407 results about "Observation matrix" patented technology

According to the invention, the idea of noise and truth value separation is fused into a PLKF framework, and an unmanned aerial vehicle target positioning method (2S-UPLKF) based on two-stage unbiased pseudo-linear Kalman filtering is provided. In the first stage, noise interference can be effectively decoupled during long-distance tracking of the unmanned aerial vehicle based on EKF angle estimation, and a robust initial state is provided for the system. In the second stage, a noise-truth value separation mechanism is constructed, and the correlation between an observation matrix and noise in a pseudo-linear equation is eliminated from the principle level; the method not only inherits the advantage of high efficiency of pseudo-linear filtering calculation, but also realizes active suppression of deviation in the dynamic tracking process through two-stage collaborative optimization. Theoretical analysis and simulation experiments show that compared with other nonlinear filtering algorithms, the method can still effectively suppress error fluctuation under extreme conditions of strong nonlinear observation, large-angle noise, long-distance observation and the like, and has better tracking performance.

The invention discloses a prefabricated cabin welding seam quality detection method based on self-supervised learning, and the method comprises the following steps: collecting a multi-source welding seam image and process parameters, carrying out the synchronous calibration, and constructing a multi-modal observation matrix; the image is input into a DINOv2 model based on a visual Transform, dense features are obtained, and inter-frame registration and sequence reconstruction are completed; performing difference analysis on adjacent frames, extracting spatial changes and marking potential defects; the dense features and the process parameters of the corresponding time periods are fused, joint features are constructed and classified according to rules, and a preliminary result is output; inputting the defect area and the process parameters into an improved DEER model, extracting an influence path and amplitude, and generating a sensitivity score and a confidence coefficient; final judgment is given in combination with the preliminary result, the sensitivity and the confidence coefficient, and feedback is formed according to judgment and process difference. According to the method, high-precision and explainable detection and process optimization of weld defects are realized, and the method is suitable for online / offline quality control and tracing.

The invention discloses a physical and data hybrid driven mechanical arm dynamic model identification method which is characterized by comprising the following steps: S1, acquiring parameters of each axis of a robot, establishing a dynamic model of a mechanical arm, and linearizing the dynamic model according to a minimum inertia principle to obtain a dynamic minimum parameter set and a corresponding regression matrix; s2, an excitation track of the mechanical arm is designed based on the finite Fourier series; s3, the mechanical arm runs the designed excitation track, angles, angular velocities, angular accelerations and torque data of all joints of the mechanical arm in the movement process are collected and filtered, and an observation torque vector and an observation matrix are calculated; s4, performing preliminary estimation on the minimum parameter set by using an iterative reweighted least square method; and S5, aiming at the dynamic characteristics of LuGre friction force in the movement process of the mechanical arm, establishing a mechanical arm dynamic model under physical and data hybrid driving.

The invention provides a BIM-based bridge construction progress monitoring method and system, and the method comprises the steps: obtaining an observation matrix based on BIM model data and sensor monitoring data of a target bridge; inputting the observation matrix into a preset Kalman filter to obtain a corresponding fusion matrix; carrying out Granger causal test by taking the overall progress deviation as a dependent variable and taking the stress ratio, the environmental index, the resource delay rate and the process progress deviation as independent variables to obtain a Granger causal test result; and constructing a corresponding DAG, and obtaining the reason causing the overall progress deviation based on the DAG. According to the scheme, BIM model data and sensor monitoring data are accurately fused, the DAG between the overall progress deviation and each piece of quantitative information is constructed, the quantitative information causing the overall progress deviation can be accurately obtained, and an effective basis is provided for subsequent decision making.

The invention provides a signal blind separation and intelligent reconstruction method and system in a complex scene, and relates to the technical field of signal processing, and the method comprises the steps: receiving an aliasing signal, and converting the aliasing signal into a multi-channel signal observation matrix; performing decomposition in a wavelet domain to obtain a wavelet coefficient, matching the wavelet coefficient with the sparse dictionary, and reconstructing a target signal source after optimization; estimating the number of signal sources based on covariance matrix eigenvalue distribution; mapping a signal source to a graph structure domain, extracting space and time sequence correlation through a mixed graph convolutional network, and obtaining a target separation signal through variational reasoning optimization; and finally, carrying out quality evaluation and post-processing to obtain a final reconstruction signal. According to the invention, the signal separation precision and robustness in a complex scene are improved.

