18 results about "Spatial estimation" patented technology

The invention discloses a power distribution network harmonic prediction method and device based on a cascade space-time diagram network, and belongs to the field of power distribution network electric energy quality monitoring. According to the method, historical harmonic voltage content ratio and injection power data of a measurement node are taken as input, and an HRU predicted value of the measurement node is output through a single-point time sequence prediction module based on a Transform architecture; the topology and power prediction data of the power distribution network and the HRU predicted value of the measurement node are fused, an inter-node harmonic propagation path is modeled through a space estimation module based on the graph neural network, the HRU value of the non-measurement node is estimated, and the predicted value of the THD is obtained based on index fusion calculation. According to the method, time sequence-space joint modeling is applied to harmonic analysis, through network cascading of multifunctional modules, the node harmonic THD prediction precision in dynamic topology and sparse measurement scenes is effectively improved, the method can be integrated in a power grid energy management system, and real-time decision support is provided for harmonic treatment.

The invention discloses a multi-period non-stationary hydrological variable valuation method, and relates to the technical field of hydrological prediction. Comprising the steps of obtaining hydrological variables of a plurality of observation stations in a drainage basin at different time points as hydrological time sequences of the observation stations; constructing an experimental variation function according to the hydrological time sequence of each observation station; selecting a drift model through a trend surface analysis method and model inspection; constructing a multi-period non-stationary variable equation set comprising a Lagrange multiplier and a hydrological variable weight based on the experimental variation function and the drift model; expressing the multi-period non-stationary variable equation set as a matrix form, and calculating a Lagrange multiplier and a hydrological variable weight through a matrix inversion method; and constructing an estimation formula according to the calculation result of the Lagrange multiplier and the hydrological variable weight, and performing multi-period spatial estimation on the hydrological variable of the to-be-measured drainage basin through the estimation formula. Dependence on the number of observation stations can be reduced, and accurate estimation can be achieved only through a small amount of observation station data.

The invention discloses a multi-period non-stationary multi-hydrological variable spatial valuation method, and relates to the technical field of hydrogeology. Comprising the steps of obtaining a univariate time sequence; combining the univariate time sequences of all observation stations in the drainage basin to obtain a multivariate random time sequence; calculating a long-term average level of each observation station through a space-time random function to obtain a long-term trend; subtracting the multivariable random time sequence of each observation station from the corresponding long-term trend to obtain a random residual error; according to the random residual error, determining a covariance function and a variation function between any two observation stations in the drainage basin; and on the basis, a multi-period non-stationary multivariable space valuation model is constructed by combining a multivariable Kriging model theory, and multi-hydrological variable valuation is performed on the drainage basin to be measured. According to the invention, internal correlation between variables can be revealed, and the prediction precision and reliability can be effectively improved.

The present invention discloses a PM based on time series and deep learning framework 2.5 The estimation method includes obtaining the PM of the area to be estimated. 2.5 Ground observation data, AOD product data, meteorological data, land use / cover and economic and social data are used together as multi-source data; the multi-source data are encoded into a time series format to obtain time series data; the spatial module, temporal module and enhancement module of the spatiotemporal enhanced neural network model are sequentially constructed to treat the PM 2.5 Perform inversion estimation; input the data of the prediction set into the spatiotemporal enhanced neural network model that has been tested for stability and accuracy, and calculate the PM 2.5 Perform spatial continuous estimation and obtain PM with a spatial resolution of 1 km 2.5 The present invention makes full use of PM 2.5 The temporal and spatial changes of remote sensing data can generate long-term, more detailed, more reliable and higher-resolution PM 2.5 The product has important theoretical and practical significance for the prevention and control of air pollution in my country.

A three-dimensional space estimation system includes a relative depth acquisition unit that acquires relative depth of a two-dimensional image, a three-dimensional skeleton information acquisition unit that acquires three-dimensional coordinates of reference points set on a human body in the two-dimensional image, a two-dimensional skeleton information acquisition unit that acquires two-dimensional coordinates of the reference points in the two-dimensional image, a parameter calculation unit that calculates parameters of a conversion formula by substituting the three-dimensional and two-dimensional coordinates of the reference points into the conversion formula, a linear ratio acquisition unit that acquires a linear ratio on the basis of an absolute distance and a relative distance between specific points on the human body, and a three-dimensional space model generation unit that generates a three-dimensional space model by arranging pixel values of the two-dimensional image at corresponding positions in a three-dimensional space using the parameters, the linear ratio, and the relative depth of each pixel.

