18 results about "Standard uncertainty" patented technology

The application discloses a link time delay calibration result uncertainty modeling evaluation method and device and a medium, and relates to the technical field of satellite navigation high-precision timing. The method comprises the following steps: selecting a time laboratory station and specifying a reference station, constructing a time transfer link, solving the receiver clock error based on an ionosphere-free combination model, and inversely deducing the hardware time delay of a station to be calibrated through clock error difference; the distribution is discriminated through time delay residual statistical characteristics, a model is matched to calculate a type B uncertainty, and an empirical type B uncertainty is obtained through weighted summation; a standard deviation of a calibration time sequence is calculated to obtain a type A standard uncertainty estimation value; a synthetic expanded uncertainty is calculated by fusing the empirical type B uncertainty and the type A standard uncertainty estimation value, the calibration result reliability is evaluated, and uncertainty quantitative analysis of time delay calibration is realized. The application provides a clear and reliable evaluation framework for link calibration error, and significantly improves the reliability and interpretability of the calibration result.

The invention provides a reflection wave band spectrum domain influence uncertainty quantification method and device for reference star radiation reference transmission, and relates to the technical field of data processing, and the method comprises the steps: inputting ground features, atmosphere and observation geometric parameters into a radiation transmission model, and simulating to generate high-resolution atmosphere top spectrum radiance data as a real radiation spectrum; carrying out convolution on the data and a reference instrument spectral response function to obtain a reference instrument measurement spectrum; the reference instrument measurement spectrum and the real radiation spectrum are respectively convolved with the target instrument spectral response function, the relative difference percentage is calculated, the root mean square is taken, and a first standard uncertainty is obtained; positive and negative deviation is applied to the central wavelength of the reference instrument, and a wavelength sensitivity coefficient is obtained through calculation and normalization; the variance is synthesized into related parameters to obtain the total standard uncertainty, then the coverage factors are fused, and the expansion uncertainty is output. The accuracy of the quantification result is improved.

The application discloses a kind of intelligent verification and deviation correction method and system of power selling day clearing result.The method collects power selling day clearing data, which is decomposed into metering, price, loss and settlement rules and other business components and evaluated uncertainty, according to tail thickness, divided into light tail type or near Gaussian type component and heavy tail type component;Through first-order Taylor expansion to obtain sensitivity coefficient, saddle point approximation second-order semi-analytic expansion is used for heavy tail component to obtain second-order cumulant curvature;Spearman dependence matrix is constructed, and positive definite correlation matrix is generated by ridge parameter disturbance and normalization, and combined with standard deviation to form covariance matrix, calculate the combined standard uncertainty and the corrected component degree of freedom, generate asymmetric verification threshold;When day clearing result is over threshold, based on contribution degree vector, deviation is counter-distributed, and kalman filter with residual auto-regression is used to update component, and output correction result.

The invention relates to the field of electric energy metering, and particularly provides an uncertainty evaluation method, device and equipment for a measurement result of electric power measurement equipment. The method comprises the steps of collecting a voltage data flow in a current sampling period, and calculating a current power grid parameter based on the voltage data flow; wherein the power grid parameters comprise a voltage total harmonic distortion rate, a voltage fluctuation rate, a frequency offset and a voltage three-phase unbalance degree; determining a current power grid state grade according to the current power grid parameters; when the current power grid state is in a stable state, selecting a target uncertainty calculation model and a target inclusion factor calculation model corresponding to the power grid state grade, and calculating the current synthesis standard uncertainty and the current inclusion factor; based on the current synthetic standard uncertainty and the current inclusion factor, a current extended uncertainty is calculated to evaluate a measurement result of the power measurement device according to the extended uncertainty. According to the technical scheme provided by the invention, the accuracy of uncertainty evaluation can be improved.

