58 results about "Interval estimation" patented technology

The invention discloses a new engineering course teaching evaluation method based on a knowledge-ability-quality triple atlas, and belongs to the technical field of intelligent education and intelligent control. Collecting data of three dimensions of knowledge, ability and quality of students to construct a unified state vector, and describing dynamic evolution of a learning process by combining a state updating and prediction model of a residual network; a multi-cell filtering method is adopted to carry out state set estimation, linear propagation and residual local linearization are combined in the prediction step, and a Lipschitz upper bound is utilized to carry out external connection on a nonlinear residual to ensure the safety and credibility of state interval estimation; in the updating step, set tightening is achieved through prediction strip intersection and generator contraction, coverage rate calibration based on quantiles is introduced, and the confidence level of set estimation is ensured. According to the method, the learning state of the student can be dynamically, stably and interpretably estimated, accurate and efficient course adjustment optimization is realized, and the method has a good application prospect and popularization value.

The invention discloses a dual-stage interval state estimation method for an electric-thermal integrated energy system, and the method comprises the steps: analyzing the uncertainty of wind power output, and constructing a wind power output uncertainty domain ellipsoid convex model; constructing a measurement model of the electric-thermal comprehensive energy system; based on the wind power output uncertain domain ellipsoid convex model, establishing an integration form of a measurement model and a measurement model of the electric-thermal integrated energy system; based on the model in the integration form, constructing an interval estimation model of the electric-thermal integrated energy system; based on an electric-thermal integrated energy system interval estimation model, first-stage interval estimation of an electric power system and a hydraulic network is realized; according to the first-stage interval estimation data, two-stage interval estimation of the thermal network is realized based on an interval linear programming model; and outputting an IEHS state quantity interval value. The method has obvious advantages in solving performance, is high in calculation efficiency and moderate in interval estimation result width, and is more suitable for practical engineering application.

The invention discloses an aviation fuel gear pump confidence degree evaluation method based on the Bayesian theory. The method comprises the following steps: step 1, firstly, determining Weibull distribution as a theoretical basis of reliability modeling according to the life characteristics of the aviation fuel gear pump, and providing probability model support for subsequent analysis; 2, on the basis of Weibull distribution, adopting a Bayesian method to fuse historical data and field test data, and achieving point estimation and interval estimation of shape parameters and scale parameters of the aviation fuel gear pump; and step 3, based on the point estimation and the interval estimation, constructing an MTBF confidence interval and verifying by adopting a Bootstrap method, and finally completing quantitative evaluation of the reliability confidence of the aviation fuel gear pump. According to the method, the specific problems of low evaluation precision, poor confidence interval reliability and insufficient historical data utilization rate of a traditional method under the small sample condition are solved.

The invention relates to a cable tunnel bridge fire prediction method and device, electronic equipment and a storage medium. The cable tunnel bridge fire hazard prediction method comprises the following steps: establishing a cable tunnel bridge fire hazard numerical simulation model, establishing a cable tunnel bridge fire hazard simulation model based on FDS software, obtaining key parameters such as heat release rate, temperature distribution and flame spread boundary under different working conditions, and constructing a multi-time sequence sample data set; performing state discretization on continuous variables such as temperature and a spreading range, and estimating a node prior probability in combination with a statistical frequency; constructing a dynamic Bayesian network topological structure according to a causal relationship among a heat source, temperature and spread, and setting a cross-time slice node dependence path to realize time sequence modeling; inputting a training sample and performing parameter learning to generate a conditional probability table; in the prediction stage, multi-step reasoning is achieved through forward propagation, flame spreading range probability distribution and interval estimation of multiple time steps in the future are obtained, and the specific position where the flame arrives is predicted.

The invention discloses a bridge bearing capacity dynamic evaluation method based on multi-source monitoring data driving, and aims to solve the problem that the bearing evaluation precision is difficult to guarantee under a sparse sensing coverage condition. According to the method, through cross-modal attention alignment guided by event anchor points, mask codes influencing line and propagation time delay constraints and geometric position coding, Green function kernels and time-varying boundary flexibility latent variables are introduced into a physical constraint neural operator model, symplectic integral propulsion is adopted, and block shape-preserving interval estimation and order-preserving calibration weighted according to working conditions are adopted. According to the method, the full-bridge space-time response and the bearing utilization coefficient are calculated, the risk level and the load limiting suggestion are output, and the technical effects of reliability improvement, physical consistency and long-time energy stability evaluation under the complex working conditions and the low signal-to-noise ratio are achieved.

