46 results about "Random model" patented technology

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 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 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 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 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 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.

The application discloses an optimization pilot implementation method based on image super-resolution in a non-stationary channel scene, wherein in an offline stage, RB data of the non-stationary channel is generated through a DMRS densest pilot pattern and a geometry-based random model, a channel frequency domain response of pilot dimension is generated as a training set through a random two-dimensional pilot muting mechanism and a random pilot RE / data RE power ratio setting, a pilot dimension super-resolution convolutional neural network is trained, a high-resolution channel frequency domain response is obtained according to the trained Pilot-SRCNN, and after interpolation processing, the high-resolution channel frequency domain response is further used as a training set for training a resource block super-resolution convolutional neural network; in an online stage, according to to-be-measured RB data, all selectable two-dimensional pilot muting mechanisms and pilot RE / data RE power ratios are selected, and the to-be-measured RB data is obtained through the trained resource block super-resolution convolutional neural network to obtain a mean square error of the to-be-measured RB data, and when the mean square error satisfies a maximum energy efficiency standard, the pilot is set according to the RB data. The application can avoid resource waste and significantly improve system energy efficiency by optimizing system energy efficiency (EE) of different non-stationary channel scenes.

The invention discloses an RTK random model refining method based on pseudo-range error distribution prediction. The method comprises the following steps: extracting a pseudo-range double-difference observation value residual error, and independently segmenting a residual error sequence of each satellite based on a variance point change detection method; performing layered parameterized model fitting and parameter estimation on the segmented residual error sequence; extracting a satellite elevation angle, an azimuth angle, a signal-to-noise ratio, a signal lock loss identifier and pseudo-range information from GNSS original observation data as features; a composite loss function combining a Huber function and priori knowledge constraints is constructed to suppress the influence of extreme abnormal values, and an intra-batch sample weight reduction strategy is introduced to improve the training stability; predicting a mean value and a standard deviation parameter of pseudo-range error distribution in real time by adopting a space-time diagram neural network architecture fusing LSTM and a diagram attention network; the predicted distribution parameters are used for refining the RTK random model in Kalman filtering; and finally, ambiguity fixation is carried out through an LAMBDA method, and a fixed solution or a floating solution is output.

The invention discloses a wide-area GNSS time-frequency transmission method, device and equipment based on crowdsourcing RTK technology, and the method comprises the steps: building an unbiased single-difference ionosphere and troposphere delay correction model at a server side through the atmospheric correction information generated by a reference station and a user side in a convergence region on the basis of a single-difference ionosphere weighted observation model, and transmitting the unbiased single-difference ionosphere and troposphere delay correction model to a server side; atmospheric correction prior information is broadcasted to a time-frequency transmission user, and a crowdsourcing RTK single-difference observation model is constructed by fixing integer ambiguity through the atmospheric correction prior information; on the basis of a crowdsourcing RTK single-difference observation model, pseudo-range hardware delay is separated from clock difference parameters, only phase hardware delay is absorbed, a phase clock model is formed, and a corresponding random model is constructed. According to the invention, the stability of zero short baseline condition magnitude is realized, and the precision upper limit of the existing GNSS time transfer technology is broken through. Meanwhile, the advantages of the crowdsourcing RTK technology in the aspect of ionosphere space transfer are utilized, and the capability is expanded to a wide area range.

The invention provides a random MPC control method driven by reinforcement learning and inspired by terminal strategy optimization, and relates to the field of on-orbit avoidance control, and the method comprises the steps: constructing a discrete relative dynamics model; generating expert demonstration data through stochastic model predictive control (SMPC); defining the state and action of reinforcement learning and designing an instant reward function; training a terminal strategy by adopting an offline reinforcement learning algorithm, determining a reinforcement learning state in a prediction time domain and generating corresponding reference control input to obtain an initial reference control sequence; and a random model prediction control SMPC framework is adopted, an initial reference control sequence is introduced into a target function, solving optimization is performed to obtain a current control quantity, and prediction and optimization at the next moment are propelled in a rolling manner. According to the method, through fusion of reinforcement learning and random model prediction control, the spacecraft control has better adaptability and robustness when dealing with orbit perturbation and environment uncertainty, and the overall control performance of spacecraft space debris avoidance is remarkably improved.

The application provides an AGV communication channel modeling method and system based on an ellipsoid geometry random model, and belongs to the technical field of wireless communication channel modeling; the method comprises the following steps: a three-dimensional digital scene covering multiple frequency bands, AGV speeds and container densities is constructed, and a multi-dimensional channel data set is constructed based on ray tracing; a dynamic ellipsoid geometry model with a fixed base station and an AGV as the focus is constructed, and a time-varying characteristic is introduced by updating the AGV position in real time; the communication channel is divided into three parts, namely, a line-of-sight part, a ground reflection part and a non-line-of-sight part, the channel complex gains of the three parts are respectively modeled, and are superimposed to synthesize the channel impulse response under the FR3 frequency band. Through the introduction of the speed-dependent Doppler effect and the density-dependent metal scattering mechanism, the time-varying non-stationary characteristics of the AGV channel in the metal dense scene of the port can be accurately described, and efficient and accurate theoretical model support is provided for the intelligent port AGV communication design.