A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas

The invention belongs to the technical field of rail transit, and particularly discloses a ballasted track bed surface ballast space pose estimation method based on machine vision. Comprising the steps of ArUco code deployment on the surface of a railway ballast, three-dimensional scanning to construct a point cloud model, local coordinate system establishment based on a mark position, camera parameter calibration, first frame image coordinate system association relation solving and railway ballast pose space estimation. The reasonability of logic and the robustness of an algorithm are tested in a laboratory, the accuracy of the method is guaranteed through multi-mark error analysis, meanwhile, railway ballast group motion tracking is achieved through multiple combination marks, a high-precision kinematics data basis is provided for intelligent monitoring of the state of a ballast bed, and the reliability of the method is improved. The follow-up research on the association relationship between the railway ballast motion characteristics and the ballast bed state is facilitated.

A local anomaly detection model for monitoring a local entity set of a network system is generated by applying a transformation function to a global anomaly detection model for the network system, without re-training the global anomaly detection model for the local entity set. The global anomaly detection model, which may be generated via unsupervised learning methods, includes global vector(s) of metrics and a global healthy vector space. The transformation function is estimated and applied to the global anomaly detection model to generate the local anomaly detection model, which includes local vector(s) of metrics pertaining to the local entity set and a local healthy vector space. Responsive to a determination that the local vector(s) of metrics comprises one or more anomalous data points outside of the local healthy vector space, an alert regarding the one or more anomalous data points can be generated and output.

This invention belongs to the field of rail transit technology, specifically disclosing a machine vision-based method for estimating the spatial pose of ballast on the surface of ballasted track. The method includes: deploying ArUco codes on the ballast surface, constructing a point cloud model through 3D scanning, establishing a local coordinate system based on marker positions, calibrating camera parameters, solving the coordinate system correlation of the first frame image, and estimating the spatial pose of the ballast. The rationality of the logic and the robustness of the algorithm were tested in the laboratory, and the accuracy of the method was ensured through multi-marker error analysis. Furthermore, various combinations of markers were used to achieve ballast group motion tracking, providing a high-precision kinematic data foundation for intelligent monitoring of track bed conditions, facilitating subsequent research on the correlation between ballast motion characteristics and track bed conditions.

The invention discloses a dictionary adaptive processing method and device based on feature subspace guidance, and the method comprises the steps: selecting space-time snapshot data from an adjacent distance unit of a to-be-detected distance unit, carrying out the clutter subspace estimation of a constructed sample set, and obtaining a clutter subspace; according to a self-adaptive dictionary matrix generated by the clutter subspace, reconstructing to obtain a clutter covariance matrix; obtaining a clutter and noise covariance matrix for calculating the weight of the STAP filter according to the clutter covariance matrix; and obtaining an STAP filter weight according to the clutter plus noise covariance matrix, and filtering the space-time snapshot data of the distance unit to be detected according to the STAP filter weight to obtain filtered data. The method has stronger clutter structure characterization capability, fundamentally solves the problem of grid mismatch, overcomes the defects of overlarge calculation amount and high correlation of dictionary atoms, and improves robustness while significantly reducing sparse recovery calculation complexity.

The invention discloses a high-resolution subspace estimation method for array covariance matrix correction, and the method comprises the steps: building a receiving signal model based on transmitting signals received by M array elements of an array antenna at a t moment; dividing the array antenna into a first sub-array and a second sub-array, respectively obtaining covariance matrixes of the two sub-arrays, performing characteristic decomposition, and dividing a signal space US into two signal sub-spaces; determining a rotation invariant factor matrix psi LS according to the two signal subspaces, and solving P eigenvalues of the psi LS to obtain P signal arrival angle estimated values; determining a probability density function of the L times of snapshots according to the received signal model and the first-order moment and the second-order moment of the received signal, and solving to obtain a maximum likelihood functional expression about a signal arrival angle as a target function; and reconstructing the US according to the estimated value of the signal angle of arrival, substituting the reconstructed signal space # imgabs0 # into a target function, minimizing the target function by using a particle swarm optimization algorithm, and performing high-resolution subspace estimation.

A set of geometric shapes to be applied by a machine learning model to objects identified in image data is defined. A learning rate of the machine learning model is updated in response to external events. The machine learning model is used to estimate spatial parameters for each of the objects identified in the image data. The spatial parameters are estimated by fitting the objects to the set of geometric shapes. Updates to the spatial parameters are temporally integrated. A spatial estimate of the objects identified in the image data is generated.

