37 results about "Cross correlation matrix" patented technology

The invention discloses a robust radio frequency fingerprint identification method based on Barlow Twins domain adaptation. Aiming at the problems of training and deployment environment distribution offset and cross-domain identification performance reduction caused by wireless channel multipath and time-varying characteristics in the prior art, the invention provides a domain adaptation framework combined with feature decoupling. The method comprises the following steps: firstly, converting a received time domain signal into a short-time Fourier transform spectrogram to reserve a time-frequency structure; then, a double-end convolutional encoder is constructed, bottom layer features are extracted through a shared backbone network, and device fingerprints and channel features are obtained in an identity branch and a channel branch respectively; in order to realize thorough decoupling of the two types of features, Barlow Twins-based independence regularization is introduced, and the identity features and the channel features are statistically orthogonal by minimizing a cross-correlation matrix of the identity features and the channel features, so that purer fingerprint features are obtained, channel interference is effectively eliminated, and generalization and recognition precision of the model under an unknown channel are remarkably improved.

The embodiment of the invention relates to the technical field of computer security authentication, in particular to a security authentication method and system based on a secure computer. The method comprises the following steps: carrying out sensing array deployment and data acquisition on a secure computer to obtain calibrated time-space synchronization data; performing temperature and current fusion processing on the calibrated time-space synchronization data to obtain a thermoelectric coupling characteristic spectrum; carrying out point location time sequence correlation analysis on the thermoelectric coupling characteristic spectrum to obtain a point location cross-correlation matrix; performing nonlinear feature extraction on the point location cross-correlation matrix to obtain a nonlinear dynamic feature set; performing dynamic behavior analysis on the nonlinear dynamic feature set to obtain a security calculation behavior dynamic phase spectrum; and performing phase difference calculation on the dynamic phase spectrum of the security calculation behavior to obtain a phase difference vector. According to the method, the early detection capability of threats which are difficult to reproduce and are in a novel hardware level is improved through real-time monitoring and anomaly recognition of computer microcosmic physical characteristics.

The invention provides a model acceleration method based on a face recognition model application. The method comprises the following steps: sampling a face picture; two different data enhancement modes are adopted for the face image, and a first distortion image and a second distortion image are obtained respectively; performing feature extraction on the first distortion graph through an original model, and outputting a first embedded vector; performing feature extraction on the second distortion graph through a simplified model, and outputting a second embedded vector; measuring a cross-correlation matrix of the first embedded vector and the second embedded vector by adopting a contrastive learning thought; and constraining the distance between the cross-correlation matrix and the unit matrix through the relative loss function, repeating until the output of the relative loss function meets the preset stability condition, and replacing the original model with the simplified model. According to the method, knowledge distillation from a heavy-weight network to a lightweight-weight network is completed, and the function of replacing a heavy-weight original model by a lightweight-weight simplified model is realized, so that hardware facilities of embedded equipment are adapted, and the real-time detection efficiency of face recognition is improved under limited computing resources.

The invention discloses an image semantic segmentation method and system based on cross-modal hierarchical knowledge distillation, and the method comprises the steps: inputting an image to a multi-modal teacher model and a multi-modal student model, obtaining teacher features and student features of each stage, and outputting a prediction map; the teacher features of all the stages are fused, common modal features are enhanced, fusion features corresponding to all the stages are obtained, and the fusion features and the student features are divided into high-level features and low-level features according to different stages; the low-level teacher fusion features and the low-level student features are converted into frequency domain graphs respectively, and high-frequency parts are taken out to transmit detail knowledge; respectively calculating a cross-correlation matrix and an autocorrelation matrix by using the high-level teacher fusion features and the high-level student features, and transmitting structural knowledge; and aligning the teacher output prediction map and the student output prediction map, and transmitting response knowledge. According to the invention, the multi-modal features are fused in a mode of modal enhancement, the modal gap between the teacher fusion features and the student features is reduced, and the single-modal student model segmentation precision is improved.