The invention belongs to the technical field of model parameter identification, and discloses an aero-engine system identification and output interval estimation method comprehensively considering performance degradation and model mismatch. For an aero-engine system, a linear variable parameter modeling method is introduced, state variable models of multiple working points of an aero-engine are combined through scheduling parameters, and a large-working-range global model is constructed; aiming at the problems of errors, model mismatch and the like in the modeling process, interval estimation is carried out on the system output uncertainty based on a quantile regression neural network, and the model precision and robustness are improved; a standard state space equation of an aero-engine system is augmented, health parameters of all parts of the system are regarded as states to be estimated, the health parameters are estimated in real time through an adaptive Kalman filtering method based on variational Bayes, and a system adaptive model is constructed. The method provides a research basis for the fields of fault diagnosis, nonlinear system control and the like of an aero-engine system.

The invention relates to a dynamic prediction time function model for surface mining subsidence and a parameter solving method thereof, which are characterized in that on the basis of a Sigmoid function, all expressions of dynamic subsidence rule characteristics of mining surface points can be realized by carrying out related optimization and improvement on the Sigmoid function and improving an interval estimation method based on a main influence radius of a probability integral prediction parameter. According to the method, the ground surface subsidence track feature, the total subsidence time feature, the subsidence speed feature and the acceleration feature can be accurately and dynamically predicted, a time function parameter solving method is simple, parameters used for parameter solving are all related prediction parameters of a common ground surface mining subsidence static prediction model-probability integral method, and therefore the method has the advantages that the method is easy to implement. In an actual operation level, parameters of the time function are easier to obtain.

The invention relates to the field of electric vacuum devices, and provides a method, a device and equipment for evaluating the service life of a space traveling wave tube, and the method comprises the steps: generating an equivalent comprehensive stress profile according to an on-orbit working profile of the space traveling wave tube; under the action of the equivalent comprehensive stress profile, continuously collecting a life characteristic parameter set; performing data preprocessing on the life characteristic parameter set, performing stable degradation interval identification on the obtained purification parameter set, and determining a starting moment of a stable degradation interval; performing filtering smoothing processing on the purification parameter set from the starting moment, inputting obtained smooth degradation data into a preset machine learning model library, determining an optimal degradation function according to an information criterion, and obtaining a life point estimation value based on the optimal degradation function; and obtaining life interval estimation according to the life point estimation value. According to the invention, the problems of long test period and low accuracy of space traveling wave tube life evaluation in the prior art are solved, and rapid and accurate prediction of the long life of the space traveling wave tube in limited test time is realized.

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 provides an aviation bearing health evolution change point online identification and residual life prediction method, and the method comprises the steps: firstly, achieving the online precise identification of the aviation bearing health evolution change point through a Welch's t hypothesis testing method under a sliding window mechanism; secondly, constructing a degradation model based on a multi-stage linear Wiener process, and accurately describing evolution characteristics of the aircraft bearing from a normal stage to a defect stage; a sliding window parameter optimization framework is established, and optimal window parameters are determined by comprehensively evaluating the detection success rate, the average absolute error and the average detection delay; and finally, deriving complete probability distribution of the residual life based on inverse Gaussian distribution, and realizing point estimation and interval estimation of the residual life. According to the method, the health state evolution change point can be timely and accurately identified, the reliability of residual life prediction is improved, effective technical support is provided for safety control and maintenance decision of an aviation system, and the method has important popularization and application values.

