16 results about "Stochastic distribution" patented technology

Disclosed in the present invention are a railway vehicle-track-bridge system dynamic response prediction method and system, and a medium. The method comprises: inputting vehicle speed samples and track irregularity samples into a vehicle-track-bridge system coupled random distribution physical model to obtain corresponding bridge dynamic responses and effective loads of the vehicle-track-bridge system, and extracting a global stiffness matrix of the vehicle-track-bridge system; constructing a training sample set; constructing a fitness function considering the effective loads, and then on the basis of the training sample set, using a genetic algorithm to optimize parameters of a BP neural network prediction model and training same to obtain a bridge dynamic response prediction model; and using the bridge dynamic response prediction model to carry out bridge dynamic response prediction. By introducing effective loads in a vehicle-track-bridge system into a fitness function in a genetic algorithm, a neural network model and a vehicle-track-bridge physical model are organically combined, thereby improving the prediction precision.

The invention discloses a concrete mesoscopic modeling method based on the calculus principle, and relates to the field of concrete modelling, and the technical scheme comprises the following steps: S1, initializing geometric model parameters; s2, generating aggregate information; s3, interference condition judgment: carrying out interference condition judgment on the generated aggregate; s4, boundary condition judgment: checking whether the aggregate is completely located in the putting domain or not; s5, storing and updating aggregate information; and S6, judging whether the current aggregate total volume reaches a preset volume or not. The method has the beneficial effects that the particle size and position information of the aggregate is randomly generated by introducing the Monte Carlo principle, and random distribution of the aggregate in a putting domain can be realized; the optimal Fuller grading curve proposed on the basis of the calculus principle can greatly reduce the time for generating the model and improve the efficiency of model generation.

The invention provides a shape function KL expansion random field discretization method considering a slope soil body irregular domain, and belongs to the technical field of soil body parameter random field analysis. Comprising the following steps: establishing a slope numerical model and dividing a discrete grid and a form function grid; establishing a probability distribution model of a soil parameter random field; calculating an element stiffness submatrix ke and an element mass matrix Ee; calculating an autocorrelation matrix rho and a global stiffness matrix B; assembling a global matrix; calculating a characteristic value lambda k, a characteristic function phi k and an expansion term number M; and random field discretization of the slope numerical model is realized. The random field discrete technology provided by the invention is used for efficiently and accurately analyzing the random distribution characteristics of the soil parameters in the rock-soil structure. The discrete random field model can be efficiently constructed in a complex geological structure, the calculation cost is remarkably reduced, the method can be conveniently combined with numerical simulation tools such as a finite element method, and the requirement of actual engineering for efficiency is met while the precision is improved.

The invention is suitable for the technical field of random clearing, and provides a power transaction method and system based on a random clearing mode, and the method comprises the steps: obtaining historical power supply and demand data, carbon emission data and supply and demand data monitored in real time of a preset region, and carrying out the power supply and demand analysis of the obtained data; determining a random distribution model of the power price of the preset region based on the power supply-demand analysis result; obtaining power related data of power generation enterprises and power utilization enterprises in a preset region, and determining a range distribution model of power distribution according to the obtained data; determining a random clearing model according to the random distribution model of the power price and the range distribution model of the power distribution, and carrying out clearing calculation and optimization based on the random clearing model; the transaction result is determined according to the clearing calculation and optimization, and the electric charge settlement is performed according to the bid-winning electricity price and the actual electricity consumption in the transaction result, so that the accuracy of the electric charge settlement based on the random clearing condition is effectively improved.

The general aspects of the present disclosure relate to a method for determining a surrogate model of a state space model. The method includes receiving a time-continuous state space model describing a system to be closed-loop controlled. The method further includes forming a time-discrete representation of the state space model describing the system to be closed-loop controlled, wherein at least the time-discrete representation of the state space model has a time dependence based on a stochastic distribution. The method further includes determining a surrogate model of the time-discrete representation of the state space model.

The invention discloses a non-Gaussian random process prediction optimization control method based on a Wasserstein fuzzy set, and belongs to the technical field of blast furnace ironmaking system control. Aiming at the problems of modeling uncertainty and insufficient robustness under external disturbance and non-Gaussian disturbance existing in a traditional method, the method comprises the following steps: setting a coal injection amount, an oxygen-enriched flow, a cold air flow, a molten iron temperature and a Si content by collecting a pressure difference of a blast furnace ironmaking system; a blast furnace iron-making system is used for building a blast furnace neural network prediction model, output in a period of time in the future is predicted based on the current blast furnace state and the neural network model, a predicted output error is regarded as a random variable, and a Wasserstein fuzzy set with empirical distribution as the center is built to describe distribution uncertainty of the Wasserstein fuzzy set; in an MPC rolling optimization process, Wasserstein distance constraint is introduced, a distribution robust optimization problem is converted into a solvable optimization problem, and a group of optimal control sequences are obtained, so that a system does not depend on a specific random distribution hypothesis, and modeling errors and non-Gaussian external disturbance can be effectively processed.

