221 results about "Observation matrix" patented technology

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 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.

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 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 a broadband spectrum compression detection method based on digital twinning and signal noise, and relates to the technical field of mobile communication, and the method comprises the following steps: S1, inputting historical spectrum data, geographic position information and electromagnetic environment parameters into a digital twinning model based on an improved FEDform model, and obtaining a digital twinning model; outputting predicted power spectral density and signal support set probability distribution; s2, generating an optimized observation matrix and a digital compression measurement vector; s3, outputting a whitening measurement value; s4, innovative components are extracted; s5, outputting reconstructed broadband spectrum data; s6, outputting a binary support set matrix containing frequency point occupancy state information; and S7, outputting the updated digital twin model. According to the invention, the limitations of dependence on high-speed sampling hardware, poor anti-noise capability and early warning delay in a traditional broadband spectrum detection method are overcome, and an efficient and accurate solution is provided.

The invention belongs to the technical field of defect detection, and particularly relates to an artificial quartz stone plate surface defect detection system and method to solve the technical problem that normal textures are easily misjudged as defects in an existing detection method. The detection system comprises an acquisition module which combines color components and anisotropy degrees into multi-dimensional features and arranges the multi-dimensional features of all pixels into an observation matrix; the construction module is used for decomposing the observation matrix into linear superposition of a global background low-rank matrix, a local texture low-rank matrix and a defect sparse matrix; and the extraction module is used for calculating the area of each candidate defect connected domain, the length-width ratio of a bounding rectangle, the average chromatic aberration and the average anisotropy degree. According to the invention, the screening standard can be obtained according to the texture characteristics of the candidate region, pseudo defects caused by noise or background residue can be reduced, and high accuracy and high reliability of the detection result are ensured.

The invention discloses a computer information processing method and system, and relates to the technical field of computers, and the method comprises the following steps: establishing a unified time baseline and a bit stream phase reference field, constructing a residual bit observation matrix, and locking the boundary of a state machine according to the residual bit observation matrix; rare symbol sequence features are extracted based on the locked state machine boundary, a symbol sequence fingerprint set is constructed, a reversible auto-encoder is utilized to invert symbol sequence triggering conditions, and risk mapping is generated; and constructing a buffer micro-residual spectrum according to the risk mapping, generating a bit-level topological link, calibrating a cross-fragment pollution starting point in the topological link, and outputting a positioning index. According to the method, bit stream anomaly identification and dynamic repair based on the time base line and the phase reference are realized, residual observation, symbol fingerprints, topological positioning, error correction rearrangement and time reversal reset are combined, a closed-loop information processing flow is constructed, the data processing stability and fault-tolerant capability are improved, and the method is suitable for high-density real-time data and complex transmission scenes.

The invention discloses an improved SVMD combined SOBI bridge dynamic strain signal adaptive noise reduction method, which comprises the steps of obtaining an original signal of bridge dynamic strain through a strain sensor, improving an SVMD algorithm by taking kurtosis and a decomposition error as stopping criteria, and further decomposing the original signal; classifying the intrinsic mode function components through a K-means clustering algorithm so as to reconstruct a noise component signal and an initial noise reduction signal; constructing a three-channel hybrid observation matrix containing an original signal, a noise component signal and an initial noise reduction signal; performing blind source separation on the hybrid observation matrix by using an SOBI algorithm, and estimating to obtain a separated source signal and a hybrid matrix; noise components are identified based on kurtosis clustering, signals are reconstructed after frequency points are set to be zero, and high-precision noise reduction is achieved. According to the method, a deep collaborative architecture is constructed by introducing an adaptive decomposition mechanism, intelligent clustering recognition and accurate reconstruction are adopted, and efficient suppression of noise and high-fidelity retention of signal features are realized.

The invention discloses an energy flow interface 3D interaction method, and relates to the technical field of vehicle electronics, and the method comprises the steps: collecting a real-time operation message of each subsystem of a vehicle through a whole vehicle communication network, and analyzing the real-time operation message into an engineering value with a physical quantity line; determining the running state of the vehicle based on the engineering numerical value, and dynamically generating an energy flow direction topology table defining a source node and a target node; calculating a three-dimensional energy flux vector containing a direction vector and a modulus length scalar according to the node anchor point coordinates; calculating a target observation matrix of the virtual camera based on the three-dimensional energy flux vector, so that the sight line direction and the space direction of the vector meet a preset geometric projection relationship; and driving a graphic rendering pipeline, and generating an interactive interface comprising the three-dimensional part model and the dynamic particle flow according to the engineering numerical value and the target observation matrix. According to the method, self-adaptive adjustment of a visual angle is realized by constructing an energy flux vector, the optimal visibility of an energy transmission path is ensured, and real-time visualization of physical data driving is realized.