The invention discloses a robot measurement system calibration and uncertainty evaluation method. Firstly, a robot motion error model is established, and an error transmission link is defined; after a theoretical framework is established, N groups of end effector pose data are measured, geometric parameter errors are calculated, and meanwhile, non-geometric parameter errors caused by an approximate external load are experienced. Secondly, estimating discrete probability distribution of related parameters, and obtaining a continuous probability density function and features through approximate fitting; and finally, setting a confidence probability, substituting the confidence probability into the error model to carry out large-scale pseudo-random simulation, obtaining error probability distribution of the end effector, and completing evaluation by combining a measurement mean value and standard uncertainty. According to the method, an uncertainty core source is locked, a parameter influence path is defined, finite sample information is fully utilized, the problems of distribution representation deviation and distortion are avoided, and a precise theoretical basis and reliable data support are provided for uncertainty evaluation of a high-dimensional parameter system.

The invention discloses a steam turbine steady state working condition automatic identification method and an application system, and belongs to the thermal generator set performance test and operation data analysis technology field, and the method comprises the following steps: calculating the fluctuation degree of preprocessing data through a sliding time window, and comparing the fluctuation degree with a corresponding preset fluctuation threshold value; determining the minimum effective number of readings, and taking the preprocessed data in the current sliding time window as a steady-state working condition candidate data segment; obtaining the standard uncertainty of the preprocessed data through root mean square synthesis; a confidence level is preset, a confidence interval is constructed for the mean value of the preprocessed data in the steady-state working condition candidate data segment according to the standard uncertainty, and when the requirement of preset performance analysis for data accuracy is met, an effective steady-state working condition data segment is obtained, and steady-state working condition recognition of the steam turbine is completed. According to the method, the technical problem that the reliability of the steady-state data is difficult to evaluate due to the lack of uncertainty measurement in the prior art can be solved.

The application discloses a kind of micro-force intelligent calibration and high-precision solving method and system based on data driving, utilize high-precision standard force source, to a micro-force measuring device is loaded experiment, constructs calibration dataset;With capacitance value change as input feature, with standard force value as output label, training set is used to train GPR model, based on the comprehensive performance evaluation index on verification set selects GPR model as final model;Based on the performance evaluation of final model to test set of whole course isolation storage, obtain the unbiased estimate of generalization performance;Final model is deployed in actual measurement, for the micro-force to be measured, collect the capacitance value change caused thereby, input model, and the force value measurement result is obtained by solving, the combined standard uncertainty of measurement result is synchronously output;Real-time monitoring own output performance, provide decision basis for predictive maintenance and model updating.The application can carry out high-precision, high-reliability measurement to millinewton to micro-newton order of magnitude micro force.

The invention discloses a gas meter digital measurement uncertainty evaluation method based on deep learning, and relates to the field of metering, and the method comprises the steps: collecting verification data of a gas meter, carrying out the preprocessing of the verification data to obtain a known data set, and dividing the known data set into a training set and a verification set; training the deep learning model by using the training set and verifying by using the verification set; calculating the mean value and the standard uncertainty of the input quantity based on the known data set; in combination with the Monte Carlo method and the prediction uncertainty probability distribution of the model instance, calculating the probability distribution of the measurement through random sampling and probability propagation; based on the measured probability distribution, the measured mean value and the standard uncertainty are calculated, and evaluation of the digital measurement uncertainty of the gas meter is completed. The evaluation process is full-automatic, and subjective errors caused by manual introduction are avoided; and the requirements of measurement development planning are met, digital measurement construction is promoted, and verification credibility is improved.

A system and method for quality-controlled three-dimensional volumetric measurement on mobile or head-worn devices. During acquisition, the device evaluates quantitative scan-quality metrics in real time and provides corrective guidance to address deficient regions. An on-device acceptance gate prevents export until thresholds for coverage, alignment, motion stability, and volumetric uncertainty are satisfied. Following validated capture, the system generates a watertight surface model, computes volume with quantified standard uncertainty, and records the quality context supporting acceptance. Follow-up scans are registered to a baseline so that longitudinal changes are judged against propagated uncertainty, enabling statistically reliable alerts. On devices lacking hardware depth, scale is stabilized by fusing visual-inertial mapping with anthropometric priors. The validated result, including volume, uncertainty, quality indicators, and device provenance, is digitally signed and packaged as an interoperable artifact for integration with electronic health record and remote-monitoring systems.