The invention discloses a communication fault diagnosis method and device based on a power system, equipment and a medium. The method comprises the following steps: S1, collecting and outputting multi-source data; s2, preprocessing the multi-source data; s3, performing topological constraint attention mechanism weighting processing on the data matrix; s4, performing dynamic threshold calculation on the fusion feature matrix; s5, positioning the abnormal node list; s6, calculating to obtain a posterior probability; s7, matching the rule base to obtain a standardized disposal instruction; according to the method, the communication traffic is collected through the compressed sensing collection algorithm, the line impedance parameters are integrated into the observation matrix in combination with the topological parameters, key node data are collected preferentially, and the effective data acquisition rate is increased; the accuracy of switch false alarm identification is improved by outputting a time-space unified fusion feature matrix; the computing resources are focused to the abnormal nodes and neighborhoods thereof through the abnormal node mask matrix, so that the positioning precision of the aged equipment is improved, and the judgment omission rate caused by switch aging is prevented from being improved.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

A robot kinetic parameter identification method based on double-layer iteration and friction compensation comprises the following steps: S1, establishing a kinetic model of a robot, and performing linearization processing on the kinetic model to obtain a linearization model represented by an observation matrix and an inertial parameter vector; s2, designing an excitation trajectory for the linearized model by adopting improved Fourier series of a quintic polynomial, and setting constraint conditions of joint positions, speeds and accelerated speeds; s3, the robot is controlled to move according to the excitation track, and joint state data of the robot are collected and subjected to noise reduction processing; and defining the noise-reduced driving torque as a measurement torque. According to the robot kinetic parameter identification method based on double-layer iteration and friction compensation, the physical feasibility of the robot kinetic parameters can be ensured, the friction model is improved to identify the friction parameters, the friction model is fitted by adopting the radial basis function neural network, and the precision of subsequent robot control is ensured.

The invention belongs to the technical field of root cause analysis in intelligent operation and maintenance, and discloses a method for mining fault propagation weight parameters, and the method comprises the following specific steps: S1, supervising data root cause probability initialization, S2, supervising data topology perception labeling, S4, unsupervised data space-time slicing, S5, multi-modal root cause reasoning, S6, parameter increment fusion and S7, online adaptive optimization. Precise modeling and dynamic adaptation are achieved through multi-stage collaborative optimization, and the root cause positioning capacity of a complex system is remarkably improved: a supervised and unsupervised data dual-drive strategy is adopted, a root cause probability baseline is constructed by utilizing work order history, and alarm streams are processed in combination with time-space slice standardization to form a structured knowledge base; the method comprises the following steps: quantifying a transition probability in a CMDB dependency relationship through topology perception annotation and a four-dimensional observation matrix, and constructing a feature matrix containing TP / FP counting to support accurate calculation of a probabilistic graph model;

The invention discloses a space-based directional observation method and device based on orbit precision evaluation. The method comprises the following steps: acquiring fixed star angular distance measurement data information, a Ka-band inter-satellite link observation value and an L-band satellite-ground link observation value of a Beidou satellite; using the information to construct a multi-dimensional observation matrix and carrying out resolving processing to obtain satellite position parameters corresponding to the three space-based directional observation modes; and performing comparison processing on the satellite position parameters by taking an MGEX precise ephemeris as a comparison baseline to obtain an optimal observation mode. Therefore, three space-based directional observation modes are provided, the orbit precision is used as an evaluation criterion, an observation mode system with high adaptability and high practicability is formed, and the orbit determination precision of the Beidou satellite is improved.

The invention belongs to the technical field of GNSS-R SAR (Global Navigation Satellite System-Radar Synthetic Aperture Radar) super-resolution imaging, and discloses a fast distance super-resolution imaging method based on a GNSS-R SAR. According to the method, matrix compression, regularization modeling and a rapid optimization algorithm are creatively combined, and a set of efficient and stable distance super-resolution processing flow is formed. Specifically, after a preliminary imaging result of a GNSS echo signal is obtained and a signal convolution model form of the GNSS echo signal is given, firstly, dimension reduction processing is performed on an observation matrix in a signal convolution model through singular value decomposition, and on the basis of a weighted singular value maintenance strategy processing result, the signal convolution model is reconstructed by using an inverse matrix of a truncated measurement matrix; then, starting from a regularization strategy, introducing an L1 norm constraint to construct a target function by utilizing the sparse characteristic of a target; and finally, solving the target function by adopting a rapid iterative optimization algorithm. According to the method provided by the invention, the range resolution of the GNSS echo data is remarkably improved.