The application discloses a natural gas load interval estimation method based on large model coupling working condition clustering, and belongs to the technical field of natural gas pipeline network operation optimization and artificial intelligence load prediction. The method extracts working condition semantic constraints by using a large model, and obtains fuzzy working condition clusters by combining historical operation data clustering; a natural gas load point prediction model is trained for each cluster, and residual probability density distribution is estimated; semantic and numerical weights are fused in real time, and the final point prediction value and the natural gas load prediction interval at the prediction time are obtained by dynamic weighting, which are taken as the natural gas load prediction result and output. The application introduces a large model into the natural gas working condition expression and clustering constraint construction process, and no longer uses the large model as a simple downstream feature generation tool, but solves the problem of interval estimation failure of the natural gas load under fuzzy working conditions such as holiday switching, peak-valley transition and extreme weather through deep coupling of the large model and working condition clustering, so that high-reliability dynamic prediction interval output under complex working conditions is realized.

The invention discloses a construction and control method of a land pollution abatement system, and belongs to the technical field of environmental pollution abatement, and the method comprises the steps: constructing a land pollution abatement model described by a singular partial differential equation; the model is converted into a control system with a movable sprayer as an original point through coordinate transformation; carrying out approximate linearization on a nonlinear term in the system by utilizing a T-S fuzzy method, and designing an interval observer and a controller capable of estimating upper and lower bounds of a system state based on the approximate linearization; the bounded tolerance of the system is analyzed based on the Lyapunov stability theory, the gain of an interval observer and the gain of a controller are obtained by solving a linear matrix inequality, and the gains are applied to the designed observer and the designed controller to complete closed-loop control. According to the method, unknown soil conditions and external interference can be effectively handled, the control robustness is ensured through interval estimation, the limitation that a traditional model only depends on a partial differential equation is avoided, and accurate and stable treatment on polluted land is achieved.

The present invention discloses a method for predicting the number of steel plate defects based on a Gaussian-Poisson hybrid variational autoencoder. Aiming at the fact that the mapping relationship between production process data and the number of steel plate defects is different under different steel plate rolling conditions, this method proposes a Gaussian-Poisson hybrid variational autoencoder as a data-driven soft sensor model. This model uses multiple parallel Gaussian-Poisson variational autoencoders to fit the mapping relationships under different conditions, fuses the parallel outputs with the help of a weight network, and constructs a loss function with the likelihood function values of all data. The proposed method realizes the online prediction of the number of steel plate defects in actual industry through data-driven soft sensor technology and can provide an interval estimate of the number of steel plate defects.

The invention relates to a dynamic truth value discovery model method for non-uniformly distributed data. The method comprises the following steps: acquiring a data source and a data object; defining a truth value discovery problem as an optimization problem at each current timestamp before the timestamp; based on a coordinate descent method, obtaining a solution formula of an estimated truth value; measuring the confidence level of a truth value by adopting an interval estimation method, converting an optimization problem into likelihood estimation by using a maximum posteriori estimation method, and obtaining a confidence source weight by using the variance of a source error; and after the timestamps, for each current timestamp, calculating a data standard deviation, and adopting a truth value evaluation mode of a stability mode and a volatility mode to obtain a truth value of the current timestamp. The problem that an existing dynamic truth value discovery method is prone to being limited by a single model, and huge overhead is generated is solved. The method considers the distribution and occurrence frequency of data points, can more accurately estimate the authenticity of each data, and is superior to a current truth value discovery method in precision and calculation efficiency.

The invention discloses a design method for an interval function observer of a nonlinear interconnection system, relates to the technical field of nonlinear interconnection systems, and aims to solve the problem of interval estimation of a state function of the nonlinear interconnection system. The method comprises the following steps: establishing a nonlinear interconnection system model of which the state varies discretely or continuously along with time; designing an interval function observer for the nonlinear interconnection system model; aiming at the interval function observer, considering nonlinear items and interconnection items existing in the nonlinear interconnection system, and designing a group of constraint conditions as sufficient conditions for existence of the interval function observer based on the stability of the designed interval function observer and the convergence of state function estimation errors of the nonlinear interconnection system; calculating a gain matrix of the interval function observer based on the sufficient condition of the existence of the interval function observer; and based on the gain matrix, completing the design of the nonlinear interconnection system interval function observer.

