108 results about "Random model" patented technology

The invention relates to the technical field of underwater intelligent navigation and integrated navigation, in particular to a submersible integrated navigation multi-source data fusion method based on an elastic random model, which comprises the following steps: preprocessing multi-source navigation data output by an inertial navigation system (INS), a Doppler velocimeter, an ultra-short baseline / long baseline, an altimeter and a depthometer; constructing a state space model containing navigation parameter errors and sensor errors, and screening an optimal sensor combination mode; the method comprises the following steps: constructing an elastic correction parameter model and a dynamic adjustment state and observation noise covariance matrix by combining an elastic PNT framework, performing local filtering on multi-source data by adopting a plurality of parallel elastic self-adaptive robust volume Kalman filters (CKF), outputting a fusion resolving result, and realizing optimal estimation of a global navigation state through a dynamic information distribution strategy. The method has relatively high anti-interference performance, self-adaptive capability and combination switching robustness, and is suitable for navigation and positioning tasks of a high-precision submersible in a complex underwater environment.

The invention discloses an ionospheric scintillation monitoring method based on a geodetic measurement receiver, which belongs to the technical field of satellite navigation, and comprises the following steps of: accurately eliminating non-scintillation errors by constructing an inter-station single-difference function model and an inter-station single-difference random model, extracting carrier phase residual errors by using a single epoch ambiguity fixing technology, and finally obtaining an ionospheric scintillation monitoring result. Establishing a reliable ionospheric scintillation monitoring index; according to the ionospheric scintillation monitoring method based on the geodetic measurement type receiver, high-precision real-time monitoring of ionospheric scintillation can be realized through a single-epoch ambiguity fixing technology under the condition of not depending on an external precision product, and reliable technical support is provided for space weather monitoring.

The invention discloses a gravitational field inversion method considering unsteady noise of a satellite gravity observation value. The gravitational field inversion method comprises the following steps: A, acquiring original observation data of a gravity satellite; b, setting observation value noise as white noise, and constructing a random model considering non-stationary noise; c, performing parameter estimation to obtain a post-test residual error of an observation value; d, estimating the noise auto-covariance and precision factor matrix of the observation value based on the post-test residual error of the observation value; e, repeating the steps C and D for iteration to realize refinement of the gravitational field parameter and observation value random model; and F, evaluating the time-varying gravity field model in the spectral domain. According to the invention, the defects in the prior art can be improved, and the precision and stability of gravitational field inversion are improved.

The invention discloses a time-varying channel estimation method and device based on correlation prediction in an RIS auxiliary system, and the method comprises the steps: assuming that the working mode of the RIS is an ON or OFF mode, and obtaining time domain cascade channel estimation at a historical moment at a base station side; constructing a training sample set by using time domain cascade channel estimation at historical moments; randomly initializing network parameters, and training the long and short-term memory neural network by using the training sample set to obtain a network model with an optimal weight and threshold; predicting the time correlation of the channel based on the trained network model; generating an AR random model by using the time correlation of the channel, and constructing a state space model of the Kalman filter based on the AR random model and the time domain receiving signal; time-varying channel estimation is carried out based on the state space model of the Kalman filter, and cascade channel estimation passing through each RIS unit is obtained. The method is suitable for high-precision acquisition of the time-varying channel information in the RIS-assisted mobile communication system.

The invention discloses a digital twinborn emergency rescue deduction optimization method for a complex disaster scene, and the method comprises the steps: constructing a composite random model for emergency resources, rescue task demands and the high dynamics and randomness of environmental elements in the disaster scene through employing a random process theory and a discretization method, and carrying out the deduction optimization of the digital twinborn emergency rescue. A deduction algorithm of a continuous state space and discrete time step length is constructed based on a digital twin deduction technology, bidirectional mapping of a physical space and a virtual space is realized, an emergency rescue optimization module based on a matching game mechanism is embedded in a digital twin deduction framework, a task demand gap and resource cost are taken as objective functions, and the task demand gap and the resource cost are taken as objective functions. And a global optimal solution is gradually approached by utilizing multi-round iteration and local strategy exchange, so that efficient optimization of resource allocation in a disaster scene is realized. According to the method, the future disaster trend can be predicted in real time, and dual optimization of task demand satisfaction and resource cost control can be realized.

