75 results about "Random model" patented technology

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 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 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 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 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 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 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 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.

This invention provides a combined navigation method for generating GNSS stochastic models using fisheye images, comprising: acquiring GNSS observation information from a base station and a rover; constructing a single-difference observation equation; robustly estimating receiver clock errors using predicted carrier pose to obtain single-difference residuals; projecting satellite feature vectors onto a pixel plane matrix using an omnidirectional model to correlate fisheye images with GNSS features; constructing a deep neural network based on an attention mechanism, inputting fisheye images and GNSS features, and outputting a GNSS stochastic model reflecting the effects of NLOS and multipath effects; fusing GNSS and IMU observations using an EKF (Extended Kinematics Function) and inputting the model to obtain pose estimates for the corresponding time. This invention can generate GNSS stochastic models suitable for complex urban scenarios, significantly reducing interference from NLOS and multipath effects in complex urban environments, and effectively improving GNSS positioning performance.

The invention discloses a satellite-unmanned aerial vehicle channel dynamic modeling method, and belongs to the technical field of wireless communication. The method comprises the following steps: determining a time-varying geometrical relationship of a communication link based on input satellite orbit parameters, an unmanned aerial vehicle flight path and a carrier frequency, and calculating large-scale path loss; initializing parameters for generating a line-of-sight component and a non-line-of-sight multipath component based on a geometric random model framework; synchronously adjusting a plurality of primary structure parameters in the geometric random model framework based on real-time rainfall intensity during model operation; jointly judging and updating a scattering cluster set according to the survival probabilities of the time domain and the frequency domain; calculating a current channel impulse response on the basis of initializing parameters used for generating a line-of-sight component and a non-line-of-sight multipath component, the adjusted primary structure parameters and the updated scattering cluster set; according to the invention, in a dynamic link of a complex environment of satellite-unmanned aerial vehicle communication, the dynamic influence of environmental factors such as rainfall on a low-altitude multipath scattering structure can be reflected in real time.

The specification describes a positioning technology for a global navigation satellite system (GNSS), aiming at improving the positioning accuracy in a complex environment. According to the technology, based on a corrected random model of least square variance component estimation (LS-VCE), the uncertainty of observation data is estimated more accurately, so that the positioning accuracy is remarkably improved. The technology comprises the following key steps: firstly, performing uncertainty modeling on GNSS observation data, and estimating variance and covariance components of the observation data by using an LS-VCE method; thirdly, defining a weight matrix to reflect the importance or reliability of different observation data, constructing an observation equation system, and associating the observation data with model parameters; and then, through iterative solution, optimal parameter estimation and variance component estimation are obtained by using a least square principle. And finally, verifying the effectiveness of the LS-VCE method through a simulation experiment, and optimizing the model according to an experiment result.

The invention discloses a tourniquet pressure closed-loop method based on ensemble learning and Bayesian optimization, and aims to solve the problems that in pre-hospital first aid, manual pressure application is not uniform in pressure distribution, blocking pressure is difficult to stably maintain under the working conditions of movement, temperature and humidity change and pollution, bleeding amount and perfusion recognition is lacked, and linkage with a decompression strategy is difficult. According to the method, micro-contact and seepage digital twinning, deep kernel Gaussian process disturbance inference, random model prediction control and multi-fidelity security Bayesian optimization are carried out, and gradient observation is introduced; the technical effects of reducing the tissue stress and the bleeding amount while ensuring that the occlusion probability reaches a threshold value, performing staged smooth decompression and realizing rapid convergence of individualized parameters are achieved.

The invention relates to the technical field of satellite orbit calculation, and discloses an inter-satellite baseline solution weighting method based on elevation angle grouping and variance component estimation, comprising the following steps: S1) acquiring an inter-station single-difference observation value; s2) carrying out preprocessing on the observation value; and S3) based on elevation angle grouping and a variance component estimation method, carrying out weight determination on an observation value used by parameter estimation, iterating parameter estimation and orbit integration until convergence, and generating and outputting a high-precision inter-satellite baseline. According to the inter-satellite baseline solution weighting method based on elevation angle grouping and variance component estimation, noise variances of observation values in different elevation angle intervals are determined by using a variance component estimation method, a more accurate and more objective random model is established, and the inter-satellite baseline precision is greatly improved.

The invention discloses a GNSS random model modeling method based on machine learning, and relates to the technical field of modeling methods, and the method comprises the following steps: S1, extracting a full-scene GNSS observation noise variance through zero-baseline double-receiver differential processing; s2, constructing a multi-dimensional feature set, and performing feature expansion and optimization; s3, performing nonlinear regression modeling on the noise variance and the multi-dimensional feature set by adopting a Light GBM tree model, and obtaining a dynamic adaptive prediction model through hyper-parameter optimization training; and S4, outputting a GNSS observation value random model capable of dynamically adapting to the change of the observation environment. According to the method, the multi-dimensional feature set is constructed by combining the satellite elevation angle, the signal-to-noise ratio, the positioning precision factor, the multipath effect index and the carrier motion state, key factors influencing the GNSS observation noise are systematically incorporated, the cooperative coupling effect of multiple factors on the noise can be fully described, the nonlinear dynamic change rule of the noise can be accurately captured, and the accuracy of the GNSS observation noise is improved. And the reliability of a final positioning result is ensured.

