306 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 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 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 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.

The invention provides a wind turbine power prediction method based on multi-modal feature fusion, and relates to the technical field of wind turbine power prediction.The method comprises the steps that multiple types of sensors are used for obtaining multi-source sensor data, and a standardized time sequence data set is generated; establishing a first observation matrix based on the standardized time sequence data set, and constructing a weight sensing fractional order adaptive genetic algorithm to optimize the first observation matrix to obtain a second observation matrix; performing feature fusion and noise reduction on the second observation matrix by applying extended Kalman filtering and combining the working state and physical modeling of the wind turbine; constructing a bidirectional long-short-term memory network, and performing power time sequence modeling based on the multi-dimensional noise reduction feature vector to obtain an output power predicted value; and feeding back an error between an output power prediction value and a true value to a weight sensing fractional order adaptive genetic algorithm to carry out parameter iterative optimization to obtain a third observation matrix, and obtaining a power prediction value of the wind turbine based on the third observation matrix.

The invention discloses a forest fire automatic identification and risk assessment method based on X-band and S-band dual-band joint detection, and the method comprises the steps: deploying an X-band radar and an S-band radar to construct a cooperative detection network, and forming a detection region covering short and long ranges through space-time registration; a Kalman filtering algorithm is adopted to fuse dual-source radar data, and the method comprises the following steps: defining a four-dimensional state vector containing a plane position coordinate and a velocity component; directly mapping the position component of the state vector to a radar reflectivity plane observation value by using a position mapping type observation matrix; fire point identification based on a random forest model comprises the following steps: inputting feature fusion space reflectivity parameters and time dynamic features; calculating the heat release rate per unit area through the linear combination of the dual-polarization radar reflectivity and the differential reflectivity; calculating fire intensity by combining the fire point area; and coupling the fire intensity with the wind field spreading prediction, and generating graded early warning information oriented to power grid safety.

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 relates to the technical field of a two-wheeled vehicle system, in particular to a two-wheeled vehicle state estimation method and device based on a Pinochio dynamics library and a Lie group, and the method comprises the steps: carrying out the modeling of a two-wheeled vehicle based on the Lie group and the Lie algebra, and constructing a motion state; the method comprises the following steps: acquiring a plurality of kinetic parameter matrixes of a two-wheeled vehicle in real time by adopting a Pinocochio kinetic library, and constructing a discrete kinetic equation; determining a predicted motion state and a predicted covariance matrix of the two-wheeled vehicle at the next moment; and according to the observation data, carrying out posteriori updating on the predicted motion state and the predicted covariance matrix by adopting a Kalman gain and an observation matrix, and determining a posteriori motion state and a posteriori covariance matrix of the two-wheeled vehicle at the next moment. According to the method provided by the embodiment of the invention, the kinetic parameter matrix is obtained by adopting the Pinocochio kinetic library, so that the manual workload is greatly avoided, the complexity degree in the modeling process is reduced, and the modeling efficiency is remarkably improved while the estimation precision is improved.

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 indoor visible light cooperative positioning method based on multi-dimensional sampling vector compressed sensing, and relates to visible light positioning. A plurality of indoor light source LEDs are utilized to issue visible light signals to a target, a target end receives and extracts arrival time difference information among different LEDs through a PD, and a sparse positioning model is constructed. A plurality of groups of reference anchor points are introduced, time difference of arrival measurement vectors are collected from different dimensions, and a multi-dimensional sampling observation matrix with higher representation capability on sparse distribution of a target space is constructed. Redundant or repeated time difference of arrival measurement is eliminated in the model, calculation redundancy is reduced, and robustness of sparse recovery is enhanced. And solving a predicted value of the PD position coordinate through norm minimization, and calculating an error between the predicted value and a true value. Under the condition that accurate clock synchronization is not needed, multi-target cooperative positioning is effectively achieved, and centimeter-level positioning accuracy can be kept under various noise and deviation conditions. The method is suitable for typical indoor positioning scenes such as industrial Internet of Things, intelligent manufacturing and robot navigation.

The invention relates to the technical field of sonar imaging, and discloses a marine organism distribution detection system and method based on sonar imaging, and the method comprises the steps: setting a plurality of depth layers, and collecting an acoustic echo signal of each depth layer; marine organism block sparse observation is carried out based on the three-dimensional acoustic echo signals, an observation matrix is obtained, and an effective biological signal block set is calculated; calculating space coordinates and reflection intensity of each biological target according to the effective biological signal block set, obtaining biological positioning data of each depth layer, and obtaining a three-dimensional acoustic image of each depth layer through three-dimensional imaging; the biological density index, the Shannon-Vaniner diversity index and the vertical migration speed of the scattering coefficient in each ecological layer are calculated, the biological detection result of each depth layer is output, data collection is carried out on the multiple depth layers, the detection reliability in the low-signal-to-noise-ratio marine environment is improved, and the detection efficiency is improved. And marine organism populations and distribution characteristics of different depth layers can be accurately identified.

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.