The invention relates to the technical field of ore raw material analysis, and discloses an intermediate fusion strategy ore sample classification method based on random model combined data, and the method comprises the following steps: S1, collecting LIBS spectrum and Raman spectrum data of a manganese ore sample; s2, preprocessing the spectral data; s3, performing dimensionality reduction on the preprocessed LIBS and Raman data by adopting principal component analysis; s4, splicing the feature matrixes after dimension reduction to construct an intermediate fusion data set; s5, training a random forest classification model based on the intermediate fusion data set; and S6, carrying out classification and identification on the manganese ore samples by utilizing the trained model. According to the method, metal element characteristic spectral lines captured by the LIBS spectrum and molecular vibration characteristics identified by the Raman spectrum are organically integrated through an intermediate fusion strategy, two-dimensional identification is achieved, redundant information is effectively eliminated while main characteristics of the spectrum are reserved, and the accuracy and reliability of manganese ore sample classification are remarkably improved.

The invention provides a Beidou PPP-B2b positioning compensation method and system, which are used in the field of satellite navigation, and the method comprises the steps: receiving a broadcast ephemeris, correcting the broadcast ephemeris according to a Beidou satellite PPP-B2b signal to obtain a PPP-B2b precise ephemeris, and obtaining a reference precise ephemeris; according to the PPP-B2b precise ephemeris and the reference precise ephemeris, a PPP-B2b satellite orbit error and a satellite clock error are obtained through calculation, and according to the PPP-B2b satellite orbit error and the satellite clock error, the Beidou satellite PPP-B2b global average SISRE is calculated; respectively calculating a difference SISRE sequence of each satellite epoch based on the global average SISRE of the PPP-B2b, and constructing an SISRE random model based on the difference SISRE sequence of each satellite epoch; the relation between PPP-B2b positioning precision and SISRE random model parameters is measured through experiments, and SISRE optimal process noise parameters are selected from an SISRE random model to compensate a PPP positioning model. According to the scheme, the convergence speed of PPP-B2b positioning can be increased.

The invention discloses a lithology identification method and system based on TLS, and belongs to the technical field of lithology intelligent identification, and the method comprises the steps: S1, collecting all rock data of a target region, and carrying out the preprocessing of the rock data, and obtaining outcrop point cloud data; s2, constructing a lithology identification unit, and dividing the outcrop point cloud data into point cloud blocks of regular grids according to a preset grid; s3, extracting features with lithologic sensitivity from the point cloud blocks, wherein the features comprise geometric features and spectral features; and S4, inputting the geometric features and the spectral features into a trained random model forest model, and fitting the geometric features and the spectral features by adopting a random forest to obtain lithology categories of various rocks in the target area. According to the method, automatic classification of outcrop lithology is realized, powerful support is provided for geological exploration and lithology analysis, understanding of the geological field is introduced into effective exploration of the artificial intelligence field, and interdisciplinary fusion development is promoted.

The invention discloses the field of automatic control systems, and particularly relates to a random model predictive control method and equipment triggered by a self-adaptive event. The method comprises the following steps: S1, dynamically adjusting the sampling time of a control system and the updating frequency of control input through a self-adaptive event triggering mechanism; the self-adaptive event triggering mechanism is adjusted according to a system state and a change condition of control input; s2, using a distributed robust optimization method to convert a prediction control problem of the random model with opportunity constraint into a second-order conic convex optimization problem for solving; and S3, at each sampling time, determining the optimal control input by solving the second-order cone convex optimization problem, performing feedback control on the state of the control system, and adjusting the communication sampling frequency. According to the method, unnecessary sampling and communication costs are reduced, the feasibility and robustness of the optimization problem are improved, the calculation burden of the system is reduced, and meanwhile, the stability of the control system is ensured.