The invention discloses an uncertainty evaluation method for a quality parameter early warning method in a crude oil distillation process, and the method comprises the steps: firstly, predicting the total uncertainty through variance quantification of interval estimation, and overcoming the defect that an existing method only provides point prediction and cannot evaluate the credibility; secondly, in combination with parameter posterior distribution and model output probability distribution, model uncertainty and random uncertainty are decomposed, and the problem that a traditional method is difficult to distinguish uncertainty sources and cannot take targeted compensation measures is solved; and finally, dynamically dividing the credibility grade of the early warning signal according to the uncertainty proportion, and providing a decision basis for an operator. According to the method, the interpretability and reliability of an early warning result are improved, and a guarantee is provided for safe and stable operation of a refinery enterprise.

A utility consumption information system may generate a variety of data that is based on and / or derived from utility consumption or usage associated with a site. In some examples, the utility consumption information system may generate data to be provided or displayed by applying a load profile to utility consumption or usage data obtained at the site over a period of time. The utility consumption information system may generate the load profile based on sample interval data obtained from one or more devices (e.g., utility metering devices) sharing characteristics with the site at which the utility consumption or usage data was obtained.

The application provides a rotor assembly geometry precision prediction and optimization method considering multi-source uncertainty, comprising: analyzing uncertainty affecting assembly precision in a rotor assembly process; representing the uncertainty as random variables and interval variables respectively through a random model and an interval model; establishing a precision prediction model considering random-interval hybrid uncertainty to predict rotor assembly coaxiality error; solving the uncertainty model based on a Monte Carlo method to obtain probability statistical results of upper and lower bounds and midpoint values of the coaxiality error interval; performing coaxiality out-of-tolerance probability interval estimation, constructing a rotor coaxiality double-layer optimization model under hybrid uncertainty, and optimizing to obtain optimal installation phases of each stage disk. The application applies an uncertainty quantification method to prediction and optimization of rotor assembly geometry precision, can realize accurate prediction and reliable optimization of rotor assembly geometry precision, and meets high-reliability assembly requirements of an aero-engine rotor.

The application discloses a satellite-ground quantum key distribution cumulative security code amount interval estimation method and system for island or coastal ground stations, which comprises the following steps: obtaining target transit weather forecast and multiple sets of historical weather forecast and corresponding measured pairing records, calculating the reference instantaneous security code rate and the reference cumulative security code amount; determining the time weight according to the contribution of each calculation time to the cumulative code amount, and determining the parameter weight according to the cumulative code amount change caused by the representative forecast error scale change of each weather parameter; determining the comprehensive difference of the target forecast and the historical forecast by using the two types of weights, and screening the historical records; applying the forecast error of the selected historical records to the target transit weather forecast as a whole according to the original time and height correspondence, forming multiple sets of modified weather scenarios, and recalculating the cumulative security code amount. The application can improve the task pertinence of historical forecast error correction and the reliability of interval estimation.

The application relates to a suspension bridge flutter response prediction method based on an enhanced Bayesian physical constraint extreme learning machine, and belongs to the technical field of suspension bridge wind resistance design. The method constructs a mixed base function form fusing a Fourier base function and a hyperbolic tangent base function for the established bridge flutter dynamic equation, and builds an enhanced extreme learning machine network. Then, a physical constraint condition dominated by a flutter dynamics control equation is introduced, a special form of a trial solution is constructed to perform hard constraint embedding processing on initial conditions, and a regularization ridge regression method is used to solve the output layer weight of the enhanced extreme learning machine network. Finally, based on a Bayesian inference framework, the posterior distribution of the output layer weight coefficient is estimated, and a bridge flutter response prediction result and an uncertainty interval are obtained. The application can realize high-precision prediction of bridge flutter time domain response, and can give reliable probability interval estimation. The application shows good robustness under the conditions of wind speed fluctuation and noise level change.

The application discloses a deep-cut valley stress field inversion method based on a neural network and a numerical model, and comprises the following steps: deep-cut valley stress field inversion region determination and numerical model construction; stress interval estimation, stress boundary parameter sample generation; batch forward calculation, measurement point stress-boundary stress data set construction; BP neural network construction for inversely deducing stress boundary conditions from geostress measurement point data; actual stress boundary conditions are inversely deduced from the measured geostress of the measurement points, and the final geostress field is calculated by combining the physical information neural network and the numerical model in a forward direction. The application embeds physical constraints such as balance differential equations and compatible equations into the loss function of the physical information neural network, solves the problem that the existing method is biased towards data statistics and the physical mechanism is unclear, realizes the physical rationality of the inversion result, generates parameter samples through orthogonal test design and carries out batch numerical forward calculation, and fully covers the parameter space under the condition that the measurement point data is sparse and the distribution is limited.