The present invention discloses a non-fragile fault-tolerant load frequency control method for a multi-area power system. To address the issue that faults in a multi-area power system can affect actual production and life, we introduce an important control method in the power system, namely load frequency control, and make corresponding improvements. First, based on the distribution characteristics of minor faults and severe faults, a stochastic distribution model of local faults is established. Then, considering the specific situation of controller gain perturbation, we take into account both multiplicative perturbation and additive perturbation in controller design. Next, considering different transmission delays, the multi-area power system is modeled as a multi-delay system, and a new non-fragile H ∞ A fault-tolerant load frequency control scheme is proposed, and the problem of handling coupling terms is transformed into the W problem in the fault-tolerant load frequency controller. Finally, the feasibility of the method is verified through numerical examples.

The present application relates to the technical field of SCR denitration system, and particularly relates to a kind of ammonia injection control method of SCR denitration system based on random distribution control algorithm.The method comprises the following steps: S100, real-time acquisition of SCR denitration system outlet NOx multi-point sampling data and SCR denitration system ammonia injection grid each partition ammonia injection amount;S200, according to the NOx multi-point sampling data and each partition ammonia injection amount obtained in step S100, estimate the probability distribution of SCR denitration system outlet NOx concentration;S300, the known SCR denitration system outlet NOx concentration probability density function is represented;S400, the next moment of SCR denitration system outlet NOx concentration probability density function corresponding is predicted;S500, the predicted output information is used to combine the feedback correction of SCR denitration system outlet NOx concentration probability distribution;S600, according to the concentration correction, the ammonia injection output control of SCR denitration system ammonia injection grid is adjusted in real time, and the random distribution control amount output of SCR denitration system is calculated.

This invention provides a method for calculating carbon emissions in a distributed generation (DG) stochastic output distribution network. The method includes: acquiring raw data of the DG stochastic output distribution network; inputting the raw data into a preset node admittance matrix to determine the distribution probability of parameters at each node in the distribution network, and performing deterministic power flow calculation based on the determined distribution probability to obtain the randomly distributed generator carbon emission intensity; calculating the corresponding judgment coefficient based on the obtained randomly distributed generator carbon emission intensity; determining whether convergence is required based on the judgment coefficient; if convergence is required, redetermining the distribution probability of each node parameter and re-performing the deterministic power flow calculation until convergence is no longer required; if convergence is not required, outputting the obtained randomly distributed generator carbon emission intensity and power flow results as the final calculation result. This invention lays a solid foundation for the comprehensive analysis and evaluation of carbon emissions under various operating conditions and meets the requirements for carbon flow calculation in distribution networks containing DG.

The present invention discloses a quality-related process monitoring method for a nonlinear mixed random distribution system, and relates to the technical field of fault detection. The method comprises the following steps: sequentially performing feature space mapping and zero-mean normalization processing on a Gaussian variable matrix and a non-Gaussian variable matrix of a device to be detected to obtain a Gaussian variable kernel matrix and a non-Gaussian variable kernel matrix of the device to be detected; calculating quality-related statistics of a Gaussian part and characteristic statistics of a non-Gaussian part of the device according to the Gaussian variable kernel matrix and the non-Gaussian variable kernel matrix; inputting the quality-related statistics of the Gaussian part and the characteristic statistics of the non-Gaussian part of the device to be detected into a trained RVM model to obtain the probability of a quality-related fault occurring in the device to be detected; and judging whether a quality-related fault occurs in the device to be detected according to the probability of the quality-related fault occurring in the device to be detected. The present invention improves the detection rate of quality-related faults and reduces the false alarm rate of fault detection.

The application relates to the field of PWM modulation technology and discloses a method for optimizing double-random PWM c The double-random PWM is obtained by randomizing the carrier frequency f c and the pulse position, wherein the random distribution of the carrier frequency and the pulse position is respectively parameterized by using a Sigmoid function, the genetic algorithm is improved by using a particle swarm algorithm, the minimum value fmin of the random number Sigmoid distribution of the carrier frequency or the pulse position random number, the maximum value fmax of the carrier frequency or the pulse position random number and a curve transverse adjustment parameter sigma in the double-random PWM are optimized by using the improved genetic particle swarm algorithm; the method disperses harmonic energy to a wider range from the frequency and phase dimensions by the double-random PWM, reduces the harmonic amplitude at the switching frequency and the integer multiples thereof, the random number adopts the Sigmoid distribution, the nonlinear probability density characteristics of the Sigmoid distribution that more samples exist on the two sides and fewer samples exist in the middle are used, the carrier frequency and the pulse position are more likely to avoid the sensitive area, and the EMI peak caused by the extreme parameters in the traditional uniform distribution is reduced.