The invention provides a high-precision precise point positioning method based on BDS-3, and relates to the technical field of wireless communication. The method comprises the following steps: firstly, introducing a traditional ionosphere-free combination model to eliminate the influence of ionosphere delay on positioning precision, and eliminating or weakening a satellite orbit error and a clock error error by using satellite orbit and clock error data provided by an IGS; secondly, constructing a troposphere correction model of adaptive weighted wet delay for eliminating troposphere delay errors; thirdly, establishing a self-adaptive random model, and reasonably weighting pseudo-range and carrier phase observed quantity; and finally, estimating the parameters to be estimated through extended Kalman filtering so as to obtain a positioning calculation result. The method has obvious advantages in the aspect of improving the positioning precision, can meet the requirement of high-precision positioning, can realize centimeter-level positioning precision no matter in a dynamic mode or a static mode, and can even reach millimeter-level positioning precision in the static mode in some observation stations and directions.

The invention relates to a river channel sand body random model generation method and device based on hidden Markov model data filling, a medium and equipment. The method comprises the steps that logging data of a target area are collected and arranged; dividing logging data according to a logging gas-bearing interpretation conclusion, and carrying out analytical statistics on the velocity and density of longitudinal and transverse waves in the logging data; initializing an initial state probability, a state transition matrix, and a mean value and a covariance of Gaussian distribution in the hidden Markov model; inputting a three-dimensional observation vector composed of the velocity and density of the longitudinal and transverse waves into the initialized hidden Markov model for iterative training; performing down-sampling on the lithology data, then constructing a state transition probability matrix, and further constructing a lithology sequence conforming to a geological law through simulation; according to the lithologic sequence, calling a hidden Markov model to generate longitudinal wave velocity, transverse wave velocity and density corresponding to the lithologic sequence; and calculating a reflection coefficient of the synthetic stratum, setting a Ricker wavelet dominant frequency, and obtaining synthetic seismic response data through convolution.

The invention discloses a post-processing device of a true random number generator, which comprises a random seed simulation module used for generating random seeds; the first processing module is used for performing accelerated multiplication on each random seed group formed by the random seeds and each original random number sub-vector formed by the original random number vector output by the true random number generator based on fast Fourier transform to obtain a first result corresponding to each random seed group; or extracting bits in the original random number vector through a single-bit extractor according to each random seed group formed by the random seeds to obtain a first result corresponding to each random seed group; splicing all the first results to obtain an intermediate result; and the second processing module obtains an actual random number vector according to the intermediate result. According to the method, bit correlation defects can be processed, original random numbers from a plurality of correlative entropy sources can be processed, and the method has unconditional security independent of any calculation hypothesis or random model.

The present invention belongs to the field of federated learning technology and discloses a method, apparatus, device, and medium for constructing a random model sharing pool for federated learning. The method comprises: randomly selecting at least one base model from the model sharing pool; randomly assigning the at least one base model to multiple participants; obtaining the model parameters of the local model recorded by each participant after training, aggregating the model parameters of all local models corresponding to the same base model after training, and obtaining a global model after the aggregation of each base model; then randomly selecting at least one global model and re-randomly assigning it to multiple participants to update the local model of each participant, and each participant then performs the next iterative training with the updated local model until the global models corresponding to each base model converge. The present invention can provide better guarantees for privacy protection in federated learning and provide a better privacy protection solution for digital governance.

The invention provides a PPP-RTK random model construction method and system for weakening ionospheric scintillation influence, and belongs to the technical field of navigation positioning, the PPP-RTK random model is applied to an observation equation of a PPP-RTK rapid precision positioning model to carry out PPP-RTK positioning, and the method comprises the following steps: when the carrier-to-noise ratio of satellite observation data is higher than a preset threshold value, determining that the PPP-RTK random model is a PPP-RTK random model; determining weight factors of the pseudo-range and the phase observation value based on the satellite elevation angle so as to set random noise distribution obeyed by the pseudo-range and the phase observation value in the observation equation; and when the carrier-to-noise ratio of the satellite observation data is lower than a preset threshold value, determining weight factors of the pseudo-range and phase observation values according to the carrier-to-noise ratio intensity and the satellite elevation angle so as to set random noise distribution obeyed by the pseudo-range and phase observation values in the observation equation. According to the invention, the influence of ionospheric scintillation on PPP-RTK positioning is effectively weakened, and rapid, accurate, continuous and steady PPP-RTK positioning in a scintillation environment is realized.

