329 results about "Normal density" patented technology

The invention discloses a PINN-based high-precision hydrodynamic numerical simulation method and system, and the method comprises the steps: firstly building a computational domain, setting reasonable geometric parameters and boundary conditions, and constructing a dimensionless Navier-Stokes control equation set; then designing a deep neural network architecture with space-time coordinate input and flow field variable output, and adopting a loss function combining physical constraint and data driving; the core innovation lies in providing a timing sequence sensing RAR-D adaptive sampling strategy, dividing a time domain into a plurality of time frames, performing residual error evaluation in each frame, constructing a probability density function related to residual errors, and balancing priority sampling and overall coverage of a high residual error region; adam and L-BFGS optimizers are adopted to carry out network training, the weight of a loss function is dynamically adjusted, and a sampling point set is periodically updated; and finally, the solution precision is verified through multi-dimensional flow field visualization analysis. Therefore, the prediction precision of the complex flow field is effectively improved, and the calculation efficiency is remarkably improved.

The invention discloses a power system operation reserve quantification method, system and device based on photovoltaic probability prediction and a medium. The method comprises the following steps: calculating an Euclidean distance between a photovoltaic predicted value of a point to be decided and a historical photovoltaic predicted value, and searching a photovoltaic power generation historical data set similar to the point to be decided; performing quantile regression based on the similar historical data set to obtain quantiles corresponding to a plurality of tail end quantile levels, fitting a probability density function of photovoltaic prediction deviation by using a Gaussian mixture model, and further calculating a risk loss expectation of a point to be decided; and constructing a standby cost function, considering the reliability constraint of the standby, taking the sum of the minimum standby reserved cost and the risk loss expected cost as a target function, and finally optimizing to obtain the standby capacity of the power system. According to the method, the change of the reserve price along with the capacity is considered, the photovoltaic probability prediction information can be fully utilized, and a more economical and reliable power system reserve quantification result is obtained.

The invention discloses a real-time quality monitoring method based on a production process parameter dynamic optimization model, relates to the technical field of intelligent quality monitoring, and solves the technical problems of empirical parameter adjustment and insufficient pertinence of exception handling. The method captures time sequence association of process parameters, process variables and quality indexes through a dynamic optimization model, quantifies uncertainty in combination with a probability density function, avoids limitation of a fixed threshold value, realizes early warning accuracy through deviation degree grading, reduces false alarm and missing alarm, screens core influence parameters based on model feature importance, and improves early warning accuracy. Quantitative adjustment suggestions are generated in combination with historical cases and inversion calculation, and empirical operation is replaced; multiple parameters are ranked and adjusted according to influence degrees, coupling interference is avoided, a single-point problem and a linkage problem are distinguished through parameter association chain analysis, a processing flow is formulated in a targeted mode, the single-point problem focuses on local repair and rapid recovery, the linkage problem focuses on cutting off a conduction chain and radically treating the source, and invalid intervention is reduced.

The invention discloses a new energy power grid probability harmonic load flow calculation method based on improved semi-invariant and Gram-Charlier series expansion. The new energy power grid probability harmonic load flow calculation method comprises the following steps: establishing a random variable probability model, a wind-light output model and a wind-light joint distribution model; secondly, considering the output correlation between wind and light fields, converting the wind and light combined distribution model based on a Copula function into independent variables by utilizing Rosenblatt conversion for calculation, then calculating each-order semi-invariant of load, photovoltaic output and wind power output according to the expected values of node voltage and branch power flow and a sensitivity matrix, and finally calculating the output correlation between the wind and light fields according to each order semi-invariant of the load, the photovoltaic output and the wind power output. The method comprises the following steps of: obtaining a state variable, further solving each-order semi-invariant form of the state variable and the branch power flow, and finally, obtaining a probability density function and a probability distribution function of harmonic voltage of each node by combining Gram-Charlier series expansion.

The invention relates to a wind power distribution prediction method and device, electronic equipment and a medium. The method comprises the following steps: constructing multi-dimensional feature data, preprocessing the multi-dimensional feature data to obtain preprocessed multi-dimensional feature data, extracting time sequence features, spatial features and meteorological features, and constructing a multivariate condition feature vector. And further inputting the current multi-dimensional feature data into the trained diffusion generation model, and outputting a wind power probability distribution sample. And carrying out probability density function fitting based on the wind power probability distribution sample to obtain multi-dimensional joint probability distribution of the wind power. According to the method of the embodiment, the complex characteristics of the wind power are effectively captured by constructing the multi-dimensional characteristic data, the prediction accuracy is remarkably improved, the time-space characteristics of the data are fully mined through characteristic extraction, the depiction ability of the model to complex wind conditions is enhanced, and the reliable probability distribution prediction is provided by utilizing the excellent distribution learning ability of the diffusion model.