The invention discloses a power system probabilistic load flow calculation method based on a calculation force load random model. The method comprises the following steps: acquiring illumination intensity data, wind speed data and basic data of a power grid system; establishing a probability distribution model of wind power and photoelectric output and an output probability distribution model of a common load in the line; establishing a probability model of the data center load; constructing a linearized probabilistic power flow model of the power system structure; according to the linearized probabilistic power flow model, calculating the original moment and the center distance of an input variable to obtain the semi-invariant of each order, and further obtaining a state variable and the semi-invariant of the branch power flow; and obtaining a probability density function and a cumulative distribution function of the state variable and the branch power flow according to the state variable and the semi-invariant of the branch power flow. The method not only considers the photovoltaic and wind power uncertain power injection of the power distribution network, but also considers the random model of the calculation load of the data center, so that the result is closer to reality, the calculation accuracy is high, and the speed is high.

The invention relates to an equipment life prediction method and device based on multi-modal fusion. The method comprises the following steps: acquiring equipment health degree time sequence data; performing quality evaluation based on the time sequence data, and calculating a comprehensive quality score; a Savitzky-Golay filtering parameter is dynamically adjusted on the basis of the comprehensive quality score; on the basis of the adjusted Savitzky-Golay filtering parameters, judging degradation and akaike information criteria through CUSUM, and calculating a first residual life prediction value of the equipment; calculating a second remaining life prediction value of the device based on a physical mechanism of the device; a multi-sensor Wiener random model based on the equipment is constructed; calculating a third residual life prediction value of the equipment through the Wiener random model; and fusing the first residual life prediction value, the second residual life prediction value and the third residual life prediction value through confidence to obtain a final life prediction value of the equipment. Through fusion of multiple life prediction models, the accuracy of equipment life prediction is improved, and the risk of accidental shutdown is reduced.

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 invention relates to the technical field of energy system scheduling and control, and discloses a biomass energy green energy utilization system scheduling method, which comprises the following steps: collecting real-time data of raw materials, loads and system operation, and constructing a multi-dimensional system state vector; predicting a multi-energy load by using an LSTM network, and modeling a raw material supply capability in combination with a graph attention network; the prediction result and the state vector are input into a collaborative optimization solver based on random model prediction control, and an optimal scheduling instruction is generated under the condition that safety, material and environmental protection constraints are met by taking the minimum full life cycle cost as a target; and closed-loop adaptive scheduling is realized through a rolling time domain mechanism. The system comprises a multi-source data acquisition module, a state construction module, a load and raw material prediction module, a collaborative optimization module and a rolling execution module. According to the invention, the operation efficiency, economy and robustness of the system are obviously improved.

A park integrated energy system optimization operation method considering multiple uncertainties comprises the steps that a fuzzy parameter space of multiple uncertain parameters is constructed, and the fuzzy parameter space comprises a source load equipment random model and a fuzzy parameter space model; establishing a scene slice optimization operation model based on membership function dimension reduction analysis, wherein the model comprises an objective function and constraint conditions composed of power balance constraint, energy purchase constraint, conversion equipment constraint, energy storage constraint and uncertain parameter constraint; and carrying out sensitivity analysis on the fuzzy parameters by using a fuzzy parameter global sensitivity analysis method based on a membership function. According to the park integrated energy system optimization operation method considering multiple uncertainty, economic and robust operation of the park integrated energy system can be realized in multiple uncertainty environments, and key uncertainty factors and influence intensity thereof can be accurately identified.

The invention discloses a dual-mode adaptive random model adjustment method, system and device and a medium, and the method comprises the steps: obtaining real-time state sensing data at each epoch of a Kalman filtering calculation process through monitoring a prediction error sequence of Kalman filtering and calculating a normalized prediction error square statistic; judging whether the monitoring point enters a high-dynamic mode or not; in response to the judgment of entering the high-dynamic mode, executing a model adjustment operation through a continuous adaptive control algorithm to obtain an adaptive filtering model for the high-dynamic mode; when the adaptive filtering model is adopted, judging whether a dynamic event is ended or not; in response to judging that the dynamic event is ended, recovering the filtering model to a state before adjustment; a filtering model in a current state is applied to original observation data for positioning calculation, a new prediction error sequence generated by calculation is fed back, and a continuous high-precision positioning result is obtained. According to the invention, timeliness and reliability of geological disaster monitoring and early warning are substantially improved.

The application relates to a device life prediction method and device based on multi-modal fusion, which comprises the following steps: acquiring device health time series data; performing quality evaluation based on the time series data and calculating a comprehensive quality score; dynamically adjusting Savitzky-Golay filtering parameters based on the comprehensive quality score; calculating a first residual life prediction value of the device by CUSUM discrimination and Akaike information criterion based on the adjusted Savitzky-Golay filtering parameters; calculating a second residual life prediction value of the device based on the physical mechanism of the device; constructing a Wiener random model based on the multi-sensor of the device; calculating a third residual life prediction value of the device through the Wiener random model; and obtaining a final life prediction value of the device by fusing the first residual life prediction value, the second residual life prediction value and the third residual life prediction value through confidence. The application improves the accuracy of device life prediction and reduces the risk of accidental shutdown by fusing multi-life prediction models.