The invention belongs to the technical field of geological exploration, and particularly discloses a symmetrical excitation anti-explosion geological exploration system and method, and the method comprises the steps: receiving array multi-channel signals at two sides, and carrying out the time alignment correction of all channel signals, so as to eliminate the time delay; a multi-channel cross-correlation matrix between the left array and the right array is constructed based on the aligned signals, and matrix elements are obtained through integral cross-correlation operation of array channel signals on the two sides; performing low-rank decomposition on the cross-correlation matrix, and extracting the first k main components as public noise modes; carrying out inversion calculation on the contribution of each noise mode in each acquisition channel, and reconstructing a channel noise component; and subtracting the reconstructed noise component from the original signal, and outputting a purified geological reflection signal. The method does not need special hardware, can accurately suppress the homologous low-frequency noise of the tunnel, improves the signal-to-noise ratio of the signal and the geological imaging resolution, and is suitable for geological detection of closed spaces such as the tunnel.

An isometric self-supervised line-of-sight estimation method and device, the method establishes a camera coordinate system and a world coordinate system, calibrates the camera, and estimates the extrinsic parameters of the world coordinate system; adjust the camera posture, rotate and scale the camera coordinate system; a virtual camera coordinate system is established, and after the head of the human body moves in three directions for two-dimensional projection, a homography matrix corresponding to the image transformation is obtained; the transformed picture is sent into the encoder for feature extraction, and after the features are extracted, they are projected to the vector space through the multilayer perceptron network, and the Barlow Twins loss is calculated through the hidden space feature mutual correlation matrix; the obtained encoder is connected with the multilayer perceptron network, and in the line-of-sight gaze data environment, the 3D line-of-sight direction is fitted by using transfer learning. The application can efficiently utilize unlabeled data for 3D line-of-sight estimation; it is invariant to appearance transformation and isometric to geometric transformation.

The application belongs to the technical field of transformer partial discharge identification, and discloses a transformer partial discharge intelligent identification method based on deep learning, which constructs an electroacoustic thermal three-mode acquisition architecture, captures 1ns level discharge pulses through a high-frequency current sensor, avoids sound field superposition interference by reasonably arranging an ultrasonic sensor on the oil tank wall, focuses on the easy discharge area to collect thermal signals through an infrared thermal imager, and synchronously obtains working condition parameters such as load rate and oil temperature; subsequently, the effective signal segments are retained through a multi-modal signal cross-correlation matrix, and the interference signals with large deviations are removed, then different networks are used to extract electroacoustic thermal characteristics, the coupling characteristics are calculated by combining the physical constraint layer weight and the cross-field interaction layer, and the key characteristics are retained; the feature overlap of air gap and surface discharge is effectively reduced, the influence of high-frequency interference and signal distortion is reduced, the partial discharge identification precision is improved, the samples are expanded and the fault and normal sample ratio is reasonable through time stretching and additive noise processing.

The invention provides a channel estimation method for a minimum mean square error, which comprises the following steps of: performing Fourier transform on a received baseband signal to obtain OFDM (Orthogonal Frequency Division Multiplexing) frequency domain data, and obtaining frequency domain data of all symbols on a receiving side through solution resource mapping; the method comprises the following steps: processing by utilizing frequency domain data of a pilot symbol at a receiving side and a priori known pilot symbol sequence to obtain channel responses of SNR (Signal to Noise Ratio) estimation and LS (Least Square) channel estimation; processing according to the channel response of the LS channel estimation to obtain root-mean-square delay expansion; calculating a data-pilot frequency cross-correlation matrix and a pilot frequency self-correlation matrix according to root mean square time delay expansion, and calculating to obtain an MMSE filtering channel estimation coefficient according to the data-pilot frequency cross-correlation matrix and the pilot frequency self-correlation matrix; mMSE filtering is carried out according to the MMSE filtering channel estimation coefficient, and finally a final channel estimation value is obtained. According to the method, channel response is accurately estimated while operation is simplified, so that reliable communication and low operation delay are ensured.

