536 results about "Normal density" patented technology

The invention is suitable for the technical field of life evaluation, and provides an enhanced high-voltage circuit breaker life evaluation method, which comprises the following steps: constructing a dynamic evolution model of a contact material wear rate; constructing a nozzle degradation dynamic prediction model; constructing residual life probability distribution of the insulating material; according to the dynamic evolution model of the wear rate of the contact material, the dynamic prediction model of nozzle degradation and the residual life probability distribution of the insulating material, constructing a comprehensive life evaluation index; inputting the comprehensive life evaluation index into a preset layered prediction architecture for evaluation; wherein the first layer generates basic life distribution through a Bayesian network, the second layer outputs posterior life distribution correction parameters through a convolutional neural network, and the third layer optimizes posterior distribution through a KL divergence minimization algorithm to obtain a probability density function and a confidence interval of residual electrical life; according to the method, the error of life evaluation can be reduced on the basis of complex working conditions.

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 provides a trajectory design method and system in an unmanned aerial vehicle hidden and sensing integrated network, and the method comprises the steps: firstly deducing a multi-listener cooperative receiving signal expression and a probability density function in the sensing integrated network with multiple cooperative listeners; then deriving a minimum detection error rate expression of multi-listener collaborative detection and an optimal power division ratio expression meeting covert communication constraints based on a maximum likelihood principle; and establishing an unmanned aerial vehicle track and scheduling optimization problem of anti-cooperative detection in the unmanned aerial vehicle hidden and inductive integrated network according to the deduced formula. And finally, iteratively solving the problem based on convex approximation and an ellipsoid method to obtain an optimal unmanned aerial vehicle trajectory and scheduling. According to the method provided by the invention, the covert communication requirement in the unmanned aerial vehicle communication perception integrated network can be well met, and the communication safety in the future unmanned aerial vehicle Internet of Things is 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 the technical field of data processing, in particular to a multi-series short-duration rainstorm data frequency analysis method. The method comprises the following steps: obtaining rainfall original data of multiple series of short duration, and carrying out missing data interpolation and sequence extension processing, duration consistency and rainfall intensity rule check to obtain a rainstorm data set; respectively performing reliability, representativeness and consistency review and sample validity screening on the rainstorm data set; a Pearson III type probability density function model is constructed, probability distribution fitting is carried out, and a frequency distribution curve is generated; and carrying out extra heavy rain value analysis and empirical frequency adaptive line analysis adjustment, generating a corrected frequency parameter, further calculating a design heavy rain value of each design return period, and carrying out duration consistency and spatial rationality check to obtain a final design result output data set. According to the invention, through multi-dimensional data complementation, checking and optimization fitting, the system improves the accuracy, reliability and engineering applicability of short-duration rainstorm frequency analysis.

The invention discloses a new energy joint output scene generation system based on data driving, and the system comprises a data preprocessing module which collects and preprocesses wind power and photovoltaic historical output data; the joint probability distribution model construction module obtains wind power and photovoltaic edge distribution through probability density function fitting, and performs correlation analysis on wind power and photovoltaic through a Kendall rank correlation coefficient and a tail dependency coefficient to obtain a matching error of each connection function; the Euclidean distance between each connection function and the preprocessed wind power and photovoltaic historical output data is calculated, and an optimal connection function is obtained by combining the matching error, so that a wind power and photovoltaic joint probability distribution model is established; the scene generation module uses a Markov chain to form a joint probability distribution model with time correlation, and generates a random scene set through Markov chain Monte Carlo algorithm sampling. The stability of the power system is improved, and the operation cost is reduced.

The invention provides a hidden backscatter communication method based on multicarrier randomized continuous waves. The method comprises the following steps: acquiring multicarrier randomized continuous wave signal characteristic parameters, backscatter signal characteristic parameters and channel parameters between a transceiver and a listener and between the transceiver and each user; according to the characteristic parameters of the multicarrier randomized continuous wave signal and channel parameters between the transceiver and the listener and between the transceiver and each user, determining relative entropy between probability density functions of the listener receiving signal when the user generates a backscattering signal and when the user does not generate the backscattering signal; determining the total reachable rate between the transceiver and all users according to the multicarrier randomized continuous wave signal characteristic parameters, the backscattering signal characteristic parameters and the channel parameters; and maximizing the total reachable rate through a target optimization algorithm by taking the condition that the relative entropy meets the concealment condition as the constraint, and obtaining the optimized statistical characteristic parameters of the multicarrier randomized continuous waves. According to the invention, hidden backscatter communication can be realized.