The application provides a verification method and system of a maximum likelihood sequence detection chip, the verification method comprises creating a random environment, and a random model is created in the random environment; a verification environment is created, a reference model is run in the verification environment, and a device to be tested is run in the verification environment; the random environment is run, random configuration information is generated from the random environment and is sent to the reference model; the reference model forms a test signal according to the received configuration information, and inputs the test signal to the device to be tested; the reference model generates reference data according to the configuration information and outputs the reference data to a checker, the device to be tested generates running data after running according to the test signal and outputs the running data to the checker, and the checker compares the reference data with the running data and generates a verification result. The application also provides a verification system for realizing the above method. The application can realize decoupling of generation of configuration information and the verification environment, and improves the accuracy of chip verification.

The invention relates to a stochastic simulation method considering dimensional deviation and concrete material variability, and belongs to the field of concrete structure analysis. According to the method, accurate simulation of the mechanical properties of the concrete structure under the influence of construction errors and material randomness is realized by determining random factors, establishing a random field matrix, carrying out sample transformation, establishing random models in batches, solving in batches and carrying out post-processing. A random simulation result is used as an output parameter, a random sample is used as an input parameter, and a PRRM-EPR algorithm is adopted to establish a machine learning prediction model. Compared with a traditional method, the method has the advantages that the generation quality of the random sample is improved, the calculation cost is reduced, the randomness in actual engineering can be better reflected, and the established prediction model can directly predict the mechanical response (displacement, load and the like) of the component through actually measured random parameters. The method is suitable for design, evaluation and optimization of a concrete structure, and particularly has a remarkable effect on improvement of structural safety.

The application provides a single-step absolute antenna phase center calibration method and system with PCV constraint, comprising the following steps: establishing an antenna phase center calibration field based on a mechanical arm, forming a short baseline by using the mechanical arm and a static observation base pier, and collecting calibration data outdoors; constructing three-difference observation values, determining an observation model and a random model, and calculating the attitude of the antenna through mechanical arm data; for the three-difference observation values, a PCO approximate value is given, the observation value residual is calculated, then a spherical harmonic function is used to model the PCV, and the PCV value on the grid point is solved and output; according to the coupling between the PCO and the PCV, the phase model is expressed as different combinations of the PCO and the PCV, the minimum PCV constraint is applied, and the final consistent phase model result is obtained, so that the antenna phase center calibration is realized. The application has the advantages that the two-step estimation is reduced to one step, the data processing flow is simplified, the convergence speed of the antenna phase solution is improved, and the consistency of the antenna phase model calibration result is enhanced.

The present invention provides a real-time adjustment method for the random walk variance of the BDS PPP ionospheric delay, which relates to the technical field of GNSS satellite navigation and positioning. This method constructs a random walk form of the ionospheric delay, utilizes the parameter correlation between the epoch-to-epoch ionospheric delay EDI change value and the true ionospheric delay in the short term, weakens the noise in the ionospheric observations, calculates the power spectral density PSD of the ionospheric delay, and then combines the relationship between the PSD and the random walk variance to adjust the variance constraint value in real time, thereby effectively shortening the integer ambiguity fixing time and improving the positioning accuracy. The present invention realizes the real-time adjustment based on the random model of the BDS ionospheric parameters, effectively improves the positioning accuracy, and has strong practical application value.

The application relates to the technical field of satellite orbit calculation, and discloses an inter-satellite baseline solution weight determination method based on elevation angle grouping and variance component estimation, which comprises the following steps: S1) obtaining inter-station single-difference observation values; S2) pre-processing the observation values; S3) determining the weights of the observation values used for parameter estimation based on the elevation angle grouping and variance component estimation method, iteratively performing parameter estimation and orbit integration until convergence, and generating high-precision inter-satellite baseline and outputting the same. The inter-satellite baseline solution weight determination method based on the elevation angle grouping and variance component estimation determines the noise variances of observation values in different elevation angle intervals by using the variance component estimation method, establishes a more accurate and objective random model, and greatly improves the inter-satellite baseline precision.

