4782results about "Using wave/particle radiation means" patented technology

The invention provides a color steel plate coating flatness evaluation method and system based on artificial intelligence. According to the method, the three-dimensional point cloud data is generated by collecting the interference fringe image, and the surface fluctuation characteristics are quantified; capturing a multi-dimensional vibration spectrum of the transmission roller shaft, generating a servo motor compensation control signal, driving a multispectral scanning head to perform reverse displacement compensation, and generating real-time compensation data; inputting the surface topography features in the three-dimensional point cloud and the real-time compensation data into a lightweight convolutional neural network, and outputting fusion features; and dynamically classifying and identifying surface defects and uneven areas based on the fusion result, adjusting a classification threshold in combination with the speed of the production line, outputting a flatness evaluation result, and synchronizing the flatness evaluation result to a speed regulation system of the production line to realize closed-loop optimization. According to the method, laser interference, vibration compensation and lightweight AI technologies are fused, dynamic high-precision evaluation of the surface flatness of the color steel plate of the high-speed production line is achieved, and the problems of defect misjudgment and measurement distortion caused by vibration interference are solved.

The invention provides a slope deformation monitoring method and device based on multi-source data fusion, and relates to the technical field of dam slope safety monitoring. The method comprises the steps that a multi-source monitoring system composed of a global satellite signal receiver, a measuring robot and an interference synthetic aperture radar is arranged in a dam slope area; according to the collected slope deformation disturbance parameters, performing monitoring area selection division on a dam slope area; determining a data fusion weight corresponding to each data fusion observation area; collecting multi-source monitoring data in each data fusion observation area; and based on the determined data fusion weight, performing weighted fusion processing on the multi-source monitoring data in the selected area to generate a slope deformation monitoring result. According to the technical scheme, fusion of multi-source monitoring data is realized through region division, the reliability, comprehensiveness and environment adaptability of slope deformation monitoring data are improved, and the stability, accuracy and precision of a slope deformation monitoring result are improved.

The invention discloses a geotechnical engineering stability early warning system and method combining side slope deformation monitoring and numerical simulation, and relates to the technical field of geotechnical engineering safety monitoring and disaster early warning. Side slope deformation monitoring and numerical simulation are combined, and multi-source real-time monitoring data of side slope deformation and numerical simulation are fused; constructing a dynamically updated slope deformation evolution analysis system; performing dynamic partition identification on the slope deformation evolution process according to the multi-source real-time monitoring data, and identifying a slope deformation threshold value and abnormal evolution characteristics of rock and soil; and setting an automatic triggering grading early warning signal of a geotechnical engineering early warning index, and completing slope risk dynamic assessment and early warning output of the rock and soil. According to the invention, the automatic triggering of the grading early warning signal is realized, the response speed, the judgment accuracy and the risk assessment scientificity of the geotechnical engineering early warning system are obviously improved, and the intelligent, dynamic and systematic technical support is provided for the safety prevention and control of the side slope.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

A method for monitoring an infrastructure of interest, the method comprising: providing a distributed acoustic sensor, an analysis module, and optical fibre cable adjacent to the infrastructure wherein the optical fibre cable is divided into one or more sections, and the distributed acoustic sensor generates DAS sensor data from the one or more sections; providing a processing module comprising one or more processors and a memory, wherein the memory comprises at least two or more processing algorithms, wherein the one or more processors are configured to execute the processing algorithms with the DAS sensor data as input, wherein each processing algorithm is configured to provide an output state for each section of the optical fibre cable adjacent to the infrastructure of interest.

The invention belongs to the technical field of microwave measurement, and discloses an outer wall hollowing microwave reflection detection method based on multi-modal fusion, which comprises the following steps: carrying out multi-modal scanning on a building outer wall to be detected, obtaining visible light image data and infrared temperature distribution data of the outer wall surface, and carrying out space registration and coordinate mapping; a unified multi-modal fusion data set is formed; performing anomaly screening on the multi-modal fusion data set, and identifying a thermal anomaly region by analyzing infrared temperature distribution data; detecting a bump or crack area in combination with texture and morphology anomaly features of the visible light image data; performing information fusion on the thermal anomaly region and the bump or crack region, and extracting candidate detection regions of suspected hollowing; high-precision recognition and quantitative evaluation of the outer wall hollowing are achieved, and the precision and stability of outer wall hollowing detection are improved.