The invention discloses a three-dimensional reconstruction method, system, equipment and medium for an orbit CT image, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining an unmarked orbit CT image; the method comprises the following steps: carrying out intra-block pixel replacement on a plurality of sub-regions randomly selected from an unlabeled orbit CT image through nonlinear intensity transformation based on a Bezier curve on the basis of two-way contrast learning and random horizontal overturning and rotation, and generating two distorted views; performing coding analysis of the same weight on the two distorted views, generating coding feature representations of fixed dimensions for two analysis results by using a projection network, and generating a cross-correlation matrix through the two coding feature representations to serve as coding features for fusing different levels; and decoding the fused coding features to generate a segmentation result of the orbit CT image, generating three-dimensional body data through the segmentation result, reconstructing an orbit surface grid, and calculating an orbit volume to obtain a three-dimensional reconstructed orbit model. According to the method, the morphological characteristics of the orbits can be automatically learned from a large number of eye CT images without manual annotation, and accurate orbital segmentation and reconstruction are realized.

The invention provides a clean spectrum analysis method based on an undersampled harmonic signal. The clean spectrum analysis method comprises the steps of acquiring a multi-channel tip timing signal, processing the multi-channel tip timing signal, calculating a cross-correlation matrix, initializing and iterating. According to the method disclosed by the invention, the aliasing effect is inhibited according to the coherence of the frequency components in the frequency spectrum, and the aliasing components are deleted from the actually measured frequency spectrum forming output by iteratively deleting the frequency spectrum part coherent with the peak frequency. The technical scheme of the invention can be widely applied to the technical field of blade vibration detection.

This invention relates to a method for diagnosing composite faults in rolling bearings based on fast eigenmode decomposition (EMD) in the field of rolling bearing fault diagnosis technology. The method includes the following steps: S1, Signal initialization and period estimation: Segmented narrowband filtering of the rolling bearing vibration acceleration signal is performed using a Hanning window; S2, Adaptive filter design: The original signal is filtered using the MOMEDA method to obtain multiple modal signals; S3, Correlation kurtosis calculation and mode selection: The correlation kurtosis value of each filtered signal in each frequency band is calculated, and a cross-correlation matrix between modes is constructed based on cross-correlation theory, retaining a preset number of optimal modes; S4, Fault diagnosis analysis: Hilbert envelope demodulation is performed on the retained optimal mode signals. This invention solves the mode breakage problem that may occur in existing technologies while saving significant computational costs and improving computational efficiency, demonstrating good practicality and engineering application value.

This invention discloses a method for detecting quality faults in a flotation process based on distributed dynamic graph embedding. Step 1: Collect process variables such as concentration, pH value, ore fineness, froth layer thickness, and maximum allowable density during the flotation production process as input variables, and the concentrate grade of the flotation process as the output quality indicator. Step 2: Based on the mutual information method, establish a cross-correlation matrix MI between process variables and quality variables, calculate the average threshold, and select key variables. Step 3: Based on the production process and field experience of the flotation process, divide the key variables into Q quality-related sub-blocks and B quality-unrelated sub-blocks. Step 4: Use the key variable selection and sub-block decomposition results to establish a distributed dynamic graph model to achieve quality-related fault detection. Step 5: Use a Bayesian fusion network to fuse the monitoring results for decision-making. Step 6: Determine whether the fault is quality-related based on the monitoring results.

