First, considering variable correlations, the conditional probability density distribution function of the error between the predicted and actual values ​​of variables is measured. Then, discrete convolution with complex correlations is used to measure the uncertainty range of the difference or sum of variables. Finally, a robust optimal distributed resource regulation strategy considering multi-factor uncertainties is proposed. Different game strategies are constructed for different distributed heterogeneous components, and individual optima are achieved through strategy exchange and updates between game supply and demand entities. A potential function ensures that the local and overall objective functions are consistent with optimality.

The invention discloses an all-working-condition risk quantification method applied to a new energy civil aircraft power system, which comprehensively considers the influence of discrete factors and continuous factors, quantifies the performance risk of the power system under all working conditions into a probability density distribution function, and comprises the following steps: defining a discrete variable and continuous variable distribution set; establishing a power system performance change degree probability density function PDFcon under the influence of continuous variables; establishing a power system performance probability density function PDFsys under the common influence of the continuous variable and the discrete variable; and step 4, quantifying the system risk, namely quantifying the system risk under the common influence of the discrete variable and the continuous variable into a probability density distribution function for evaluating the risk of the system at the moment. According to the method, quantitative analysis of all-working-condition risks can be realized, and reliable support is provided for airworthiness verification, safety evaluation and engineering application of a new energy power system.