The application discloses a flexible joint mechanical arm state estimation method and system based on an ellipsoid beam, relates to the technical field of mechanical arm control, and comprises the following steps: the performance of a Luenberger state observer is quantified by estimating an error, a target function is generated and solved, and a gain matrix of the Luenberger state observer is obtained; an ellipsoid beam propagation law of the estimation error is established based on the gain matrix, a disturbance is expressed as a disturbance ellipsoid beam, and an error ellipsoid beam at the next moment is obtained based on the ellipsoid beam propagation law and the disturbance ellipsoid beam; a state estimation set at the next moment is obtained based on a generated matrix set in the error ellipsoid beam at the next moment; and a certainty upper limit and a lower limit of the state estimation set at the corresponding moment are obtained based on the state estimation set at the next moment, so that an interval estimation result of the system state is obtained. The application realizes a collective disturbance description of the ellipsoid beam, only needs to know the boundary information of the disturbance, avoids the limitation of the Gaussian noise assumption, and provides a certain compact error limit.

The present application provides a sewage treatment biological process fault detection method and system based on interval estimation, belonging to the technical field of automatic monitoring of sewage treatment process. The present application is to solve the problem that the existing sewage treatment detection is strongly dependent on model precision and noise statistical characteristics, and is easy to cause divergence and false alarm and miss alarm when facing channel fading and nonlinear uncertainty. The method comprises the following steps: S1: T-S fuzzy model modeling; S2: robust observer design; S3: interval propagation design, obtaining state interval and residual interval; S4: establishing double fault detection strategy, outputting fault category, fault size and fault occurrence time. The system comprises: modeling module, channel processing module, observer design and operation module, interval propagation module and fault detection decision module.

The invention provides a system reliability estimation and control optimization method based on a fusion distributed likelihood ratio. The method comprises specific processes of fusion distributed multi-component system reliability modeling, parameter interval estimation and reliability index verification. After a motor train unit fusion distribution model is established, the fusion distribution model is solved by utilizing an expectation maximization algorithm to obtain a point estimation value, and on the basis, a confidence limit estimation value of the fusion distribution model is given by constructing a single-parameter likelihood ratio pivot statistical magnitude and utilizing related properties of the single-parameter likelihood ratio pivot statistical magnitude. And applying an interval to estimate a lower limit value, giving a dynamic change of a reliability index of the fusion distributed system under a conservative condition, and finally quantitatively verifying the feasibility of system maintenance period margin extension. The likelihood ratio test method is good in effect when processing large sample data, key information can be efficiently and accurately extracted, and powerful data support is provided for feasibility verification of prolonging the maintenance cycle interval of an equipment system.

The present invention belongs to the technical field of model parameter identification and discloses a method for identifying and estimating the output interval of an aero-engine system that comprehensively considers performance degradation and model mismatch. For an aero-engine system, a linear variable parameter modeling method is introduced to combine the state variable models of multiple operating points of the aero-engine through scheduling parameters to construct a global model with a large operating range. To address issues such as errors and model mismatch in the modeling process, a quantile regression neural network is used to perform interval estimation of the system output uncertainty, thereby improving the model's accuracy and robustness. The standard state-space equation of the aero-engine system is augmented, and the health parameters of each component of the system are treated as states for estimation. A variational Bayesian adaptive Kalman filter method is used to estimate the health parameters in real time, thereby constructing a system adaptive model. This invention provides a research foundation for fields such as fault diagnosis and nonlinear system control of aero-engine systems.