The invention discloses a multi-dimensional feature self-supervision fan fault diagnosis system and method based on time sequence weight self-adaption, and belongs to the field of wind power generation. Comprising a data acquisition module, a working condition alignment module, a time sequence weight adaptive convolution module and a fan multi-component fault feature cross-correlation calculation module. The method comprises the following steps: collecting real-time and historical data of multiple fans in the same wind field, carrying out working condition alignment and time synchronization processing, and constructing a time sequence weight adaptive convolution model in combination with multi-fan transverse data and single-fan longitudinal historical data; according to the model, time-frequency local features are extracted through Gabor transformation, broadband domain features are obtained through spectral convolution, and multi-dimensional time-frequency joint features are formed. And further calculating a cross-correlation matrix of multi-fan and multi-component fault features, analyzing feature differences between healthy and fault units, dynamically updating model parameters and a loss function according to the feature differences, and realizing self-learning and continuous optimization of the model. The method does not need a large amount of manual annotation, and is high in diagnosis precision, strong in generalization ability and good in adaptability.

The application provides a surface electromyogram signal decomposition method based on fast gradient kernel compensation, comprising the following steps: mathematically modeling a multi-channel surface electromyogram signal; extending the constructed multi-channel surface electromyogram signal model; constructing a convolution mixed model of the corresponding surface electromyogram signal based on the extended electromyogram signal, a firing time sequence and a noise sequence; constructing a cross-correlation matrix of the surface electromyogram signal and initializing an activity index; initializing a cross-correlation vector based on the activity index; obtaining a gradient function and a learning rate; introducing an exponential weighted moving average model, offline calculating and iteratively updating the cross-correlation vector; extracting the electromyogram signal in a sliding window and expanding it, calculating the cross-correlation matrix after expansion, and estimating the firing time sequence of each motor unit; recalculating the firing time sequence of each motor unit until all signal decompositions are completed. The application can improve the surface electromyogram signal decomposition efficiency and is suitable for real-time decomposition process.

The application discloses a feature subset selection method and device and a storage medium. The method comprises the following steps: acquiring high-dimensional feature data; obtaining a feature importance list by using a random forest; obtaining a feature cross-correlation matrix by using Spearman correlation; obtaining a fitting degree of each feature according to a target GRU model; performing feature relationship type fusion on the feature importance list, the feature cross-correlation matrix and the fitting degree of each feature to obtain an overall evaluation value of each feature; continuously performing feature sorting on the overall evaluation value of each feature, removing the feature with the lowest evaluation value and determining whether the number of the remaining features is less than a required number of features until the number of the remaining features is not greater than the required number of features; and obtaining a feature subset. The feature subset selection method of the application uses Spearman correlation analysis, a random forest and a GRU for joint evaluation, completes diversified feature evaluation, realizes feature data dimension reduction, improves the feature evaluation processing flow and reduces the time complexity and the space complexity for subsequent work.

This invention discloses an image semantic segmentation method and system based on cross-modal hierarchical knowledge distillation. The method includes inputting an image into a multimodal teacher model and a student model to obtain teacher features and student features at each stage and an output prediction map; fusing teacher features at each stage and enhancing common modal features to obtain corresponding fused features for each stage; dividing the fused features and student features into high-level features and low-level features according to different stages; converting low-level teacher fused features and low-level student features into frequency domain maps respectively, extracting high-frequency components to convey detailed knowledge; calculating cross-correlation matrices and autocorrelation matrices using high-level teacher fused features and high-level student features respectively, and conveying structural knowledge; aligning teacher output prediction maps and student output prediction maps, and conveying response knowledge. This invention fuses multimodal features through modal enhancement, reducing the modal gap between teacher fused features and student features, and improving the segmentation accuracy of the single-modal student model.

This invention discloses a method for tracing the source of generated images based on spatial-frequency cross-domain second-order statistics. This method extracts spatial domain residual anomalous features and frequency domain anomalous features from the generated image, and constructs a cross-domain second-order statistical coupling relationship between them, forming a cross-domain statistical fingerprint for source tracing. The cross-domain statistical fingerprint matrix is ​​obtained by calculating the cross-domain cross-correlation matrix, and then normalized and compressed to obtain a cross-domain second-order statistical feature representation. Based on the similarity matching or distance measurement results between this feature representation and the statistical fingerprint templates of each source category, the source category or confidence score of the generated image is output. This invention can explicitly characterize the coupling relationship between spatial anomalies and frequency anomalies, and still has strong source discrimination ability and good interpretability and generalization performance even when the generation models are highly similar.