The application discloses a kind of complex microstructure surface step height or groove depth uncertainty fast evaluation method, belong to optical measurement technical field.The application obtains the three-dimensional topography original measurement data of step or groove in integrated circuit complex microstructure surface, obtains effective three-dimensional topography matrix Z m With X, Y plane after dimension reduction, is decomposed into reconstruction matrix A and B, and the data point of corresponding row constitutes dot matrix;The centroid distance of data point in dot matrix after clustering of corresponding row is converted into step height or groove depth value;All row data are traversed, and the mean value of step height or groove depth value is regarded as key dimension representation result, and mean square error value is regarded as standard uncertainty;The representation value of all row data is arranged in ascending order, and the upper and lower limits of 95% confidence interval are regarded as key dimension uncertainty distribution range.The application can realize the step height or groove depth representation of all points of complex microstructure surface by once measurement, and simultaneously can realize uncertainty evaluation.

The application provides a wind direction calibration result measurement uncertainty evaluation method and system, signal transmission time between two groups of probes in a two-dimensional plane is acquired, wind direction indication values are calculated according to the relationship among the distance between the probes, the sound propagation speed and the signal transmission time; a measurement model is established based on external environmental factors and internal factors that affect the inaccuracy of the wind direction indication values and the standard wind direction values; the standard uncertainty is determined and calculated according to the measurement model; the sensitivity coefficient is determined according to the function partial derivative of each input parameter in the measurement model; the expanded uncertainty is determined according to the probability synthesis method based on the Bayesian network and the confidence interval prediction method of machine learning; specifically, starting from the measurement working principle of the ultrasonic wind direction and speed sensor, the factors affecting the wind direction measurement indication values are comprehensively analyzed, the sources of the uncertainty in the measurement process can be more accurately identified and evaluated, and the accuracy of the measurement uncertainty evaluation is improved.

The invention discloses an uncertainty modeling evaluation method and device for a link time delay calibration result and a medium, and relates to the technical field of satellite navigation high-precision time service. The method comprises the following steps: selecting a time laboratory observation station and appointing a reference station, constructing a time transfer link, resolving a receiver clock error based on an ionosphere-free combination model, and reversely deducing hardware time delay of a station to be calibrated through clock error difference; according to the method, distribution is discriminated through time delay residual error statistical characteristics, B-class uncertainty is calculated through a matching model and weighted summarization is carried out to obtain experience B-class uncertainty, standard deviation of a calibration time sequence is solved to obtain an A-class standard uncertainty estimation value, the experience B-class uncertainty and the A-class standard uncertainty estimation value are fused to calculate synthesis extension uncertainty, and the synthesis extension uncertainty is obtained. And the reliability of the calibration result is evaluated, and the uncertainty quantitative analysis of time delay calibration is realized. According to the method, a clear credible evaluation framework is provided for the link calibration error, and the reliability and interpretability of a calibration result are remarkably improved.

This application discloses a method and system for verifying the uncertainty of an absolute phase noise standard using high- and low-frequency cross-verification. The method includes: determining the source of the maximum permissible error between a millimeter-wave relative noise standard and a low-frequency absolute phase noise standard; testing the low-frequency absolute phase noise standard and the millimeter-wave absolute phase noise standard based on the source of the maximum permissible error; and verifying the uncertainty of the millimeter-wave absolute phase noise standard based on the parameters obtained from the tests. This application solves the problem of the inability to verify the uncertainty of millimeter-wave absolute phase noise standard devices, thereby verifying the uncertainty of the millimeter-wave absolute phase noise standard.