The invention relates to the technical field of fuel cells, and discloses a proton exchange membrane fuel cell long-term prediction method based on a multi-step prediction strategy, comprising the following steps: preprocessing experimental data to obtain a model training data set and long-term prediction initial input data; establishing a model and a training model to obtain a trained hybrid probability data driving voltage decline trend prediction model; performing long-term prediction based on a multi-step prediction strategy; and prediction result and uncertainty analysis: taking the average value of the same prediction position as a point estimation result, taking a standard error range of 95% confidence level as an interval estimation result, analyzing the point estimation result and the interval estimation result through an error evaluation index, and calculating an interval estimation concentration rate to evaluate the prediction uncertainty. The proton exchange membrane fuel cell long-term prediction method based on the multi-step prediction strategy is good in long-term prediction effect and high in prediction result precision and credibility.

The invention provides a UWB positioning method fusing CIR interval constraint and ToA ranging optimization. The UWB positioning method is characterized in that a CIR positioning interval estimation module, a ToA ranging error module and a fusion position prediction module are included; the CIR positioning interval estimation module comprises a CNN (Convolutional Neural Network) model and a Copula model; the ToA ranging error module comprises a GMM model; the positioning method comprises the following steps: a CIR positioning interval estimation module processes CIR data of a positioning target to obtain a first positioning error interval and a second positioning error interval, and a ToA ranging error module processes ToA data of the positioning target to obtain an optimized target function and a weighted cost function; and the fusion position prediction module adopts a PSO algorithm, performs particle optimization in a first positioning error interval according to an optimization objective function, and then performs weighted average operation processing on particles in a second positioning error interval according to a weighted cost function to obtain a positioning prediction value of a positioning target. By adopting the positioning method, the positioning precision and robustness of the positioning target in a complex indoor environment can be greatly improved.

This invention relates to the field of product reliability analysis technology, specifically to a reliability assessment method for a non-fixed shape parameter dependent competing failure model. The method includes the following steps: constructing a dependent competing failure model, designing an adaptive stepwise type II hybrid truncated constant-load life test, estimating parameter points based on test data, establishing an acceleration equation, calculating product reliability under normal stress, and performing interval estimation. This invention constructs a MOBG distribution description that assumes fixed shape parameters and does not consider the dependencies between unknown failure mechanisms and causes. It designs an adaptive stepwise type II hybrid truncated constant-load life test to obtain data, solves for parameters based on maximum likelihood estimation and Bayesian estimation, and combines the acceleration equation to obtain parameter estimates under normal stress. This leads to the calculation of product reliability and interval estimation, improving the accuracy and applicability of reliability assessment. It is suitable for reliability analysis of various products with multiple failure mechanisms and potentially unknown causes.

The invention relates to a modal parameter identification method for a bounded and related uncertainty structure, and belongs to the technical field of modal parameter identification and structure health monitoring. According to the method provided by the invention, the propagation rule of the structural parameter convex set uncertainty is deduced, and a general solving method is provided for parameter identification work under the influence of the convex set uncertainty. According to the method, the correlation between structural uncertainty factors is considered, and compared with an identification method which independently considers the uncertainty of each factor, the method is more scientific and higher in applicability; in addition, a second-order convex set singular value decomposition algorithm and a second-order convex set eigenvalue decomposition algorithm are provided, compared with a first-order method, through second-order Taylor expansion of singular values and eigenvalues, a more accurate singular value second-order interval estimation result and an accurate eigenvalue second-order interval estimation result are obtained, and the method has the advantages of being high in accuracy and high in robustness. And more accurate estimation results of modal parameters such as natural frequency and damping ratio can be obtained.

The present invention discloses a method for event-triggered fault detection of a chemical mixing tank system based on iterative interval estimation. The method first uses linearization technology to establish a discrete dynamic model of the chemical mixing tank system, then uses the system output at the triggering moment to design an iterative observer method, and designs a dynamic output trigger mechanism consisting of the system output and error-dependent trigger parameters to reduce the communication transmission frequency, and then designs a controller that makes the chemical mixing tank system asymptotically stable. Finally, Zonotope technology is used to use data from the normal operation of the system to generate the safety threshold required for fault detection. Compared with the traditional one-step-based estimation method, this method is a new interval estimation method. When applied to a chemical mixing tank system, this method produces a tighter safety threshold interval, which improves the sensitivity and timeliness of fault detection.