This invention relates to a method and system for simulating the spatial variability of concrete based on stochastic discrete element method (DEM). The method includes: modeling the concrete material using the DEM based on the Monte Carlo approach, assigning a corresponding particle contact model according to the material's mechanical characteristics; using a random seed number to characterize the random distribution of aggregates; simulating the spatial distribution characteristics of strength parameters between particles using Monte Carlo simulation based on the random aggregates; and introducing a random field into the spatial variation of strength parameters, changing the internal strength spatial distribution of the material through different random parameters. Compared with existing technologies, the numerical simulation method of this invention, based on the DEM and employing a coupling of direct and indirect methods, fully considers the random distribution of aggregates and the spatial variation of strength parameters in concrete materials, and can better reproduce the mechanical variability characteristics of concrete materials.

The invention relates to a ship berthing method and device, electronic equipment and a computer storage medium, and belongs to the technical field of maritime affairs, and the ship berthing method comprises the steps: obtaining multi-physics field data of a ship berthing area, constructing an instantaneous random distribution model of each physics field data, and determining the uncertainty of the multi-physics field data; adopting a coupling six-degree-of-freedom frequency-time domain hybrid solver to solve the joint probability response of the motion state of the ship body when the uncertainty is synchronously propagated in the ship body kinetic equation, the mooring elastic-plastic equation and the propeller kinetic equation; determining the berthing failure probability that the posture of the ship body exceeds a preset posture safety threshold value and the tension of the cable exceeds a preset tension safety threshold value, performing joint constraint optimization on the berthing failure probability and a preset berthing constraint condition, and determining a dynamic safety probability boundary of ship berthing within a preset time period; and determining a berthing scheme of the ship in a preset time period based on the dynamic safety probability boundary. The ship berthing scheme can be efficiently and automatically proposed.

The invention relates to a maximum reliability control method for a discrete linear non-Gaussian stochastic system, which belongs to the field of stochastic distribution control and comprises the following steps of: firstly, establishing a discrete linear non-Gaussian stochastic system model and clarifying a maximum reliability control problem with a state feedback form; secondly, deriving a one-step probability density function prediction equation of the closed-loop control system; then, establishing a one-step reliability prediction model of the closed-loop control system; thirdly, constructing a maximum reliability control optimization problem through Taylor approximation and a recursive algorithm; and finally, solving an optimization problem to obtain a vectorized state feedback gain increment, and obtaining a state feedback gain required to be designed through matrix reconstruction. According to the method, the one-step reliability of the discrete linear non-Gaussian random system is maximized, and a new index design thought is provided for improving the control performance of a general non-Gaussian random system through a simple principle and a simple calculation process.

The present invention discloses a method and system for sparse estimation of complex ship HRRP matching K - distribution clutter characteristics. The present invention uses the K - distribution to constrain the probability characteristics of sea clutter, uses a single - parameter random distribution to constrain the sparse characteristics of complex ship HRRP, and uses the A - D test to determine the sparse parameters of complex ship HRRP q , which solves the problem of clutter model mismatch when the existing sparse optimization methods are applied to the estimation of complex ship HRRP in the background of non - Gaussian sea clutter, and significantly reduces the performance loss of complex ship HRRP estimation caused by clutter model mismatch.

The invention discloses a noise enhancement nonlinear system joint detection and estimation method under a Neyman-Pearson framework, and belongs to the field of signal processing. Firstly, independent additive noise is added to a nonlinear system input signal, and noise-corrected nonlinear system output is obtained after the nonlinear system input signal passes through the nonlinear system. And secondly, under the Neyman-Pearson criterion, performing binary hypothesis judgment based on the output, and estimating unknown parameters in the signal of which the judgment result is hypothesis H1. And under the condition of ensuring that the detection probability and the false alarm probability meet certain constraints, constructing a noise enhancement nonlinear system joint detection and estimation model which minimizes the conditional estimation risk. The additive noise is the optimal solution of the model and is random distribution formed by convex combination of not more than three constant vectors with a certain weight. According to the method, noise enhancement and nonlinear system joint detection and estimation under the Neyman-Pearson criterion are combined, and the estimation performance is further improved under the condition that the detection performance is not reduced.