The invention relates to the technical field of three-dimensional observation, in particular to a geological deformation three-dimensional observation system and method, and the method comprises the steps: multi-dimensional data collection: synchronously obtaining data, and collecting ground temperature gradient field data and underground water flow velocity field data of a monitoring region; data space-time calibration processing: performing space-time reference unification processing on the acquired data to generate a multi-source displacement data set after space-time calibration; cold door parameter extraction: generating an environment coupling parameter matrix based on ground temperature gradient field data and underground water flow velocity field data in multi-dimensional data acquisition; modeling a three-dimensional deformation field: generating an initial three-dimensional deformation field model; dynamic model correction: performing iterative correction on the initial three-dimensional deformation field model to generate an optimized three-dimensional deformation field model; and visual early warning output: generating an early warning signal with a risk level identifier. According to the method, a multi-source heterogeneous data fusion system is constructed, and a cold geological parameter dynamic compensation mechanism is combined, so that high-precision three-dimensional deformation field modeling and early warning are realized.

A sensor as a service ecosystem is disclosed herein. In use, the system includes functionality for receiving first sensor data at a sensor as a service platform, where the first sensor data corresponds to a first capability level of a first sensor. The system also receives a selection of a sensor upgrade for the first sensor, and configures enhanced sensor capabilities for the sensor upgrade based on the selection. Further, the system sends a sensor update with the enhanced sensor capability from the sensor-as-a-service platform to the first sensor. Finally, the system receives second sensor data from the first sensor at the sensor as a service platform, where the second sensor data corresponds to a second capability level of the first sensor.

The invention provides a bridge structure monitoring method and device based on a microwave deformation radar, and relates to the technical field of bridge structure monitoring, and the method comprises the steps: obtaining the multi-point three-dimensional displacement data of a bridge structure through the microwave deformation radar, carrying out the thermal expansion pseudo displacement compensation and multi-point space smoothing through combining with temperature information, and obtaining a displacement field after environment correction; secondly, extracting a vertical component and separating the vertical component into a static deformation component and a dynamic vibration component by adopting variational mode decomposition; further analyzing and identifying a decoupling region through a time window coherence coefficient, performing recursive quantitative analysis, bispectrum analysis and energy distribution entropy calculation on a dynamic signal of the region, and constructing a high-order damage sensitive feature set; and finally, the dynamic characteristics and the static curvature change are fused to form a comprehensive degradation degree index, the deviation degree is judged according to working condition classification and the mahalanobis distance, and multi-dimensional and cross-working-condition degradation identification and risk early warning of the bridge structure are achieved.

The invention discloses a self-adaptive InSAR-GNSS high-precision three-dimensional deformation resolving method with regularization introduced twice, and the method comprises the steps: obtaining InSAR deformation data and GNSS three-dimensional deformation data of a to-be-monitored region, and building an InSAR error correction model considering spatial correlation based on the InSAR deformation data and the GNSS three-dimensional deformation data; performing system error correction on the InSAR deformation data by using the corrected error correction model to obtain preliminary correction data; grouping the preliminary correction data according to data precision characteristics and determining an initial weight; iteratively calculating until unit weight variances of each group of observation data are approximately equal to obtain a final weight; and obtaining a high-precision earth surface three-dimensional deformation rate based on the final weight. According to the method, Tikhonov regularization is introduced twice, the weight is dynamically adjusted in combination with the IGGIII equivalent weight function, and various error influences are effectively reduced.

The invention provides a vortex aircraft detection signal processing method and system based on optical fiber sensing. The method comprises the following steps: firstly, acquiring an original micro-vibration signal; secondly, performing environment self-adaptive cooperative processing on the original micro-vibration signal to generate a self-adaptive micro-vibration signal; converting the generated self-adaptive micro-vibration signal into a spatio-temporal evolution sequence based on eddy current physical characteristics; then analyzing the spatio-temporal evolution sequence to output a type discrimination result of the vortex aircraft; and finally, generating a customized detection report based on the type discrimination result. According to the technical scheme provided by the invention, the detection and identification capability of a low-altitude low-speed small target in a complex environment is improved, and reliable detection and identification of the vortex aircraft in the complex environment are also realized.

The invention discloses a tower foundation landslide monitoring method, system and device and a medium. The method comprises the steps that InSAR data and GNSS data are fused to conduct wide-area deformation monitoring, a quadratic fitting model is established to conduct error dynamic correction on InSAR observation values, and a potential landslide hidden danger area is recognized; the method comprises the following steps: performing multi-dimensional cooperative monitoring on a potential landslide hidden danger area through an unmanned aerial vehicle carrying a sensor, and fusing multi-source data by utilizing edge computing equipment to generate a landslide risk thermodynamic diagram; an active waveguide acoustic emission system is adopted to capture landslide deep deformation signals, and early warning is achieved through ringing counting and acoustic emissivity parameters. According to the invention, the InSAR and GNSS technologies are fused, and the Kalman filtering algorithm is combined, so that the precision of surface deformation monitoring is improved; the InSAR observation value is constrained and corrected through the GNSS observation value, so that the deformation monitoring precision is improved; more accurate data support is provided for landslide monitoring through the unmanned aerial vehicle carrying sensor equipment; powerful support is provided for early warning of landslide disasters through an active waveguide acoustic emission technology.