The invention provides a three-dimensional dereverberation beam forming method and system based on a cross-correlation matrix, and relates to the technical field of sound source recognition, and the method comprises the steps: collecting sound pressure data through a spherical microphone array; performing fast Fourier transform on the sound pressure data to obtain corresponding frequency domain data; calculating a cross-spectrum matrix corresponding to the spherical array according to the frequency domain data; eliminating reflected sound components contained in the cross-spectrum matrix to obtain a filtered cross-spectrum matrix; and drawing a sound source identification cloud picture based on the filtered cross spectrum matrix. According to the three-dimensional dereverberation beam forming method based on the cross-correlation matrix, the technical problem of insufficient sound source recognition precision of a spherical array in a complex sound field environment in the related technology is solved.

This invention relates to the field of computer security authentication technology, and more particularly to a security authentication method and system based on a secure computer. The method includes the following steps: deploying a sensor array and acquiring data from the secure computer to obtain calibrated spatiotemporal synchronization data; performing temperature and current fusion processing on the calibrated spatiotemporal synchronization data to obtain a thermoelectric coupling feature spectrum; performing point-to-point temporal correlation analysis on the thermoelectric coupling feature spectrum to obtain a point-to-point cross-correlation matrix; extracting nonlinear features from the point-to-point cross-correlation matrix to obtain a nonlinear dynamic feature set; performing dynamic behavior analysis on the nonlinear dynamic feature set to obtain a dynamic phase spectrum of secure computing behavior; and calculating the phase difference of the dynamic phase spectrum of secure computing behavior to obtain a phase difference vector. This invention improves the early detection capability of difficult-to-reproduce and novel hardware-level threats through real-time monitoring and anomaly identification of the computer's microscopic physical characteristics.

The invention provides a signal transmission method, device and equipment based on non-orthogonal overlapping chirp, and relates to the technical field of signal transmission. The method comprises the following steps: at a sending end, carrying out time delay on a basic chirp signal based on a preset signal interval to obtain a group of delayed chirp signals, and obtaining a subcarrier signal according to the delayed chirp signals; performing amplitude modulation on the subcarrier signal by using the to-be-transmitted signal to obtain a sending signal; at a receiving end, receiving signals corresponding to the sending signals are obtained, the receiving signals are demodulated based on the inverse matrix of the equivalent channel matrix, demodulation signals corresponding to the signals to be transmitted are obtained, and the equivalent channel matrix is obtained based on the cross-correlation matrix of the subcarrier signals. According to the method, the spectrum efficiency can be improved, the anti-interference capability is considered, and meanwhile, the signal demodulation complexity is relatively low.

The invention discloses a channel map assisted large-scale MIMO low-orbit satellite communication pilot frequency scheduling method and system, and the method comprises the steps: constructing a Fisher information matrix based on a channel parameter vector based on a receiving signal model of a large-scale MIMO low-orbit satellite communication system under a pilot frequency multiplexing condition; spatial features are extracted based on the Fisher information matrix, a feature matrix is constructed, the feature matrix is a symmetric matrix and used for quantifying parameter estimation correlation between different user terminals, and element values in the feature matrix are determined according to an autocorrelation matrix and a cross-correlation matrix in the Fisher information matrix; performing dimension reduction on the feature matrix to generate a channel map; and based on the channel map, pilot frequency scheduling is carried out by using relative spatial positions among users in the channel map so as to suppress pilot frequency interference caused by pilot frequency multiplexing. According to the method, the channel estimation precision and the spectrum efficiency of the low-orbit satellite large-scale MIMO system in the pilot frequency multiplexing scene can be effectively improved.