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 invention discloses a hysteresis capability degradation evaluation method for an in-service BRB buckling-restrained brace. The method specifically comprises the following steps: establishing a corresponding relation between a BRB brace base material corrosion surface topography characteristic and a service environment characteristic parameter and a service age; establishing a random model of the corroded surface morphology of the BRB base material in the corrosion environment based on the random field theory; a cyclic loading test is carried out to determine the relation between the corrosion characteristic parameters and the anti-seismic performance of the rusted BRB buckling-restrained brace; mechanical behaviors of the rusted BRB buckling-restrained brace under different working conditions are simulated through the numerical model; according to the evaluation method, mechanical test results and numerical analysis results of the rusted BRB buckling-restrained brace are compared, and the residual hysteresis performance of the in-service BRB buckling-restrained brace is converted through service environment characteristic parameters and service age. According to the method, the influence of corrosion and other factors on the hysteresis capability of the BRB buckling-restrained brace is comprehensively considered, and reliable evaluation on the residual hysteresis performance of the in-service BRB buckling-restrained brace is effectively completed.

The present disclosure relates to a computer-implemented method for price prediction, comprising: - providing a stochastic model configured to predict future price information of a financial instrument; and - executing the stochastic model in a single iteration having multiple steps by propagating a distribution of a price of the financial instrument through the multiple steps to predict the future price information of the financial instrument, wherein the distribution of the price indicates potential values of the price, and wherein each step of the multiple steps of the single iteration performs calculations on the distribution of the price.

The application discloses a data and model hybrid driving mechanical component residual service life prediction method, and relates to the field of intelligent manufacturing and health management of equipment. The method adopts extended Kalman filtering to calibrate parameters of an exponential random model, learns position information of input embedding through an adaptive coding layer of a hybrid driving prediction model, and then models a mapping relationship between input data and residual service life through a multi-head self-attention mechanism. The application combines the calibrated exponential random model and the multi-head attention neural network structure, simultaneously retains accuracy of a model-based method and generalization ability of a data-driven method, can improve the accuracy of residual service life prediction of mechanical components, and has important significance for application of the data / model hybrid driving method in the field of intelligent manufacturing and health management of mechanical equipment.

The application discloses a time-frequency electromagnetic resistivity inversion method based on resistivity and layer thickness threshold model constraints, comprising the following steps: determining an inversion depth, collecting geological information of a target area, constructing a geological model, and obtaining a two-dimensional geological model information graph of an inversion profile; determining inversion stratum information according to physical properties, and determining the electrical property layer thickness and resistivity value of the target area to constitute a solution space of the geological model; performing forward modeling on a group of random models in the solution space of the geological model to obtain a forward modeling response function; constructing a target function by using the forward modeling response function and the geological model; determining a fitting difference function of the target function and a preset iteration number, and obtaining a constraint term of the geological model; obtaining a correction amount of the geological model according to the constraint term, and combining the fitting difference to perform correction or performing correction according to the preset iteration number. In conclusion, the application has the advantages of simple logic, strong correlation, reliable inversion and the like.

The invention relates to the technical field of wireless communication, and provides an active and passive symbiotic covert communication method and system, and a power control method under fixed power comprises the steps: 1, solving an optimal detection threshold and a minimum detection error probability by a monitor through employing an improved optimal hypothesis test to represent the concealment; step 2, calculating effective throughput under fixed power according to the communication mode of the hybrid transmitter; and step 3, optimizing parameters and solving. The power control method under the random power comprises the following steps of: 1, constructing a random model of the transmitting power of the wireless radio frequency energy station; 2, a monitor adopts an improved optimal hypothesis test to solve an optimal detection threshold value and a minimum detection error probability to represent the concealment; step 3, calculating expected effective throughput under random power; and step 4, optimizing parameters and solving. According to the invention, the communication behavior between legal communication devices is not known by an illegal third party, so that the communication security is ensured.

The application discloses a low-orbit satellite precise orbit determination method and device considering navigation signal distortion deviation, and belongs to the technical fields of satellite navigation, satellite precise orbit determination and signal processing. The method is aimed at the high dynamicity of low-orbit satellite operation and complex space environment, function models and random models of a navigation signal distortion deviation estimation method suitable for the low-orbit satellite end are constructed, and high-precision extraction of the navigation signal distortion deviation of the low-orbit satellite end is realized; long-time sequence deviation values extracted are used for precision evaluation and characteristic analysis, and a low-orbit satellite navigation signal distortion deviation correction model is constructed; in the real-time orbit determination process, the correction value is added to the pseudo-range observation equation as a virtual observation value, and the low-orbit satellite precise orbit determination considering the navigation signal distortion deviation is realized. The application effectively eliminates systematic deviation caused by receiver hardware differences, firmware versions and navigation signal characteristics.

The invention provides a quality uncertainty-oriented waste power battery recycling and disassembling integrated optimization method, which comprises the following steps of: firstly, taking minimization of total operation cost as an objective function, and considering coupling constraint, recoverable packaging related constraint, vehicle related constraint, demand satisfaction constraint, inventory related constraint, disassembling line related constraint and decision variable value range constraint; constructing a stochastic programming model for recycling-disassembling integrated optimization of the waste power battery; converting the random model into an equivalent deterministic model by adopting a sample average approximation method, and performing linearization processing on a nonlinear term to enhance the solving performance; and finally, solving the proposed mathematical model by adopting a two-stage heuristic algorithm, and selecting a required optimal scene number according to an actual problem. According to the invention, integrated optimization of two stages of recycling and disassembling of the waste power battery in an environment with uncertain quality is realized, and a scientific solution is provided for a complex optimization problem in the field of recycling of the waste power battery.