The invention discloses an underwater AUV cluster communication optimization method based on multi-view fusion and sequence reinforcement learning, and the method comprises the steps: firstly, carrying out the construction of a multi-view perception data matrix and the design of a view integrity function, and secondly, generating a high-precision fusion observation matrix; thirdly, performing state estimation on the fused data by using a sequence modeling operator and a progressive memory vector to ensure the time sequence continuity in a high packet loss scene; then local small-scale disturbance and potential diffusion risks thereof are identified, and disturbance information is fed back to the sequence modeling and reinforcement learning module; an optimal action sequence is further generated, and communication link selection and data transmission are optimized through dynamic link scheduling and a redundancy forwarding mechanism; and finally, detecting local accumulative errors and communication channel blocking, and dynamically returning to the multi-view sensing step for recalculation and model updating. According to the invention, through a closed-loop feedback and adaptive optimization mechanism, the cooperative performance and communication reliability of the underwater unmanned cluster under a complex sea condition are improved.

The invention discloses an MEKF attitude estimation method based on a physical information neural network, and the method comprises the steps: dynamically predicting an observation matrix of an MEKF through the dynamic modeling capability and constraint embedding characteristics of the physical information neural network, and extracting sensor sequence features through the long-range dependence capturing capability of a time domain convolutional network; and meanwhile, the physical rule constraint is embedded into network training to ensure the physical rationality of a prediction result. Through data-driven nonlinear modeling and a recursive estimation framework of the MEKF, the limitation that the MEKF is high in dependence on noise covariance and poor in dynamic adaptability is effectively overcome, and the precision and the anti-interference capability of attitude estimation in a complex environment are remarkably improved.

The invention belongs to the technical field of wireless communication networks, and discloses a wireless signal testing method and system for an unmanned vehicle. The method comprises the steps of synchronously collecting electromagnetic signals inside and outside a vehicle, and generating an electromagnetic observation data set; constructing a calibration observation matrix based on a vehicle attitude, position information, a road side reference signal and a communication link state, extracting a candidate interference cluster set and constructing a power curve, and calculating a Pearson's correlation coefficient and Doppler frequency shift to identify an interference type label; determining an interference source position of each interference cluster by adopting a corresponding interference source positioning algorithm according to the interference type label; comprehensively evaluating the communication reliability of the wireless link of the whole vehicle by combining all interference source positions, corresponding labels and power curves and fusing communication link performance indexes and vehicle state information; according to the invention, accurate identification and traceability of wireless link interference of the unmanned vehicle are realized, and the accuracy and real-time performance of communication reliability evaluation are improved.

The invention relates to the technical field of image analysis, in particular to a metal composite material surface defect intelligent detection method based on machine vision, which comprises the following steps: acquiring an original image and converting the original image into an observation matrix; decomposing the matrix into a low-rank matrix and a sparse matrix through a robust principal component analysis algorithm; utilizing a nuclear norm constraint low-rank matrix to establish a background statistical model, and utilizing a norm constraint sparse matrix; positioning a defect area in the sparse matrix and extracting a gray level co-occurrence matrix feature vector; performing connected domain analysis on the sparse matrix, calculating geometrical morphology parameters and evaluating a stress concentration coefficient; and establishing a self-adaptive decision model to carry out fusion judgment so as to judge the physical damage. According to the method, through low-rank sparse matrix decoupling and multi-dimensional statistical geometric feature fusion analysis, accurate stripping of weak defects and quantitative evaluation of physical damage attributes under a complex background are realized.

The invention discloses a million-frame-level industrial vision system and method based on event driving and compressed sensing, and the system is characterized in that an event camera imaging module in the system captures the brightness change of each pixel in a field of view of the event camera imaging module in an asynchronous manner, and generates an event containing a pixel coordinate, a timestamp and change polarity for each change; a compressed sensing coding module constructs sparse image vectors for events in a time window, and the sparse image vectors are projected to low-dimensional observation vectors through an observation matrix phi; the sparse image reconstruction module is used for optimizing an objective function through sparse constraint and total variation regularization; a dynamic ROI compression module controls a compression mask function according to the event density and a gradient threshold. The method can break through the limitation of the traditional frame rate, has the advantages of high precision, high efficiency, low power consumption, strong robustness and the like, and has a wide industrial application prospect.