The invention discloses a power distribution network optimization scheduling method and system based on multi-target deep reinforcement learning, and relates to the technical field of power distribution network optimization operation, and the method comprises the steps: building a power distribution network operation model and a probabilistic load flow model considering the photovoltaic output and load demand uncertainty, completing the probabilistic load flow calculation under the uncertainty, and obtaining a power distribution network optimal scheduling model; analyzing to obtain a probability density function of node voltage and line power flow of the power distribution network; introducing a utility function based on preference, establishing voltage out-of-limit and line overload risk indexes considering severity weight, and constructing a risk-economic collaborative power distribution network multi-objective optimization operation problem; the decision process of the problem is modeled into a multi-target Markov decision process, a decomposition-based multi-target deep reinforcement learning algorithm is adopted, learning and training of the decision process are performed on reinforcement learning agents, a Pareto strategy set is obtained, an optimal operation strategy is screened out, and optimization regulation and control are performed on the power distribution network. And collaborative optimization of the operation risk and cost of the power distribution network system is realized.

The invention belongs to the field of temperature regulation, and particularly relates to a carburant drying equipment temperature regulation method and system to solve the technical problems that an existing regulation mode is insufficient in prediction precision and poor in reliability. Establishing a joint probability distribution model of process disturbance and measurement noise by adopting a Gaussian mixture model; s2, obtaining a probability density function of a state trajectory in a future prediction time domain; s3, setting probability constraints represented by conditional value-at-risk; and S4, taking the first element of the optimal heater power sequence as the heater power control quantity of the current control period, taking the first element of the optimal working mode sequence as the working mode of the current control period, and jointly applying to the equipment. On the premise of ensuring a high safety standard, collaborative optimization of the power and the working mode of the heater is realized, and the energy consumption of equipment is reduced.

In one implementation, a method of encoding or decoding point cloud data is provided, comprising: obtaining a feature map representing attributes of voxels in an octree structure; determining one or more probability distribution parameters for a probability density function associated with an attribute of a current voxel, based on the feature map; determining a probability mass function of the attribute of the current voxel based on the one or more probability distribution parameters for the probability density function for the current voxel; and encoding or decoding attribute information of the current voxel in the octree structure, based on the probability mass function of the attribute for the current voxel.

The invention discloses an assembly deviation interval rapid prediction method based on fuzzy comprehensive evaluation, and belongs to the field of assembly deviation interval prediction in the digital assembly process of aviation complex structural parts. The method comprises the following steps: constructing a simulation model of a target assembly body, executing Monte Carlo analysis, and extracting a manufacturing deviation value and an assembly deviation simulation value of a sampling sample; constructing a manufacturing deviation uncertainty quantitative model based on an assembly success rate function, wherein the assembly success rate function is obtained through probability density function integration and is defined as a fuzzy membership function; establishing an evaluation system containing three indexes of'over-small, moderate and over-large ', and calculating an assembly deviation comprehensive evaluation value; constructing a point cloud mapping model based on the assembly deviation simulation value of the sample and the assembly deviation comprehensive evaluation value, and fitting an assembly deviation prediction interval through a linear boundary function; and the accuracy of the prediction interval is verified through measured data. The method can rapidly and accurately predict the assembly deviation interval, reduces the calculation amount, and reflects the individual difference.

The invention provides an efficient and high-precision prediction method for the minimum failure probability of an aviation structure system based on an exponential penalty learning mechanism, and relates to the technical field of structural reliability analysis. Respectively generating an initial sample and a candidate sample according to the probability density function of the random variable of the performance function to be analyzed; real performance function responses corresponding to the initial samples are calculated to form an initial training sample set, and an initial Kriging agent model is constructed; selecting an optimal sample point from the candidate sample set through the proposed EPAL function; merging the optimal sample and the real response thereof into the initial training sample set, and iteratively updating the Kriging model until an error-based stopping criterion is met; judging whether the failure probability variation coefficient meets the requirement or not; and finally, calculating the failure probability of the structure based on a trained Kriging model and a Monte Carlo method.