This invention discloses a method for identifying and suppressing multiple synchronous satellite navigation spoofing interferences, comprising: constructing a denoised cross-correlation matrix using received data vectors and reference data vectors; calculating the spatial power spectrum using an observation function based on the constructed denoised cross-correlation matrix; adaptively classifying the estimated power and direction of arrival of the observed pairs of signals using a maximum entropy threshold based on the spatial power spectrum to identify the power and direction of arrival of the incident navigation signal; reconstructing the signal autocorrelation matrix and the interference plus noise covariance matrix based on the identification results; calculating the optimal vector based on the signal autocorrelation matrix and the interference plus noise covariance matrix; and then suppressing the spatial spoofing interference based on the optimal vector. This method identifies and suppresses multiple synchronous satellite navigation spoofing interferences.

The application proposes a large-scale MIMO-OFDM channel acquisition method based on multiple groups of adjustable phase shift pilots. In the application, users are divided into multiple groups, each group uses the same basic pilot sequence to generate multiple adjustable phase shift pilots, and different groups use different basic pilot matrices; the autocorrelation matrix of the basic pilot matrix is a unit matrix, and the sequence after FFT transformation of the diagonal element of the cross-correlation matrix of different basic pilot matrices is sparse, wherein each non-zero complex element has the same argument. In the channel acquisition method, each user terminal sends known and scheduled multiple groups of adjustable phase shift pilot signals to the base station, the base station pre-processes the received signals, and then completes channel estimation. The method can greatly improve the spectral efficiency and channel information acquisition accuracy of the large-scale MIMO-OFDM system, especially in the communication scene with a large number of users and strong mobility, and has superior performance.

The application discloses a water supply pipeline leakage noise monitoring and early warning method and system based on correlation principle, a plurality of sound monitoring devices are arranged at a plurality of preset nodes of a water supply network, each sound monitoring device comprises a data acquisition module, a wireless communication module and a power supply module; noise signals of the water supply pipeline are collected in real time through the data acquisition module, and the collected noise signals are transmitted to a data processing center through the wireless communication module; the data processing center pre-processes noise signals collected by each sound monitoring device, and obtains pre-processed noise signals and time-frequency images; based on the correlation principle, the cross-correlation coefficients between the pre-processed noise signals collected by different sound monitoring devices are calculated, and a cross-correlation matrix is obtained; according to a pre-set leakage noise template, the cross-correlation matrix is matched to determine whether there is leakage and estimate the leakage position. The application improves the detection efficiency, implements real-time monitoring on the water supply network, and effectively improves the accuracy of leakage judgment.

Disclosed in the present invention is a method for estimating a direction of arrival of a sub-array partition type L-shaped coprime array based on fourth-order sampling covariance tensor denoising. The implementation steps are as follows: constructing an L-shaped coprime array partitioned with linear sub-arrays; modeling a receiving signal of the L-shaped coprime array and deriving a second-order cross-correlation matrix thereof; deriving a fourth-order covariance tensor based on the cross-correlation matrix; realizing fourth-order sampling covariance tensor denoising based on kernel tensor thresholding; deriving a fourth-order virtual domain signal based on denoised sampling covariance tensor; constructing a denoised structured virtual domain tensor; obtaining a direction of arrival estimation result by decomposing the structured virtual domain tensor. The present invention makes full use of the statistical distribution characteristics of the high-order tensor of the constructed sub-array partition type L-shaped coprime array, realizes high-precision two-dimensional direction of arrival estimation through denoised virtual domain tensor signal processing, and can be used for target positioning.

The application discloses a wireless communication system scene adaptive multipath time delay alignment method and device, the method uses SISO channel detection system to measure channel data, adopts a rectangular or RRC shaping filter to generate a CAZAC oversampling sequence as a template function, calculates the cross-correlation matrix of the IQ signal and the template function, calculates the noise threshold based on the median power and false alarm rate, executes dynamic threshold noise filtering, generates the cross-correlation matrix after noise reduction, and completes the preliminary estimation of the time domain impulse response; the SAGE algorithm is used to extract the multipath time delay and complex gain from the cross-correlation matrix; the channel impulse response is drawn to draw a power delay profile (PDP); and the PDP is aligned in combination with the geographic position information of the transceiver during actual measurement. The application solves the defects of traditional alignment in a bad scene, improves the statistical modeling accuracy, and can effectively align the multipath time delay through experiment verification, thereby providing reliable support for channel modeling.