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 discloses a JSD-based field distribution test method and device in a complex electromagnetic environment, a medium and equipment. The method comprises the following steps: acquiring sample data of an electromagnetic signal in a microwave reverberation chamber; the method comprises the steps of obtaining a preset zero hypothesis and a preset alternative hypothesis; converting discrete sample data into a continuous empirical probability density function through kernel density estimation; calculating the Jensen-Shannon divergence between the empirical probability density function and the probability density function of the assumed distribution as the statistical magnitude of the JSD test; if the JSD test statistic is lower than the critical value, the assumed distribution corresponding to the preset zero hypothesis is not rejected, otherwise, the assumed distribution corresponding to the preset zero hypothesis is rejected, and the alternative hypothesis is accepted. The method can recognize the distribution form of the electromagnetic field in the reverberation chamber, and solves the limitation of a conventional hypothesis testing method when the number of independent samples is small or candidate distribution is similar.

The invention provides an electric power system power supply capacity insufficiency evaluation method considering new energy uncertainty, and the method comprises the steps: building the probability distribution of electric quantity prediction results at different time scales in a to-be-evaluated time period through Gaussian mixture distribution, and obtaining the probability distribution of the electric quantity prediction results in a total time period; constructing a joint probability density function according to the prediction error distribution of the generated power of the plurality of wind power plants and the prediction error distribution of the generated power of the plurality of photovoltaic power stations, and obtaining a space joint probability density function; establishing the probability density distribution of a wind-solar joint prediction error band in the to-be-evaluated time period according to the probability distribution of the electric quantity prediction result in the total time period and the spatial joint probability density function; calculating to obtain load shedding probability distribution through probability density distribution of a day-ahead operation domain and a wind-solar combined prediction error band; therefore, the risk of insufficient power supply is determined. And flexible assessment of the short-term scale power supply capacity insufficiency risk in the power system with short-term uncertainty in the new energy is realized.

Embodiments of the invention disclose a slow node detection method and apparatus, a device, a storage medium and a program product. The method comprises the steps of obtaining respective first duration data of a plurality of processing nodes participating in distributed model training; the first duration data represents the calculation time consumption of the corresponding processing node in the current iteration; based on a parallel strategy trained by the distributed model, grouping the first duration data to obtain a first duration set of multiple groups; the parallel strategy represents an association relationship of the plurality of processing nodes in a forward and backward propagation process and training tasks undertaken by the plurality of processing nodes; and based on the respective probability density function and a first threshold value of the plurality of groups, respectively analyzing and processing the first duration sets of the plurality of groups to obtain slow nodes in the plurality of processing nodes. Thus, the slow nodes in distributed model training can be intelligently and accurately detected, the efficiency and accuracy of slow node detection are improved, and the method has high compatibility.

A nuclear power plant passive system reliability analysis method based on meta-model adaptive sampling comprises the following specific steps: 1, extracting a sample to establish a model, obtaining a response value through an optimal estimation program, and training an initial response surface; 2, extracting a sample, mapping by using a response surface, and selecting a response value as an intermediate event critical value; 3, selecting a design point from the samples within the critical value range as a new sampling center, generating a new sample, supplementing training data, and constructing a new response surface; 4, obtaining a sample obeying important sampling probability density function distribution by using new response surface mapping, obtaining sample points falling in a critical value range, and re-selecting a critical value; and 5, repeating the steps 3 and 4 until the critical value is less than 0, ending hierarchical calculation, and obtaining an estimated value of the failure probability. The method aims at reliability analysis and development of the passive system of the nuclear power plant, the precision of the response surface model is improved through the adaptive sampling algorithm, the calculation cost is low, and the calculation precision is high.