The present invention relates to the technical field of turbulence intensity evaluation. Provided are a method and system for evaluating the effective turbulence intensity in a wind farm. The method comprises: acquiring wind speed time series data of a wind farm, and on the basis of the wind speed time series data of the wind farm, obtaining a wind frequency matrix and a turbulence matrix at a target position; acquiring wind-frequency discrete probability density functions for distributions of different wind direction sectors; respectively acquiring environmental turbulence intensities under a normal distribution, a log-normal distribution and a Weibull distribution; performing a chi-square test of independence on an observed frequency and an expected frequency in each turbulence bin interval at each wind speed; on the basis of the results of chi-square tests of independence, using the environmental turbulence intensities under the corresponding distributions to determine turbulence probability density functions; and on the basis of the turbulence probability density functions and the wind-frequency discrete probability density functions, obtaining an effective turbulence intensity. The present invention can improve the evaluation accuracy of effective turbulence intensity.

The invention provides an image generation method of a distributed adaptive generative adversarial network based on hypothesis testing, which introduces parameter hypothesis testing into training of a GAN network, aims to solve the problem of mode collapse of the GAN network in a training process of generating an image, and provides a solution for designing a loss function in a training step. Penalty terms of mean value test and variance test are added to constrain the generator; and determining a rejection domain boundary according to a preset significance degree and a probability density function to design penalty terms of the mean value test and the variance test, and punishing the generator by falling the mean value test statistic and the variance test statistic in the rejection domain. The mean value test is based on z test or t test, and the variance test is based on Levene test. According to the method, hypothesis testing is applied to GAN training to suppress mode collapse, and the difficulty of large-batch requirements is overcome, so that the method keeps the advantages of a distribution matching method while being used in more scenes, and is free of manual design, easy to transplant, small in calculation amount and low in time overhead.

The invention discloses an offshore wind turbine generator stochastic dynamics analysis method capable of processing high-dimensional stochastic factors, which comprises the following steps of: establishing a self-adaptive weight factor-based uniform subdivision technology, and obtaining a high-dimensional stochastic input variable of a structure in a complex environment; deriving a probability density integral equation of the offshore wind turbine generator with high-dimensional random factors, and depicting a structural uncertainty propagation process; solving the probability density integral equation by adopting a direct probability integral method based on a uniform subdivision technology to obtain a probability density function of the structural response of the wind turbine generator; and in combination with equivalent extreme value mapping, a direct probability integral method based on a uniform subdivision technology is further used for solving the fatigue reliability of the wind turbine generator. According to the self-adaptive uniform subdivision technology based on the weight factors, the weight factor solving process and the representative point selecting process are decoupled, the representative points of the high-dimensional probability space can be generated in a self-adaptive mode, the assignment probability of the representative points is calculated based on the weight factors, and the random factors of the structure in the complex environment are obtained.

The invention discloses a probabilistic power supply guarantee capability assessment method considering new energy uncertainty, which comprises the steps of obtaining historical statistical data of new energy in different time periods, constructing a new energy output probability density function based on the historical statistical data, and determining new energy probability density distribution; time sequence production simulation calculation is carried out based on the source network load storage calculation boundary in the target research area, and a conventional unit output curve, a tie line power exchange curve, a pumped storage energy output curve and a demand side response working curve are obtained; based on the conventional unit output curve, the tie line power exchange curve, the pumped storage energy output curve, the demand side response working curve and the load demand curve, constructing a power demand curve which needs to be satisfied by new energy output; and based on the power demand curve and the new energy probability density distribution which need to be satisfied by the new energy output, obtaining the power gap probability and the power shortage expectation of the target time period, and carrying out supply guarantee capability evaluation based on the power gap probability and the power shortage expectation.

The application discloses an underwater unmanned submarine environmental noise sensing method and system, and the method comprises the following steps: A, using an underwater unmanned submarine to preprocess marine environmental noise acoustic data; B, performing marine environmental noise probability density function (PDF) fitting calculation; C, performing PDF calculation of different distribution models; D, calculating the fitting parameters of the plurality of different models, and outputting the fitting results. According to the application, the probability density function (PDF) fitting of the complex marine environmental noise and the fitting of various distribution models can be performed, the distribution type of the marine environmental noise can be judged, and the robustness of the sensing marine environmental noise characteristics can be improved.