The invention relates to the technical field of agricultural monitoring, and discloses a quantum dot luminescence characteristic-based plant disease and insect pest intelligent early warning system, which comprises a multi-element composite quantum dot sensing array unit, a coherent time sequence coding excitation unit, a single photon time correlation counting detection unit and a central processing and alarm decision unit. In the working process, the sensing array unit and plant stress metabolites act and generate differentiated fluorescence lifetime responses; the detection unit obtains a time-resolved data stream of the response; the central processing and alarm decision-making unit processes the data and constructs a multi-modal composite fingerprint formed by fusing a real-time life vector, a real-time cross-correlation matrix and a collaborative response dynamic curve; and inputting the composite fingerprint into a preset classification model for matching operation so as to output accurate prediction of the health state of the plant. According to the invention, by constructing the multi-dimensional composite fingerprints, accurate and intelligent early warning of the plant stress event in the early stage is realized, and the accuracy and specificity of diagnosis are improved.

The invention relates to a rotating long baseline interferometer high-resolution angle parameter estimation method and device and a medium, and the method comprises the steps: obtaining two array element receiving signals of a rotating interferometer, carrying out the discretization of the two array element receiving signals of the rotating interferometer, and carrying out the cross-correlation processing to obtain a cross-correlation signal; an initial cross-correlation matrix and an initialized power spectrum are obtained based on the cross-correlation signal, iterative updating is performed on the power spectrum based on the cross-correlation matrix, the cross-correlation matrix is the initial cross-correlation matrix and the power spectrum is the initial power spectrum during initial updating, and high-resolution power spectrum estimation is obtained; power spectrum density is obtained based on high-resolution power spectrum estimation; obtaining special display points based on the power spectrum density, and obtaining coordinate information of each special display point; a pitch angle and an azimuth angle of the corresponding radiation source are calculated and estimated based on the coordinate information. Compared with the prior art, the method has the advantage that distortion of target source number and angle parameter estimation is effectively avoided.

The application discloses a kind of multi-parameter lognormal random field automation generation method based on finite element grid, the method first reads finite element grid file, parses the unit topological relation of target analysis area and automatically extracts the geometric center point coordinate of entity unit;Obtain multidimensional uniform distribution sample, and convert into independent initial standard normal random matrix;Respectively construct the cross correlation matrix of each parameter space autocorrelation matrix and parameter;Multi-parameter lognormal random field is generated using nonlinear mapping equation;Finally, the random field data is mapped according to unit number and verified.Outputs.Through the combination of original finite element grid analysis and multiple correlation decoupling calculation, the method can efficiently and accurately generate the random field subject to complex correlation structure, effectively eliminate the error caused by traditional independent grid interpolation mapping, provide a fully automated pre-processing tool for large-scale random finite element simulation and engineering reliability assessment.

The application relates to the technical field of agricultural monitoring, and discloses a plant disease and pest intelligent early warning system based on quantum dot light emitting characteristics, which comprises a multivariate composite quantum dot sensing array unit, a coherent time sequence coding excitation unit, a single photon time correlation counting detection unit and a central processing and alarm decision unit. In work, the sensing array unit is acted on by plant stress metabolites and generates a differentiated fluorescent lifetime response; the detection unit acquires time-resolved data flow of the response; the central processing and alarm decision unit processes data and constructs a multimodal composite fingerprint fused by a real-time lifetime vector, a real-time cross-correlation matrix and a synergistic response dynamic curve; the composite fingerprint is input to a preset classification model for matching operation to output accurate prediction of the plant health state. Through construction of a multidimensional composite fingerprint, early and accurate intelligent early warning of plant stress events is realized, and the accuracy and specificity of diagnosis are improved.