The invention discloses a processing process fault prediction method for essential oil production, and relates to the technical field of essential oil processing, and the method comprises the steps: constructing an environment equipment coupling model, and drawing an electrical equipment insulation resistance attenuation curve through a salt mist deposition experiment; environment disturbance variables are introduced into the generative adversarial network, and an anomaly detector capable of recognizing salt spray corrosion characteristics and humidity condensation characteristics is trained; capturing time-space correlation between temperature mutation and quality fluctuation through a self-attention mechanism; calculating a probability density function of the residual service life of the equipment in real time, and automatically correcting a temperature over-limit threshold value based on the residual service life distribution; calculating the contribution degree of each node to the fault through intervention simulation; and constructing a digital twinborn model containing thermodynamic and hydrodynamic characteristics. Through environment dynamic modeling, causal reasoning and digital twinning methods, the key problems of low fault prediction precision, poor traceability efficiency and lack of quantitative support in decision making in essential oil production are solved.

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.

The invention discloses a method and device for determining the reserve capacity of an electric power system, electronic equipment and a storage medium, and belongs to the technical field of reserve capacity management.The method comprises the steps that empirical mode decomposition is conducted on a predicted net load curve and an actual net load curve, and a high-frequency component, an intermediate-frequency component and a low-frequency component of the predicted net load curve and the actual net load curve are generated; calculating and predicting a high-frequency error sequence, an intermediate-frequency error sequence and a low-frequency error sequence; carrying out probability distribution modeling to generate a high-frequency error probability density function, an intermediate-frequency error probability density function and a low-frequency error probability density function; and determining high-frequency reserve capacity, intermediate-frequency reserve capacity and low-frequency reserve capacity of the power system according to the high-frequency error probability density function, the intermediate-frequency error probability density function and the low-frequency error probability density function based on a preset confidence coefficient. By implementing the invention, the problem that the reserve capacity of the power system is not accurately determined in the prior art can be solved.

The invention belongs to the technical field of reliability analysis, and discloses an electric energy meter adaptive residual life prediction method and system based on random degradation modeling, and the method comprises the steps: obtaining degradation data of an electric energy meter through an accelerated degradation test, removing failure data, and calculating a drift coefficient and a diffusion coefficient through a maximum likelihood estimation method; establishing a multi-source data fusion model of the drift coefficient and the temperature and humidity stress in combination with historical data, and fitting model parameters; expanding the model to a normal-temperature and normal-humidity condition, and calculating a corresponding drift coefficient; and based on the improved random degradation model, deriving a probability density function, an accumulative distribution function and a reliability function, and predicting the residual life of the electric energy meter at normal temperature and normal humidity. The system comprises a temperature and humidity test box, a power source table body and an upper computer, and is used for automatically collecting and analyzing data. Through multi-source data fusion and random degradation modeling, the prediction accuracy is improved, the test cost and time are reduced, and the method is suitable for electric energy meter reliability evaluation under complex working conditions.

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 discloses a time-varying reliability analysis method based on a double-layer BP neural network, and relates to the field of time-varying structure reliability analysis, and the method comprises the steps: generating a sample pool based on a joint probability density function which affects a random design variable of a to-be-analyzed time-varying structure; calculating weights of sample points, screening part of the sample points and establishing a first training data set; an inner-layer BP neural network model is constructed and trained, the minimum value of the time-varying performance function corresponding to each sample point in the first training data set about time is calculated, and a second training set is formed; an outer-layer BP neural network model is constructed, and a second training set is adopted for training; the reliability of a time-varying structure is calculated by adopting a trained outer-layer BP neural network model, the minimum value of a time-varying performance function about time is solved through an inner-layer model, and the outer-layer model is constructed based on parameters of sample points and the minimum value solved by the inner-layer model, is used for calculating the failure probability and has good applicability. The method can be used for time-varying reliability analysis of complex structures.

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 invention discloses an output optimization method, system and device under complementary scheduling and a medium. The method comprises the following steps: acquiring data of cascade hydropower, a wind field and a photoelectric field; a graph neural network is constructed based on the geographic position data, the weight of an edge is configured according to historical power generation data, and nodes of the graph neural network represent fans, photovoltaic arrays and hydropower stations at different geographic positions; carrying out meteorological prediction according to the current short-term meteorological data based on the constructed graph neural network; and constructing an evaluation model based on the double-layer Monte Carlo-conditional risk, outputting a probability density function of output prediction according to the evaluation model, and executing output optimization. According to the method, the propagation condition of possible meteorological elements in the non-uniform observation network is captured by establishing the graph neural network; and comprehensive evaluation of water, wind and light is realized by constructing an evaluation model, and a reference direction of short-term complementary output optimization is given through a probability density function, so that the scheduling effect of complementary optimization is improved.