The invention discloses a method and device for detecting color reducibility of a mobile phone camera and a medium, and relates to the technical field of image quality detection.The method comprises the steps that after geometric alignment is conducted on a color card image set, color block areas are divided, and a cross-light-source color block observation matrix is obtained through color block feature extraction; converting the cross-light-source color block observation matrix into a target color space coordinate, and performing illumination correction to obtain a cross-light-source color coordinate matrix; inputting the cross-light-source color coordinate matrix into a deep learning model, outputting a color block level high-dimensional color embedding matrix, and obtaining a high-dimensional color embedding vector set corresponding to the tested mobile phone camera in combination with an attention weight; calculating a color reducibility error index based on the high-dimensional color embedding vector set in combination with the standard reference library; and performing qualification judgment according to the color reducibility error index to generate a qualification detection conclusion. According to the invention, a stable and reliable technical basis is provided for camera quality detection under a multi-light-source imaging condition.

The invention provides an inertia / satellite / vision adaptive integrated navigation method and system. The method comprises the following steps: carrying out inertia measurement and navigation calculation to obtain inertia data; calculating satellite navigation observed quantity according to the satellite navigation data and the inertial data, and constructing a satellite observation model; calculating visual navigation observed quantity according to the visual navigation data and the inertial data, and constructing a visual observation model; aiming at a satellite and a visual observation model, respectively adopting an innovation-based adaptive covariance estimation method to obtain satellite and visual observation noise covariance updated values; constructing a satellite and visual navigation health degree, and calculating a satellite and visual fusion weight according to the satellite and visual navigation health degree; controlling updating of satellite and visual navigation observed quantity according to the satellite and visual fusion weight; and calculating a weighted equivalent observation matrix and a noise covariance, and carrying out filtering estimation. According to the method, an adaptive noise estimation and sensor health degree evaluation mechanism is introduced, dynamic weighted fusion of multi-sensor data is realized, and the robustness and precision of a navigation system in complex environments such as satellite signal lock losing and visual feature missing are improved.

The invention provides an underground pipe column detection method and system and a medium, and the underground pipe column detection system comprises detection equipment, an excitation signal generator and an underground detection device; the detection equipment is in communication connection with the excitation signal generator and the underground detection device; the underground detection device comprises a transmitting coil and a magnetic sensor array consisting of a plurality of receiving coils; the excitation signal generator is configured to apply transient excitation current to the transmitting coil; the transient excitation current enables the transmitting coil to generate an excitation magnetic field; the excitation magnetic field enables the sleeve layer to generate an induced magnetic field; the magnetic sensor array is configured to collect the magnetic field intensity of the induced magnetic field and generate a magnetic field intensity vector; the detection equipment is configured to perform sparse reconstruction solution on the target vector containing the wall thickness information of the casing layer according to the magnetic field intensity vector and the space observation matrix corresponding to the magnetic sensor array, and through the embodiment of the invention, the limitation of the number of array elements on the detection resolution can be broken through, and the detection precision of the wall thickness of the underground pipe column is improved.

The invention relates to the field of array signal processing, and particularly discloses a DOA joint estimation method based on a quaternion polarization sensitive array. Electromagnetic wave dual polarization components are captured through the orthogonal dipole and the loop antenna, and a quaternion observation matrix is constructed; calculating a quaternion covariance matrix by adopting a sliding window mechanism, and separating a signal / noise subspace by adopting quaternion singular value decomposition; and constructing a spatial spectrum function in combination with a quaternion steering vector, and realizing joint estimation of an azimuth angle and a pitch angle through two-dimensional search. According to the method, the unified characterization capability of quaternions on polarization-airspace information is fully utilized, and the DOA estimation precision and the anti-interference performance of the multi-polarization signal are remarkably improved.