The invention relates to the technical field of pile foundation engineering quality detection, in particular to a non-reference visual evaluation method and system for pile foundation reinforcement cage stirrup spacing. The method comprises the following steps: acquiring a single-frame image of a pile foundation reinforcement cage; constructing an intersection point identification model, and identifying effective intersection points of the main reinforcements and the stirrups in the single-frame image by using the intersection point identification model; calculating a representative value of a stirrup pixel pitch, a proportionality coefficient and a standard uncertainty based on the effective intersection points; based on the proportionality coefficient, the standard uncertainty and the stirrup pixel pitch representative value, the real pitch and the expansion uncertainty are calculated; and based on the real spacing and the expansion uncertainty, generating an evaluation result of the spacing of the stirrups of the pile foundation reinforcement cage. According to the method, the problem that in the prior art, pile foundation reinforcement cage stirrup spacing evaluation is not intelligent and accurate enough is solved, through improvement and construction of the high-precision intersection point recognition model, accurate recognition of the intersection points is achieved, the real spacing and expansion uncertainty are deduced in combination with parameters and uncertainty, and the intelligence and accuracy of evaluation are improved.

The invention provides an on-site test index and theoretical analysis error confidence evaluation method, which relates to the technical field of advanced energy, and comprises the following steps: screening out key meteorological parameters by analyzing the uncertainty source of measurement data, and calculating the confidence of the measurement data by analyzing the probability distribution rule of the measurement data under the key meteorological parameters; establishing a comprehensive comparison relation and a mapping function of the measurement data; according to the electrical quantity measurement data, in combination with the comprehensive comparison relation and the mapping function of the measurement data, standard uncertainty under different evaluation results is calculated, and the effective degree of freedom of the synthesis standard uncertainty is calculated based on the standard uncertainty; and calculating an environment correction coefficient of the measurement data by using the effective degree of freedom of the uncertainty of the synthesis standard and combining a confidence level look-up table, and applying the calculated environment correction coefficient to the measurement data to adjust the measurement data. According to the invention, the dynamic compatibility of the uncertainty of the field measurement result and the data precision can be ensured.

The invention discloses a micro-force intelligent calibration and high-precision resolving method and system based on data driving, and the method comprises the steps: carrying out the loading experiment of a micro-force measurement device through a high-precision standard force source, and constructing a calibration data set; the GPR model is trained by using the training set with the capacitance value variation as an input feature and the standard force value as an output label, and the GPR model is selected as a final model based on the comprehensive performance evaluation indexes on the verification set; performing performance evaluation on the final model based on a test set of whole-course isolation and storage to obtain unbiased estimation of generalization performance; deploying the final model for actual measurement, collecting capacitance value variation caused by to-be-measured micro force, inputting the capacitance value variation into the model, calculating to obtain a force value measurement result, and synchronously outputting synthetic standard uncertainty of the measurement result; the self output performance is monitored in real time, and a decision basis is provided for predictive maintenance and model updating. According to the invention, high-precision and high-reliability measurement can be carried out on the micro force from milliNewton to micro Newton magnitude.

The application discloses a kind of platform limit measurement error evaluation methods, the platform limit measurement error evaluation methods, comprising: measuring the parameter value of each limit size of platform using measuring trolley;The parameter value obtained in step 1 is substituted into the corresponding limit size formula, and the numerical value of the corresponding limit size is calculated;Move the measuring trolley, repeat the measurement at a given unit step length, and record the data;Establish a measurement model, calculate the uncertainty of each component of the measurement model, calculate the total combined standard uncertainty u ci , and calculate the expanded uncertainty U, compare the calculated value of U with the half-width value of the maximum allowed error interval of the corresponding limit size, if the calculated value of U is less than the half-width value, it can be judged that the measured limit size parameter is qualified. The method of the above steps can evaluate the related parameter measurement of platform limit size measurement, so as to ensure the accuracy and reliability of measurement.