The invention relates to a fusion method of satellite navigation system double-antenna attitude information and an inertial navigation system, a program, equipment and a storage medium, and belongs to the technical field of inertial base integrated navigation systems. In order to solve the problems that the attitude precision is indirectly improved by mainly depending on speed and position information and dual-antenna attitude information is not fully utilized in the conventional inertial navigation integrated method, attitude measurement of an inertial navigation system is constructed by directly utilizing elevation angle and azimuth angle information output by a dual-antenna satellite navigation system; and direct fusion of attitude information is realized in combination with a Kalman filter. The method comprises the steps of filter initialization, inertial navigation solution, state transition matrix calculation, measurement vector construction, observation matrix calculation, filter time and measurement updating, state correction and the like. The attitude estimation precision of the inertial navigation system is obviously improved, the course angle precision is obviously improved, the pitch and roll angle precision is improved, and the method is simple, efficient and suitable for the navigation fields of unmanned aerial vehicles, automatic driving and the like.

The invention belongs to the technical field of navigation, and particularly discloses a combined navigation method for improving an AEKF algorithm under a GNSS incomplete condition, which comprises the following steps of: executing the following operations in each filtering period of the AEKF algorithm: determining state priori estimation in the current filtering period based on a state transition matrix and a state vector in the previous filtering period, the state vector is constructed based on the position, the speed and the clock error of the receiver; based on the state noise variance matrix and the error covariance matrix in the previous filtering period, determining error covariance priori estimation in the current filtering period; correcting an observation matrix and a measurement noise variance matrix based on the dynamic weight coefficients of the GNSS and the Loran C; and based on the corrected observation matrix and the corrected measurement noise variance matrix, obtaining the Kalman gain in the current filtering period, and updating the prior estimation. According to the invention, the overall performance of the integrated navigation system under the condition that the GNSS is incomplete can be improved.

The application provides a uniform plane array FDA-MIMO three-dimensional target positioning method and system based on complete decoupling atomic norm minimization. The method converts the problem into a semi-positive programming solution by constructing a low-dimensional observation matrix and using complete decoupling atomic norm, and realizes joint estimation of distance, pitch angle and azimuth angle through Vandermonde decomposition. Compared with high complexity algorithms (VANM, etc.), the application significantly reduces the calculation amount by decomposing the Toeplitz matrix, and improves the single snapshot precision by using the multi-slice constraint mechanism. Compared with low complexity algorithms (3D-DANM), the application introduces an auxiliary variable matrix to restore the distance-angle coupling structure, avoids the performance loss caused by excessive relaxation, and significantly improves the estimation accuracy and multi-target resolution capability while ensuring the calculation efficiency.

The application provides a channel estimation method of a visible light communication system, and relates to the technical field of channel estimation. The method comprises the following steps: obtaining a pilot observation matrix of a receiving end; calculating an initial channel estimation by using an LMMSE estimator; splicing the initial channel estimation and a noisy state of a diffusion time step to obtain a joint input tensor; inputting the joint input tensor into a pre-trained LMRDNet, recovering a predicted residual matrix from the noisy state through a reverse diffusion process; and calculating refined channel state information according to the initial channel estimation and the predicted residual matrix. Through the fusion of an LMMSE statistical priori and a customized lightweight diffusion model, the application realizes high-precision channel estimation under the condition of limited pilots and low signal-to-noise ratio.

This invention discloses a method and system for inverting mechanical parameters and intelligently analyzing the stability of tunnel surrounding rock, relating to the field of intelligent construction technology for tunnels and underground engineering. The method includes: collecting tunnel monitoring and measurement data and constructing a displacement field observation matrix; constructing a physical information neural network embedded with the geotechnical mechanics control equations to invert the mechanical parameters and stress field of the surrounding rock; automatically calling the finite element kernel through a programming interface and calculating the safety factor of the surrounding rock using the strength reduction method; using evidence theory to fuse multi-source analysis results and output the stability level; and driving online model updates and support optimization through prediction-monitoring comparison verification. This invention achieves the integration of parameter inversion, automated numerical simulation, and closed-loop verification, improving the accuracy, efficiency, and intelligence level of surrounding rock stability analysis.