The invention relates to the field of metal heat treatment and industrial automation control, in particular to a normalizing air cooling system of a metal continuous heat treatment furnace, and particularly relates to an ideal field reconstruction module which is used for obtaining steel plate parameters and environmental parameters, introducing a thermodynamic phase change dynamics principle to calculate a phase change latent heat release process and constructing an ideal pure temperature flow field matrix; the fault simulation generation module is used for calling a fault mechanism model to convert an equipment fault into a parameterized interference coefficient matrix, and superposing the parameterized interference coefficient matrix to an ideal matrix to generate a theoretical damaged state matrix; the double-track differential analysis module is used for collecting real-time data to construct an observation matrix, and respectively calculating a first difference between the observation matrix and the ideal matrix and a second difference between the theoretical damaged matrix and the ideal matrix to obtain a real and theoretical difference matrix; the feature coupling judgment module is used for extracting the difference matrix features, calculating the coupling degree, judging that the abnormality belongs to a real fault or environmental noise, and generating an air cooling regulation and control instruction; according to the method, non-linear fluctuation misjudgment is eliminated through phase change latent heat calculation, dual-track differential stripping environment common-mode interference is utilized, and micro equipment abnormity is accurately captured in a noisy environment.

The invention relates to the technical field of gas sensor mixed signal processing, in particular to a gas mixed signal feature extraction and blind source separation method based on rapid independent component analysis, which comprises the following steps of: acquiring mixed gas signals acquired by a multi-channel gas sensor array to form an observation matrix; sequentially performing de-trending, normalization and filtering preprocessing on the observation matrix to obtain a preprocessed observation matrix; and whitening the preprocessed observation matrix through principal component analysis. According to the method, signal preprocessing, rapid independent component analysis, blind source separation and dynamic interference compensation are combined, mixed gas signal feature extraction and blind source separation which do not need to label data and are low in calculation complexity are achieved, and the detection precision, stability and real-time performance of a gas sensor array in a complex environment are effectively improved.

The invention relates to the technical field of dam safety monitoring, and discloses a multi-source remote sensing data deep learning dam slope monitoring method, which comprises the following steps: resampling each mode to a common ground grid, and calculating robust statistics and a time stability agent on grid and block scales; determining a reference image according to the block-level robust score, and adaptively setting a local displacement search range with a high-intensity centroid difference; evaluating the discrete candidate displacement in a grid neighborhood by using a median absolute difference to obtain a local displacement field and a residual error; three types of subitems are constructed based on robust noise, time stability and registration residual errors, and pixel-level reliability weights are adaptively synthesized through logarithmic variance proportions among modes; analyzing and solving local linear mapping in a neighborhood by using a weight-weighted observation matrix, and calculating a weighted residual error according to the local linear mapping; a binary and probability anomaly graph is generated with a robust threshold.

The invention discloses an acoustic temperature field reconstruction algorithm based on sparse matrix and refraction effect consideration, and belongs to the field of acoustic temperature measurement. The algorithm introduces a sparse reconstruction technology and refraction effect optimization to solve the problems of large reconstruction error of an underdetermined equation set, sensitivity to a measurement error and the like caused by the fact that the number of sound rays is smaller than a grid sectioning number in existing acoustic tomography. The method comprises the following specific steps: dividing a temperature measurement area into N grids, constructing a compressed sensing model through an observation matrix A and a sparse base psi, reconstructing a sparse signal by using an improved generalized orthogonal matching algorithm, namely IMOMP, reconstructing a temperature field through a relationship between sound slowness and temperature, and improving precision by using cubic spline interpolation. Meanwhile, the sound wave refraction effect in the non-uniform temperature field is considered, the sound ray path layout is optimized, the algorithm effectively deals with inverse problem instability, the reconstruction error is reduced, the anti-noise capability is improved, and the method is suitable for complex temperature field reconstruction and has application value for industrial temperature measurement.

The invention discloses an intelligent reflector assisted user arrival angle estimation method in a multi-carrier ISAC system. In the method, a plurality of users send pilot signals, and each IRS reflects the user signals to a remote radio access unit (RRH). The RRH uploads the received signal to a centralized baseband processing unit pool (BBU) through a forward link; the BBU performs joint processing on the received signals on the plurality of RRHs, and the observation dimension is increased by constructing the frequency domain snapshot observation matrix of the pilot signals on the plurality of subcarriers, so that the estimation precision of the arrival angle from the user to the IRS path is improved. According to the method, frequency domain snapshot information brought by pilot frequency subcarriers is fully utilized, and the angle estimation resolution and robustness are improved. By adopting the scheme of the invention, high-precision user positioning can be realized in a complex environment.