The invention discloses a TFT-LCD residual life probability prediction method fusing physical features and Gaussian process regression, and the method comprises the steps: S1, constructing a multi-source feature data set which comprises performance monitoring data, structure response feature data and environment stress data; s2, constructing a Gaussian process regression model with time and environmental stress as input and performance degradation amount as output, and initializing hyper-parameters of the model by using the multi-source feature data set; s3, adopting variational Bayesian inference to realize online updating and uncertainty quantification of model hyper-parameters; s4, based on the updated Gaussian process regression degradation model, obtaining predicted distribution of performance degradation values at future time points; and S5, based on the prediction distribution, generating a probability density function of the residual life and prediction intervals under different confidence levels through Monte Carlo simulation. The method provided by the invention overcomes the defect of insufficient prediction precision of a traditional method under a small sample condition, and can provide individualized life prediction with a confidence interval.

The application discloses a kind of experimental measurement methods of non-spherical particle energy recovery coefficient, it is characterized in that, including the following steps: 1) establish the flat plate model of particle-wall collision working condition;2) establish non-spherical particle model and carry out DEM simulation;3) according to the motion parameter of non-spherical particle after collision, combined with free fall motion, the landing point of particle is calculated, and the random landing point distribution of non-spherical particle after collision with wall is quantified using probability density function;4) design several non-spherical particle models to carry out the above simulation and calculation, obtain the energy recovery coefficient and random landing point distribution of several different non-spherical particles;5) build artificial neural network training model, use Python tool to establish the nonlinear fitting relationship between random landing point distribution and energy recovery coefficient by artificial neural network, construct non-spherical particle energy recovery coefficient prediction model;The application has important significance to the research of particle-wall collision field.

The embodiment of the invention discloses a method and system for predicting the life stage of a vulnerability, a storage medium and electronic equipment, and the method comprises the steps: obtaining the basic information of a to-be-predicted vulnerability; according to the basic information, repairing parameters of the to-be-predicted vulnerabilities are calculated; based on a preset vulnerability life cycle prediction function, calculating a distribution probability value of a to-be-predicted vulnerability according to the basic information and the repair parameters; and determining the life stage of the to-be-predicted vulnerability according to the distribution probability value based on a preset mapping relationship between the life stage of the vulnerability and the distribution probability. According to the method and the system, the life cycle prediction of the vulnerability is associated with the time and the code library of the vulnerability, the probability density function for predicting the life stage of the vulnerability is constructed, and finally the life stage of the vulnerability is determined through the mapping relation between the life stage of the vulnerability and the distribution probability, so that the life stage of the vulnerability can be predicted more accurately, and the reliability of the vulnerability is improved. And thus, engineers can repair the vulnerabilities more pertinently.

The invention belongs to the technical field of electric power meteorology, and provides a medium and long term wind and light resource prediction method and system considering climate remote correlation factors, and the method comprises the steps: obtaining global climate index historical data, and carrying out the standardization processing of the data, and obtaining a standardized climate index; for the historical resource sequence of the target station, calculating the mutual information value of the standardized climate index, and constructing a forecasting factor set by taking the lag time corresponding to the maximum value of the mutual information as the optimal early warning window period; extracting wind and light resource measured data of the same period in historical years to construct a reference probability density function; and taking the reference probability density function as prior distribution, combining a preset dynamic mode prediction result as a likelihood function, and outputting a prediction result containing a deterministic value and a confidence interval. By means of the characteristic that ocean signals change slowly, the method breaks through the 15-day prediction limit of an atmospheric mode, effective trend prediction from the quarterly level to the annual level is successfully achieved, and the requirement for medium and long term resource evaluation is met.

The invention discloses a blast furnace burden porosity calculation method based on a Monte Carlo algorithm, and belongs to the technical field of blast furnace ironmaking. The method comprises the steps that firstly, according to a particle size distribution probability density function, Monte Carlo random sampling is adopted to generate and initialize the particle size of furnace charge particles; then, in a three-dimensional Cartesian coordinate system, a three-ball contact geometric constraint equation set is established and solved, and spatial positions of newly-added particles in an existing accumulation system are searched; meanwhile, the search radius of the supporting particles is dynamically adjusted, and collision detection is executed; and finally, after the total number of the particles reaches a preset threshold value, calculating the total volume and the particle volume in a set spherical calculation domain, and determining the porosity by adopting a moving average and moving variance statistical method. According to the method, the limitation of a traditional experimental method and an existing numerical model is overcome, high-precision and high-efficiency calculation of the blast furnace burden porosity is realized, and a reliable theoretical tool is provided for optimizing a blast furnace burden distribution system and analyzing air permeability.