The application relates to the fields of signal processing, image processing and communication engineering, and particularly relates to a real-time sparse signal reconstruction method based on an analog circuit, which comprises the following steps: acquiring an observation signal and an observation matrix; based on the observation signal and the observation matrix, an analog circuit is constructed; the numerical value of the observation signal is converted into a corresponding electric signal and applied to the input end of the analog circuit; the analog circuit is powered on and operated to a steady state, the steady-state voltage values of a plurality of predetermined nodes in the analog circuit are measured, and the measured steady-state voltage values are output as the reconstruction values of the sparse signal. The application adopts a technical path of a full-analog computing system architecture, and aims at a sparse signal reconstruction problem, converts a key mathematical model (specifically, an equation group represented by KKT conditions) in a solving process of the problem into a circuit physical structure, and outputs an optimal solution through a steady-state response of the circuit in one step.

The application discloses a kind of distributed array high-precision time synchronization method based on compressed sensing (CS) and secondary least square (QLS), belong to wireless communication and radar signal processing technical field.For under the restriction hardware bandwidth and strong multipath environment, traditional cross-correlation method is limited by Rayleigh resolution and sampling grid, there is the problem of serious peak aliasing, insufficient synchronization accuracy, the application proposes CS-QLS joint decoupling strategy.The method constructs frequency domain observation matrix and high-density time delay over-complete dictionary by transmitting sparse multi-tone probe signal, reconstructs sparse channel impulse response using CS, separates multipath aliasing and determines direct wave coarse synchronization boundary;Further combined with main path and adjacent grid point amplitude information, QLS is used to fit continuous domain peak position, and grid mismatch error is corrected.The method breaks through the limitation of physical bandwidth and sampling grid, and realizes nanosecond-level high-precision time synchronization in complex non-line-of-sight environment with very low computational complexity.

The invention discloses an improved near-far field data transformation method and device based on a transfer matrix, and belongs to the field of RCS testing and data processing. The method comprises the following steps: according to the maximum size of a to-be-measured target on a rotation plane and the distance from the center of a measurement antenna to the scattering center of the target, calculating to obtain a synthetic aperture angle required by transformation calculation; performing one-dimensional gridding division processing on the area where the target to be tested is located along the transverse direction, and respectively establishing a near-field test observation matrix and a far-field test observation matrix of the target to be tested in the grid area obtained by division; according to the near-field test observation matrix and the far-field test observation matrix, calculating to obtain a transfer matrix for converting near-field scattering characteristic data into far-field scattering characteristic data; and updating the transfer matrix according to a preset test frequency and a conversion processing center angle, and calculating to obtain all far field scattering characteristic data of the to-be-tested target. The method can effectively reduce the scale of the observation matrix and improve the calculation efficiency.

The invention relates to the technical field of image processing, in particular to a silicon carbide tray defect detection method and system based on image recognition, and the method comprises the steps: obtaining a gray image of a silicon carbide tray to construct an observation matrix, and initializing a low-rank matrix, a sparse matrix and a Lagrange multiplier matrix for representing error accumulation; iterative optimization is executed; and repeating the iteration steps until a termination condition is met, and taking the final sparse matrix as a defect detection result. Through the technical scheme of the invention, the problems of false detection and missing detection caused by misjudging the background texture as the defect can be reduced, and the accuracy and robustness of the defect detection result of the silicon carbide tray are improved.

The application relates to the technical field of industrial information and data processing, and discloses a grey model prediction system for building structure deformation analysis, which comprises the following modules: a data preprocessing module for filtering industrial time series data sequences and storing the sequences in a first state register; an adaptive background value calculation module for determining an adaptive background value calibration weight according to a change gradient difference value, limiting the weight within 0.2-0.8 to generate a mean value background value sequence close to the weight; and a residual dynamic correction prediction module for constructing an observation matrix to solve a coefficient to output a trend prediction sequence, and calculating a deviation compensation value according to a prediction residual sequence to output deformation trend prediction data. The application can follow signal changes through adaptive weights, eliminate calculation lag and overshoot deviation in data mutation intervals, avoid divergence risks in parameter identification, and realize fast prediction response and deterministic threshold closed-loop arbitration on a low-power microprocessor.