The invention discloses a gradeability quantitative evaluation method and system considering flexible resource states, and the method comprises the steps: classifying the historical data of flexible resources according to different states, and obtaining sub-data sets in different state intervals; probability density estimation is carried out on the sub-data sets in different state intervals, and a climbing rate probability density function in each state interval is obtained; revising the climbing rate probability density in each state interval based on a preset confidence coefficient to obtain a revised climbing rate set, and solving an expectation of the revised climbing rate set to obtain a climbing rate expectation of the flexible resource in each state interval; and acquiring real-time data of the flexible resource, determining the state interval of the real-time data according to the state of the real-time data, determining the climbing rate expectation in the state interval of the real-time data as the climbing ability of the flexible resource, and determining the final climbing ability in combination with the state constraint condition of the flexible resource. According to the method, the consideration is more comprehensive, and the accuracy of quantitative evaluation of the system gradeability is improved.

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 a construction method of a fiber concrete fiber bridging numerical analysis model, and belongs to the technical field of fiber concrete. The method comprises the following steps: firstly, establishing a nonlinear slippage constitutive relation of a fiber-matrix interface; secondly, adopting a probability density function to describe anisotropic distribution of a three-dimensional fiber direction angle, simulating randomness of fiber embedding length in combination with logarithmic normal distribution, and introducing a correction factor to quantify interaction between fibers; secondly, mapping a debonding behavior of a microscopic interface to a macroscopic fracture surface through a heterogeneous data transfer protocol to realize seamless coupling of a multi-scale mechanical state; meanwhile, the grid density of the crack tip area is dynamically optimized by combining posterior error estimation and an unstructured hexahedral grid local subdivision technology. By the adoption of the method, the limitation of a traditional model in the aspects of interface behavior characterization, fiber distribution idealized assumption and grid rigid division is effectively solved, and the precision and reliability of fiber bridging effect simulation are remarkably improved.

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 provides a steel-concrete composite beam hogging moment region rigidity degradation rule calculation method and a storage medium, and belongs to the technical field of computer-aided calculation. The method comprises the following steps of S100, determining random fatigue loads borne by a hogging moment area of the steel-concrete composite beam, and calculating the amplitude S and the amplitude distribution rule of each fatigue load; s200, performing curve fitting on the amplitude distribution rule of the fatigue load, and determining a probability density function of the amplitude of the fatigue load; s300, determining the fatigue life of the hogging moment area of the steel-concrete composite beam; s400, the static load ultimate bearing bending moment of the hogging moment area of the steel-concrete composite beam is determined; s500, determining a rigidity reduction value of the hogging moment area of the steel-concrete composite beam during fatigue failure; and S600, calculating the rigidity degradation rule of the steel-concrete composite beam under the random fatigue load. According to the method, the rigidity degradation process of the hogging moment area of the steel-concrete composite beam can be calculated and predicted in advance, and a theoretical basis is provided for maintenance and repair of a bridge.

The invention relates to the technical field of three-dimensional modeling, in particular to a few-sample target detection method and system based on a neural radiation field, and the method comprises the steps: collecting a to-be-modeled object, and carrying out the processing of the to-be-modeled object, and constructing a target data set; setting multi-resolution Hash mapping through the three-dimensional coordinates, establishing a Hash table, and obtaining mixed features based on the Hash table; performing neural network reasoning by using MLP to obtain density and RGB color, generating light and delimiting a light range, and determining a sampling mode to construct a probability density function; rendering to obtain a multi-view texture image, and fusing and outputting a three-dimensional model by using Poisson reconstruction; constructing two-dimensional images of different angles, generating a training data set, training the three-dimensional model by adopting the training data set, obtaining a to-be-detected sample, and inputting the to-be-detected sample into the trained three-dimensional model to obtain a detection result; through a cooperation mechanism of explicit three-dimensional reconstruction and implicit feature coding, a three-dimensional model is reconstructed to carry out few-sample target detection, and the view angle constraint of traditional two-dimensional detection is broken through.