The invention provides an optical fiber vibration auscultation method and system for water supply network leakage, and relates to the technical field of water supply network monitoring, and the method comprises the steps: carrying out the synchronous sampling of a dual-channel optical fiber vibration signal through an FPGA main clock, so as to obtain a pipeline vibration signal and an environment noise signal which are aligned in time; performing multi-dimensional feature fusion on the pipeline vibration signal and the environmental noise signal to obtain a pipeline leakage feature vector; and inputting the pipeline leakage feature vector into the CNN-LSTM hybrid model to obtain pipeline leakage information output after the CNN-LSTM hybrid model performs feature analysis and prediction processing on the pipeline leakage feature vector. The technical problem that the recognition accuracy of water supply pipeline leakage in the complex environment is low in the prior art is solved, and the technical effect of improving the recognition accuracy of water supply pipeline leakage in the complex environment is achieved.

The invention discloses a full-automatic cable vibration high-precision measurement method based on a machine vision technology, and relates to the technical field of structural vibration measurement. The method comprises the following steps: installing and calibrating equipment, and ensuring that a cable plane coincides with an imaging plane; high-frame-rate video acquisition is carried out, and comparison verification is carried out in combination with an accelerometer; screening effective pixels based on the pixel intensity time sequence variance, and constructing a pixel intensity space-time matrix by using the time sequence of the pixels; sVD decomposition is adopted to extract an accumulated energy ratio gt; 90% of the main modes are reconstructed, and real signals are revealed; a plurality of inhaul cables are automatically positioned and coded through gradient features and Hough transform; sub-pixel-level vertical displacement is calculated by applying an optical flow method, and median filtering is combined to resist noise; fourier transform is carried out to obtain a spectrogram, and a fundamental frequency is identified; and calculating cable force based on a string pulling theorem. According to the method, remote, full-field and multi-target synchronous monitoring is realized, the displacement measurement precision reaches a sub-pixel level, and a high-precision and full-automatic solution is provided for health monitoring of structures such as bridges.

The invention discloses a millimeter wave SAR icing thickness inversion algorithm based on deep learning, and aims to solve the problems of insufficient icing monitoring precision and poor real-time performance under complex meteorological conditions. According to the algorithm, high-quality input is provided for deep learning modeling through data preprocessing including slope distance correction, polarization denoising, back scattering characteristic extraction and data enhancement. A multi-branch deep neural network structure is adopted for feature extraction, and in combination with a Swin Transformer V2 trunk, a Gated MLP module, cross-modal attention fusion and physical consistency constraint, the adaptability of the model to millimeter wave SAR data and multi-modal input is improved. Meteorological parameters and line operation data are integrated through multi-modal data fusion, and model robustness is enhanced. Finally, an icing thickness prediction value is output through real-time reasoning, intelligent early warning is triggered in combination with confidence analysis, end-to-end real-time response is achieved, the intelligent level of safe operation of a power grid is remarkably improved, and the method has wide application value.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

The invention discloses a roadway deformation monitoring device based on a millimeter wave radar. The roadway deformation monitoring device comprises a main shell, the millimeter wave radar, a laser sensor, a PCBA (Printed Circuit Board Assembly) and a bubble level, and an integrated multi-modal data fusion analysis module, a dynamic measurement range intelligent adjustment module, a real-time deformation prediction and control module and a self-calibration and fault diagnosis module. The multi-modal fusion system fuses radar and laser data through a Kalman filtering algorithm, and full-scale high-precision monitoring is realized by using median filtering, Gaussian fitting preprocessing and dynamic weight distribution; the dynamic range measurement module intelligently switches measurement and frequency sweeping ranges based on a decision tree algorithm according to mining progress, geological conditions and distance fluctuation coefficients; the real-time prediction module adopts an ARIMA model to predict the deformation in advance, and automatically adjusts the scanning angle and the acquisition frequency; the self-calibration module uses a high-precision reflecting plate to correct errors everyday; the system supports 5G communication and remote control, has the advantages of high precision, self-adaption and high reliability, and is suitable for long-term deformation monitoring of mine roadways.

The invention provides a millimeter wave radar-based bridge deformation monitoring method and system, and the method comprises the steps: obtaining a transmitting signal and an echo signal of a radar, and carrying out the preprocessing of the signals, and obtaining an intermediate frequency signal; processing the intermediate frequency signal by adopting matched filtering and a zoomFFT algorithm to obtain target distance information; processing the intermediate frequency signal by adopting a MUSIC algorithm to obtain target angle information; and obtaining a target distance angle unit by combining the target distance information and the target angle information, and obtaining target deformation information by adopting phase difference and phase unwrapping. According to the invention, the interference of noise and background can be avoided, the deformation displacement of the target area can be accurately calculated, and the real-time monitoring and analysis of the deformation state of the bridge can be realized.