The present invention relates to the field of ocean engineering technology, and provides a method for rapidly and randomly correcting the model of a step-type ocean platform structure. According to the design parameters of the ocean platform, an initial finite element model of the ocean platform is established; among the design parameters, the parameters to be corrected are selected, and the maximum and minimum values of each parameter to be corrected are determined; sub-sampling is performed on the maximum and minimum values of each parameter to be corrected to obtain a matrix of corrected parameters; the matrix of corrected parameters is substituted into the initial finite element model to obtain an updated finite element model, and the structural frequency vector corresponding to the vector of corrected parameters is obtained, and a Kriging model is established; based on the vector of corrected parameters and the Kriging model, the frequency of the initial finite element model is calculated; a distance objective function and a distribution objective function are constructed to perform two corrections on the mean and standard deviation of the corrected parameters; based on the frequency of the finite element model after the second correction, the corrected finite element model of the ocean platform is obtained.

The invention relates to an ionosphere pseudo observation random model construction method considering time-varying characteristics and baseline trend, which comprises the following steps: extracting a single-difference ionosphere delay estimated value as a sample, counting the random characteristics of the sample through a sub-window, and converting the absolute value expectation of the sample in the sub-window into an estimated value of a single-difference ionosphere delay standard deviation; the method comprises the following steps: selecting a satellite elevation angle, a baseline length, local time and a baseline azimuth angle as influence factors, expressing the standard deviation of single-difference ionosphere pseudo observation as a product form of each influence factor, and introducing logarithmic transformation into the standard deviation of single-difference ionosphere pseudo observation; and converting the product form into a linear regression form taking a satellite elevation angle, a baseline length, local time and a baseline azimuth angle as independent variables, and performing exponentiation on the linear regression form to obtain a single-difference ionosphere pseudo observation random model. The interpretation capability of the random model constructed by the method on the uncertainty of the ionosphere is remarkably enhanced, and the RTK resolving performance is remarkably improved under the condition of a low-latitude complex ionosphere.

The application discloses a global ionospheric model precision factor generation method and device, and belongs to the technical field of satellite navigation and ionospheric modeling. The method comprises the following steps: inversing ionospheric vertical total electron content based on carrier phase smoothing pseudo-range; extracting ionospheric unmodeled error by using a global ionospheric grid map; analyzing the skewness and kurtosis distribution characteristics of the error; combining experience error optimization and measured error driving to reconstruct the ionospheric model precision factor, and improving the reliability and stability of the precision factor by introducing monitoring point classification, scale adjustment factor, inflation factor time smoothing and other mechanisms; further proposing an ionospheric error precision consistency map and an RMS envelope probability index to verify the consistency of the precision factor and the measured error. The application can generate a precision factor that truly reflects the statistical characteristics of ionospheric error, provides effective support for the construction of an ionospheric random model in high-precision GNSS positioning, and significantly improves the positioning precision and convergence performance.

The invention relates to a satellite clock estimation joint weighting method based on mutual information and clustering, comprising the following steps: S1, reading original observation data of a monitoring station covering a service area, extracting elevation angle and signal-to-noise ratio parameters for each satellite, and constructing the elevation angle and signal-to-noise ratio parameters into two-dimensional feature matrix data; s2, classifying the two-dimensional feature matrix data by adopting K-means clustering to obtain cluster distribution; s3, respectively calculating first mutual information between the elevation angle and the cluster as a first weight and calculating second mutual information between the signal-to-noise ratio and the cluster as a second weight by using a mutual information method; s4, substituting the first weight and the second weight into a random model to form a joint weighting model; s5, calculating a dual-frequency pseudo-range noise variance and a carrier phase noise variance by using a joint weighting model; and S6, performing posterior quality control on the real-time satellite clock error Kalman filtering, performing Kalman filtering posterior residual detection, finding out an abnormal value, and removing the abnormal value.

The invention belongs to the technical field of software testing, and particularly relates to an automatic testing method for automatic flight control software, which comprises the following steps of: calling different units and parts of the automatic flight control software through a configured interface; and unit-level, component-level and configuration item-level curve consistency testing of the automatic flight control software can be realized under the condition that the software is not changed. And the random input test method is designed, the random model can be generated by converting the system model, the system model does not need to be changed by developers, and the use difficulty of the developers is reduced. And by designing an automatic comparison script of the test data, whether the test is passed or not and specific information of the data which does not pass the test are clearly displayed to developers. The automatic testing method is flexible to use, high in reusability and simple to operate, the testing efficiency can be remarkably improved, and the correctness of software implementation is ensured.