The invention provides a vehicle passing planning method and system for a no-signal intersection, and relates to the field of traffic route planning, the method is based on a partially observable hidden Markov chain decision-making model, and modeling is carried out on an intersection vehicle passing order and an advancing effect thereof; an observation matrix, a state matrix, a state transition probability model and a target optimal (average shortest vehicle passing time) calculation expression of vehicle passing are constructed, the motion behavior of a single individual passing vehicle at an intersection is modeled, and the interpretability of a planning decision is improved; in addition, an optimal group target motion time sequence chain is searched based on a cost (passing time) calculation method of a Monte Carlo tree model, so that an optimal vehicle real-time passing plan is obtained, and the uncertainty of time consumption of intersection vehicle passing plan calculation is reduced within predictable algorithm time complexity.

The invention discloses an X-ray diffraction spectrum sparse reconstruction method and system based on a dynamic dictionary, and the method comprises the steps: generating an initial dictionary, and carrying out the dynamic optimization and updating of the dictionary, and obtaining a sparse dictionary; selecting an X-ray diffraction scanning angle subset based on the sparse dictionary, constructing an observation matrix, encoding an observation process, and obtaining compressed and sampled X-ray diffraction projection data; and sparse vectors are obtained from the obtained X-ray diffraction projection data after compressed sampling, and a reconstruction spectrum is obtained. And the accuracy of sparse representation is improved. The invention provides a high-throughput XRD (X-Ray Diffraction) material spectrogram analysis method combining dynamic dictionary learning, a compressed sensing theory and deep learning, and aims to improve the analysis efficiency and precision of crystal structure recognition and phase composition in new material research and development.

The invention relates to the technical field of information fusion, in particular to a robust filtering method for solving uncertainty of one-step random observation time delay, which comprises the following steps of: taking multiplicative noise, one-step random time delay, data packet loss, continuous loss observation and uncertain noise variance which are dependent on a system state and a state in an observation matrix into consideration; specifically, based on a maximum and minimum robust estimation principle, a CI fusion Kalman estimator (a predictor, a filter and a smoother) and two FCI fusion Kalman estimators are provided. According to the method, an augmentation method and a virtual noise method are combined, a robustness proving method based on permutation matrixes and a Lyapunov equation is introduced, it is proved that the proposed fusion estimator has robustness by defining different permutation matrixes, and in addition, through an application example of autoregressive moving average signal processing, the robustness of the fusion estimator is improved. It is verified that the robust local and fusion signal estimation problem can be solved through the state estimation method, and therefore solid practical application support is provided for the system modeling result.

The invention discloses a mining area load spectrum anomaly detection method based on standard variable analysis, and belongs to the technical field of hydraulic pump anomaly detection.The mining area load spectrum anomaly detection method comprises the steps that S1, standardized data are obtained through data preprocessing, and a typical working condition data set of a hydraulic pump is constructed in combination with working conditions of an excavator; s2, constructing a historical vector and a future vector, and constructing a historical observation matrix and a future observation matrix according to the historical vector and the future vector; s3, constructing a Hankel matrix according to the autocorrelation matrix and the cross-correlation matrix, decomposing the Hankel matrix and determining a model order; s4, mapping the original data to a standard variable space and a residual space, and respectively evaluating the total variable quantity of the standard variable in the state space and the sum of squares of change errors in the residual space; and S5, determining an evaluation threshold value, and if the evaluation threshold value exceeds the control line, judging that the hydraulic pump operates abnormally. The method is based on standard variable analysis, adopts the pressure pulsation data of the hydraulic pump to perform anomaly detection, is sensitive to the internal running state of the pump, is not easily influenced by the external environment, and can perform early warning on faults of the hydraulic pump.

The present invention belongs to the technical field of GNSS-R SAR super-resolution imaging, and discloses a fast range super-resolution imaging method based on GNSS-R SAR. The present invention innovatively combines matrix compression, regularization modeling and fast optimization algorithm to form a set of efficient and stable range super-resolution processing procedures. Specifically, after obtaining the preliminary imaging results of the GNSS echo signal and giving its signal convolution model form, the observation matrix in the signal convolution model is first reduced in dimension by singular value decomposition, and on the basis of the processing results of the weighted singular value preservation strategy, the signal convolution model is reconstructed using the inverse matrix of the truncated measurement matrix; then, starting from the regularization strategy, the L1 norm constraint is introduced to construct the objective function using the sparse characteristics of the target; finally, a fast iterative optimization algorithm is used to solve the objective function. The method of the present invention significantly improves the range resolution of GNSS echo data.