The application discloses a channel estimation method and device, electronic equipment, storage medium and program, and applies to the field of wireless communication, and the method comprises the steps of adding a window function on the basis of a pilot signal and a received signal of an obtained target channel, constructing a windowed channel estimation model, determining a posterior distribution probability density function of a channel estimation parameter in the windowed channel estimation model, obtaining an auxiliary manifold and a target manifold which are approximate to a nonlinear term in the posterior distribution probability density function, determining an auxiliary manifold parameter, generating a target manifold parameter based on the auxiliary manifold parameter, and generating a channel estimation parameter of the target channel based on the target manifold parameter. The embodiment of the application can reduce the calculation complexity of the channel estimation process, improve the channel estimation efficiency, reduce the signal transmission delay, and enhance the user experience.

This application provides a behavior recognition method, apparatus, electronic device, and storage medium based on exponential fitting, comprising: determining the corresponding power delay spectrum for the channel frequency response of a Wi-Fi signal at each observation time; fitting an exponential probability density function based on the power distribution of each sampling point in the power delay spectrum; performing a Fourier transform on the probability density function to obtain a frequency domain correlation function; determining filter coefficients based on the frequency domain correlation function and a preset frequency domain signal-to-noise ratio; filtering the channel frequency response based on the filter coefficients to obtain a denoised channel frequency response; and performing behavior recognition of passive targets based on the denoised channel frequency response at multiple observation times. This application's solution effectively denoises the channel frequency response of a Wi-Fi signal using exponential fitting, thereby enabling accurate behavior recognition based on the denoised channel frequency response.

The application provides a pulse interference suppression method and related equipment, and relates to the technical field of wireless communication, and the method comprises the following steps: detecting pulse interference points by performing pulse interference detection on a to-be-processed signal sequence based on a Myriad filter; and performing local smoothing repair on the pulse interference points based on an improved local weighted regression algorithm to obtain a smoothed signal sequence; the improved local weighted regression algorithm is an improved robust repair weight diagonal matrix based on a probability density function of a fitted Gaussian distribution; and the probability density function of the fitted Gaussian distribution is determined according to the amplitude of non-pulse interference points in the to-be-processed signal sequence. The application reduces the calculation complexity and improves the noise reduction efficiency through pulse interference detection and local smoothing repair; and the robust repair weight diagonal matrix is introduced based on the LOESS method, so that the noise reduction effect is improved.

The invention relates to a modeling method and system for a rainwater collection system based on gamma distribution and computer equipment. The method comprises the following steps: based on rainfall parameters including rainfall depth, rainfall duration and non-rainfall duration, simulating rainfall characteristics, and constructing a rainfall characteristic probability density function based on gamma distribution; simulating the runoff collected in the rainfall period to obtain the water amount of the runoff intercepted by the roof; simulating the residual water volume to obtain the range of the residual water volume when the rainfall cycle starts; simulating overflow based on the residual water quantity to obtain overflow; the probability density function, the water amount intercepted by the roof and overflow are synthesized, and water-saving and waterlogging-reducing efficiency parameters are constructed. By adopting the method, random rainfall characteristics can be simulated with high precision, and the method has the characteristic of few occupied computing resources.

A method for obtaining the average acquisition probability of a helical scan is presented, solving the problem that existing methods for obtaining acquisition probability cannot accurately measure the satellite's space acquisition capability, and belonging to the field of communications. This invention includes: uniformly generating N target points (x...) in an uncertain region of the helical scan. i y i Based on the spiral scan parameters and the coordinates of the dwell point, the spiral scan is used to calculate the helical scan for each target point (x). i y i The capture probability p(x) i y i , σ v ), for the capture probability p(x) i y i , σ v By fitting the data, the envelope function is obtained. Rotating the envelope function around the y-axis yields the probability distribution function P(x, y, σ). v );σ v Represents the vibration noise of the transmitting satellite; obtains the probability density function P(x, y, σ) of the receiving satellite appearing at (x, y). p ), σ p Standard deviation of satellite position change at the receiver; average acquisition probability

The invention discloses an electronic device and a method applied to the electronic device supporting the narrowband Internet of Things. The method comprises the following steps: selecting a cost function; simulating a first probability density function of the cost function on a target center frequency point, a second probability density function of the cost function on a first adjacent center frequency point of the target center frequency point, and a third probability density function of the cost function on a second adjacent center frequency point of the target center frequency point; and a fourth probability density function of the cost function on a pure noise center frequency point; and calculating the probability that the electronic device successfully detects the target center frequency point of an information frame from a base station according to the first, second, third and fourth probability density functions.