A radio-frequency (RF) receiver includes: at least n antennas, where n is an integer greater than two; m processing channels configured to receive and process n RF signals from the at least n antennas, where m is an integer greater than one and less than n; a controller configured to cause a first processing channel of the m processing channels to receive, at different corresponding times, a plurality of RF signals of the n RF signals; an indexing module configured to receive outputs from the m processing channels, and generate one or more representations of the n RF signals based on the outputs; and a spatial estimation module configured to receive the one or more representations, execute a machine learning model based on the one or more representations, and determine, based on an output of the machine learning model, a spatial estimate for an emitter of the n RF signals.

The invention provides a convolution beam space DOA estimation method based on hole filling, so as to solve challenges faced by a traditional convolution beam space method. According to the algorithm, differential common array holes are filled through low-rank matrix recovery, a solving framework is constructed through an alternating direction multiplier method, and a multi-phase component averaging strategy based on a sliding window is designed for the extraction process. Simulation results show that the method solves the problem that the precision of a traditional convolution beam space method is reduced, and only negligible extra time overhead is introduced. Through comparison, the performance of the provided method is better than that of a traditional convolution beam space method and a method based on atom norm minimization and CVX toolbox solving, and the advantages are crucial for realizing real-time and high-precision DOA estimation in a large-scale extended co-prime array scene.

This invention proposes a fusion subspace DOA estimation method based on sparse Bayesian learning. The implementation steps are as follows: first, an overcomplete steering vector dictionary matrix is ​​constructed, and a sparse representation model is built. Then, the signal is reconstructed using the sparse Bayesian learning algorithm. After obtaining the reconstructed signal, a signal covariance matrix is ​​constructed. Finally, eigenvalue decomposition is performed on the covariance matrix to construct a spatial spectral function, and the peak value of this spectral function is searched across the entire angular range. This invention integrates the SBL algorithm and the MUSIC algorithm. The MUSIC algorithm is used to perform subspace decomposition on the reconstructed covariance matrix, extracting the noise subspace and utilizing its orthogonality with the steering vector, thereby improving the angular resolution of the SBL algorithm. This makes this invention applicable to achieving high-resolution DOA estimation under conditions of few or even single snapshots.

The present invention discloses a method and device for predicting harmonics in a distribution network based on a cascaded spatiotemporal graph network, which belongs to the field of power quality monitoring in distribution networks. The method takes the historical harmonic voltage content rate and injected power data of the measurement node as input, and outputs the HRU prediction value of the measurement node through a single-point time series prediction module based on the Transformer architecture; integrates the distribution network topology, power prediction data and the HRU prediction value of the measurement node, and models the harmonic propagation path between nodes through a spatial estimation module based on a graph neural network, estimates the HRU value of the unmeasured node and obtains the THD prediction value based on the indicator fusion calculation. The present invention applies time-space joint modeling to harmonic analysis, and through the network cascade of multifunctional modules, effectively improves the node harmonic THD prediction accuracy in dynamic topology and sparse measurement scenarios, and can be integrated into the power grid energy management system to provide real-time decision support for harmonic governance.

The invention provides a geological parameter spatial data acquisition method and system, and belongs to the technical field of geostatistics, and the method comprises the following steps: obtaining particle fraction test data and eye estimation data of geological parameters of sampling points in a research area; taking the particle test data as a main variable, taking the visual estimation data as an auxiliary variable, and constructing a direct variation function of the main variable, a direct variation function of the auxiliary variable and a cross variation function of the main variable and the auxiliary variable; and constructing a co-Kriging equation set according to the direct variation function of the main variable, the direct variation function of the auxiliary variable and the cross variation function of the main variable and the auxiliary variable, and calculating the value of the geological parameter of each non-sampling point in the research area. According to the method, multi-source test data can be effectively integrated, the data potential is fully mined, and the dependence on a single data source is reduced; meanwhile, cooperative information between the main variable and the auxiliary variable can be fully utilized, and the spatial estimation precision of the geological parameters of the unsaturated zone is remarkably improved.

The invention discloses a subspace estimation adaptive matrix sensing method for compressed sensing imaging. A Gaussian random linear operator A is used as a stimulation mode to be applied to a compressed sensing imaging system so as to carry out p1-time measurement on a scene to be measured, and a corresponding observed quantity y1 is collected. And obtaining first matrix estimation of an unknown matrix X of the scene to be measured through a matrix low-rank recovery algorithm by using y1 and A. And designing a second measurement operator AH based on specific structure information estimated by the first matrix. AH is used as a stimulation mode to be applied to the compressed sensing imaging system for measurement, new observed quantity y2 is collected, and the unknown matrix X is recovered by combining the first matrix estimation, the coefficient matrix estimation and the column space base. According to the method, the total number of times of measurement of the unknown matrix representing the image can be reduced, and the method has great value in invisible light wave band imaging, machine learning and big data, especially in scenes extremely sensitive to measurement cost.