The present disclosure discloses a three-dimensional inversion method of airborne transient electromagnetics based on deep learning. The method of the present disclosure proposes two strategies that focus on training datasets, to improve the performance of deep learning models, including divide and conquer strategy and random models generating. Through a large of reasonable structural models, appropriate network setups, a more generalized result can be obtained through our proposed U-Net framework, which has been demonstrated to be effective on both synthetic and field data. This scheme can realize the rapid prospecting of three-dimensional resistivity structure in large-area target region, and solve the problem of low efficiency of traditional three-dimensional inversion calculation of ATEM and poor migration ability of three-dimensional inversion based on deep learning developed by predecessors.

The invention provides a verification method and system for a maximum likelihood sequence detection chip, and the method comprises the steps: creating a random environment, and creating a random model in the random environment; a verification environment is created, the reference model runs in the verification environment, and the to-be-tested device runs in the verification environment; running a random environment, generating random configuration information by the random environment, and sending the random configuration information to the reference model; the reference model forms a test signal according to the received configuration information and inputs the test signal to the device to be tested; the reference model generates reference data according to the configuration information and outputs the reference data to the checker, the to-be-tested device operates according to the test signal and then generates operation data and outputs the operation data to the checker, and the checker compares the reference data with the operation data and generates a verification result. The invention further provides a verification system for implementing the method. According to the invention, the generation of the configuration information and the decoupling of the verification environment can be realized, and the accuracy of chip verification is improved.

The application discloses a model training method and device, and belongs to the field of image processing. The method comprises the following steps: training a first model based on random model parameters to obtain a second model, wherein the first model is used for performing first processing on a face image; training a third model based on first model parameters of the second model to obtain a fourth model, wherein the third model is used for performing second processing on the face image, and the second processing is different from the first processing; and training a fifth model based on second model parameters of the fourth model to obtain a sixth model.

The present invention relates to the fields of cryptography and multi-source side channel analysis, and more specifically, to a multi-source adversarial side channel analysis method for cryptographic algorithms based on random models. By introducing the idea of ​​multi-source adversarial learning into the random model modeling process, the modeling accuracy of the random model is improved, and key recovery is achieved more efficiently. The present invention utilizes a noise source and an energy source for adversarial learning, introduces the conditional probability of the normal distribution and the KS test as a basis for adversarial learning judgment, and independently designs a random model modeling parameter iteration mechanism to improve the accuracy of traditional random model parameters; the method converts it into a more intuitive mathematical formula, which helps to improve the accuracy of various methods related to random models and reduce the difficulty of cryptographic algorithm side channel analysis; the present invention introduces the multi-source adversarial idea, sets a basis for adversarial learning judgment, improves the multi-source modeling accuracy of the random model, and has higher parameter quality and lower cryptographic algorithm key recovery difficulty.

The invention relates to the technical field of rail vehicle dynamics, and provides a rail vehicle dynamics performance stochastic analysis method and system and a computer, and the rail vehicle dynamics performance stochastic analysis method comprises the steps: obtaining a plurality of irregularity data sample groups of a rail, and obtaining a plurality of amplitude coefficient sets; a joint probability distribution model of a plurality of amplitude coefficient sets is constructed, and track irregularity excitation is obtained; constructing a dynamic simulation model of the railway vehicle, obtaining dynamic performance indexes of the railway vehicle, and constructing a dynamic performance random model of the railway vehicle; based on the sparse polynomial chaos primary function set and a rail vehicle dynamic performance random model, obtaining a rail vehicle dynamic performance random agent model; and obtaining a vehicle dynamics performance stochastic analysis result of the railway vehicle. Through the above method, the random correlation between the irregularity states of different rails is considered, and the limitation of the analysis of the dynamic performance of the rail vehicle is reduced.

The application relates to a Beidou regional network earth rotation parameter solving method and system based on random model refinement. The method comprises the following steps: acquiring a historical high-precision earth rotation parameter sequence, statistically processing high-order differences, verifying the stability of the statistically obtained high-order differences, providing GNSS observation method equations, providing inter-satellite link observation method equations, providing virtual observation equations based on the high-order differences, and restraining the variation of earth rotation parameters between days, and jointly solving satellite orbits and earth rotation parameters. The method provided by the application can improve the solving precision of the regional network ERP by adding Ka-band inter-satellite two-way observation and refining the random model to provide prior constraints.