The application provides a small sample constraint SGEMP damage probability evaluation method and system, adopts probability characteristics to evaluate the damage of SGEMP to a satellite electronic system, and obtains the damage probability of SGEMP to the satellite electronic system; is more consistent with the randomness of an actual SGEMP source and a space electromagnetic field, and the accuracy of evaluation is improved. The damage evaluation method firstly obtains the probability density function of sample data through parameter estimation; for sample data of a known probability distribution type, the confidence interval of unknown parameters in the probability density function is obtained by adopting an interval estimation method; for sample data of an unknown probability distribution type, multi-parameter optimization is adopted for estimation to obtain the probability density function; and then damage probability calculation is performed. The method can solve the practical problems of lack of a large amount of data in actual engineering application, and can avoid a large amount of tests to reduce the cost.

The invention proposes a method and a device for arithmetic encoding of a current spectral coefficient using preceding spectral coefficients. Said preceding spectral coefficients are already encoded and both, said preceding and current spectral coefficients, are comprised in one or more quantized spectra resulting from quantizing time-frequency-transform of video, audio or speech signal sample values.Said method comprises processing the preceding spectral coefficients, using the processed preceding spectral coefficients for determining a context class being one of at least two different context classes, using the determined context class and a mapping from the at least two different context classes to at least two different probability density functions for determining the probability density function, and arithmetic encoding the current spectral coefficient based on the determined probability density function wherein processing the preceding spectral coefficients comprises non-uniformly quantizing absolutes of the preceding spectral coefficients for use in determining of the context class.

A method for determining a characteristic bit-flip time of a quantum qubit in a superconducting quantum device, comprises the following operations: a) initializing an idling time and a probability density function (P) of bit-flip time testing parameters (θ) according to a first probability distribution (p), said bit-flip testing time parameters (θ) comprising at least a decreasing lifetime rate (Γz) said probability density function (P) of bit-flip time testing parameters (θ) being a Poisson distribution with the probability of observing a total measurement outcome y = ∑i yi   being given by (see formula (I)), where p0 / 1 is a function comprising a component of the type e-Γzt, said probability density function of bit-flip time testing parameters (θ) being stored on a grid of values for the bit-flip time testing parameters (θ), b) preparing a physical qubit in a chosen state, c) idling for a duration derived from the idling time, d) obtaining a bit-flip measurement (y) by reading the state of the physical qubit, e) updating the probability distribution function of bit-flip time testing parameters (p(θ)) using the measurement (y) of operation d), the idling time of operation c), and with the formula p(θ) = p(θ)p(y|θ,t) / p(y,t), f) calculating an estimated information gain for each possible measurement time using the updated probability distribution function of bit-flip time testing parameters (p(θ)) of operation e), from the information gain equal to the difference between the Shannon entropy of the probability distribution function of the decreasing lifetime rate (p(Γz)) and the Shannon entropy of the conditional probability distribution function of the decreasing lifetime rate knowing the measurement of operation d) and the idling time (p(Γz|(y,t)), or an estimated information flow with the information flow which is a function of the information gain and the idling time, g) defining a new idling time as the value of time which maximizes the estimated information gain or the estimated information flow of operation f), h) Returning the bit-flip testing time parameters (θ) if p(θ) satisfies a return condition derived from the evolution of the decreasing lifetime rate (Γz), and / or comparison with a return threshold, and else repeating steps b) to g) with the idling time of operation g) and the probability distribution function (p) of bit-flip time testing parameters (θ) of operation e).

The invention discloses a control rod falling reliability analysis method based on random point collocation perturbation, and belongs to the field of reliability analysis. The method comprises the following steps: firstly, based on a control rod falling structure, constructing an uncertainty rod falling dynamic model, and determining random variables, random variable vectors and distribution thereof in a rod falling process; secondly, under the condition of considering a random variable, calculating a statistical characteristic value of rod falling time by adopting a random point collocation perturbation method based on an uncertainty rod falling dynamic model; thirdly, obtaining a probability density function of rod falling time based on a maximum entropy principle through statistical feature values; and finally, based on a reliability theory, calculating a control rod falling time reliability index. Reliability analysis is carried out based on the probability density function of the control rod falling time obtained through solving, and the calculation efficiency can be remarkably improved; the blank of control rod falling reliability analysis and research when random variables exist is filled; compared with a reliability analysis method, the efficiency of calculating the reliability index is higher.