The invention discloses an intelligent electric energy meter error data processing method based on edge calculation, and belongs to the technical field of error data processing methods. The method comprises the following steps: S1, calculating a mean value and a standard deviation in a total electric energy increment sequence and a sliding window, defining a first-order autoregressive lag correction item, setting an adaptive threshold, defining a state machine system state, and finding out a computer increment sequence of a user in an inactive period by constructing an inactive period criterion; s2, storing the screened effective data increment, calculating the electric energy increment of each sub-meter, defining a continuous zero increment detection function, and constructing an observation matrix X and an observation vector Y by setting an ammeter failure detection window and parameters and collecting effective rearranged data points; s3, calculating a characteristic value and a condition number through an improved genetic optimization algorithm framework, and carrying out iteration to obtain optimal and different individuals; and S4, determining an optimal regularization parameter through an L curve method, and calculating an error coefficient of each electric energy meter through improved Tikhonov regularization.

The present application relates to the technical field of sonar imaging, and particularly relates to a multi-beam sonar compressed imaging method and system based on depth unfolding, which comprises the following steps: a multi-beam sonar system is used to emit sound waves to the seabed, and a receiving device is used to receive reflected echo signals to obtain multi-channel digital time sequence echo signals; an observation matrix is constructed to compress and sample the multi-channel digital time sequence echo signals to obtain a low-dimensional measurement matrix; the low-dimensional measurement matrix is input into a trained depth unfolding beamforming network to output a reconstructed beamforming spectrum, and a sonar sounding image is generated according to the beamforming spectrum. The present application can greatly improve the imaging speed and support real-time applications such as underwater obstacle avoidance and online monitoring.

The invention relates to the technical field of intelligent sensing systems, and discloses a method and a device for detecting the bearing capacity of a pavement engineering construction material, and the method comprises the steps: synchronously collecting a mixed signal and a pure noise signal through a main measurement sensor and a reference noise sensor, constructing a multi-dimensional observation matrix, and carrying out the centralization and whitening preprocessing, thereby obtaining the bearing capacity of the pavement engineering construction material. And blind source separation is carried out by adopting an independent component analysis algorithm based on maximized non-Gaussian property, a target bearing capacity response signal with a super Gaussian characteristic is identified according to a kurtosis value of each source signal component, and then a dynamic deformation modulus is calculated. The system comprises a sensor array module, a synchronous acquisition module, a data processing module based on FPGA hardware acceleration and a result output storage module. According to the invention, strong coherent vibration noise can be effectively suppressed, signal details are completely reserved, and high-precision and real-time bearing capacity detection is realized.

The application provides a vehicle suspension state parameter estimation method and device, and relates to the technical field of intelligent control of vehicle suspension, and the method comprises the steps of: constructing an interactive state observer based on at least two physical characteristic parameters of a vehicle suspension; determining prior state prediction parameters corresponding to each sub-observer based on a previous model probability and a transition probability matrix corresponding to each sub-observer; updating the prior state prediction parameters of each sub-observer based on current observation data corresponding to each sub-observer to obtain posterior state estimation parameters of each sub-observer; adaptively determining a current forgetting factor corresponding to each sub-observer based on the current observation data, an observation matrix and the prior state prediction parameters of each sub-observer; and fusing the current model probability, the posterior state estimation parameters and the current forgetting factor corresponding to all sub-observers to obtain a current overall state parameter estimation value corresponding to the interactive state observer. The application can improve the estimation accuracy of the suspension state parameters.

The present application relates to the technical field of Internet of Things, in particular to a city intelligent guardrail real-time monitoring method based on Internet of Things, comprising the following steps: collecting original vibration signals of the guardrail through an Internet of Things sensing terminal deployed on a city guardrail node, projecting and reducing dimension of the original vibration signals by using a preset compressive sensing observation matrix to obtain a compressed measurement value data packet; in the present application, time-frequency feature extraction is performed on the reconstructed time-domain vibration waveform, the impact event type is identified according to the characteristic data, and the rapid response and processing of traffic accidents are ensured; in the case that no impact occurs and the waveform presents a free decay trend, the decay tail wave data segment is intercepted, the guardrail structure fastening integrity evaluation index is calculated, and comparison with the safety benchmark threshold value is performed, so that the loosening state of the connecting piece can be quantitatively determined, and external impact monitoring and internal structure health condition diagnosis can be simultaneously realized by using a single vibration source without additional deployment of stress sensors.