The invention relates to the technical field of landslide displacement data prediction, in particular to a GCN-MHA-GRU-based landslide displacement prediction method for a black square platform. Acquiring synthetic aperture radar monitoring data of the black square table area; extracting landslide surface deformation data of the black square table area from the synthetic aperture radar monitoring data; decomposing the landslide earth surface deformation data by adopting a three-dimensional deformation decomposition method according to a satellite orbit; establishing a landslide prediction model based on a graph neural network, a multi-head attention mechanism and a gating circulation unit; inputting the decomposed landslide surface deformation data into the landslide prediction model for training; and carrying out landslide displacement prediction on the black square table area according to the trained landslide prediction model. According to the method, an efficient, accurate and robust landslide displacement prediction model is constructed, and powerful technical support can be provided for scientific prevention and control of geological disasters.

The invention relates to the technical field of structural health monitoring and geotechnical engineering, and discloses a breakwater foundation settlement direction real-time monitoring and early warning system and method, and the system comprises a data collection module which is used for obtaining the measurement data of at least one sensor disposed on a breakwater and a foundation of the breakwater in real time; the storage module is used for storing a preset physics-based reduced-order model, and the reduced-order model represents the overall deformation rule of the breakwater and the foundation system through a group of core deformation modes; and the processing module is connected with the data acquisition module and the storage module, and the processing module comprises a data assimilation module which is used for fusing the measurement data and the order reduction model in real time. By arranging the deformation field reconstruction module, the problem that only discrete point information can be obtained through traditional monitoring is effectively solved, real-time and high-precision expansion from sparse measurement to a continuous overall deformation field is achieved, and a more visual and reliable basis is provided for comprehensively evaluating the safety state of a structure.

The invention relates to the technical field of distributed optical fiber acoustic sensors, in particular to a distributed optical fiber sensor vibration identification method and system, and the method comprises the steps: obtaining original data, and carrying out the preprocessing; inputting the preprocessed vibration time sequence into a Mamba model, and extracting a time domain feature vector reflecting a time evolution rule; after obtaining a time-frequency spectrogram of the preprocessed vibration time sequence, extracting to obtain a frequency domain feature vector representing different frequency components and a global association relationship thereof; inputting the time domain and frequency domain feature vectors into a time-frequency feature fusion model, and outputting a vibration event recognition result of the distributed optical fiber acoustic sensor; training the time-frequency feature fusion model, and storing the optimal model weight for prediction; and performing prediction by using the optimal model, storing a prediction result, performing verification, calculating an evaluation index, and finally performing result comparison. The invention aims to solve the problems of insufficient performance and the like of a distributed optical fiber acoustic sensing technology in the prior art.

The invention relates to the technical field of optical fiber sensing, and discloses an optical fiber sensing signal adaptive demodulation analysis method for inhibiting common-mode interference, which comprises the following steps of: synchronously acquiring an original signal of a main sensing optical fiber channel and a common-mode interference signal of a reference optical fiber channel, and performing time-frequency conjoint analysis on the signal of the reference optical fiber channel to obtain a common-mode interference signal; s2, extracting an interference spectrum component containing temperature drift and light source fluctuation characteristics, constructing a hybrid adaptive filter bank which comprises a recursive least square (RLS) filter and a variable step size least mean square (LMS) filter which are connected in parallel, and dynamically adjusting a forgetting factor of the RLS filter and a step size factor of the LMS filter based on the interference spectrum component extracted in the step S2, according to the optical fiber sensing signal adaptive demodulation analysis method for suppressing common-mode interference, a parameter adjustment strategy is continuously optimized, a pure vibration signal with an output signal-to-noise ratio is improved, and the effect of accurately suppressing common-mode interference in real time is achieved.

The invention relates to the technical field of road repair state monitoring, in particular to a road repair state monitoring method and system based on edge calculation. The method comprises the following steps: acquiring planning information of a repaired road, deploying 3D laser scanning vibration meters at a plurality of repairing points, collecting pavement vibration signals in different time periods, and performing disordered distribution time domain feature analysis to obtain a vibration signal disordered distribution time domain graph; then, structure weakening behavior simulation is carried out based on the graph, periodic structure weakening data is quantified, and a pavement water damage cumulative gradient is obtained through water damage cumulative gradient estimation; and finally, in combination with the periodic structure weakening data and the water damage cumulative gradient, carrying out service life evaluation on the restoration state, and sending an evaluation result to the terminal. According to the invention, the road repair state monitoring technology is optimized, so that the road repair state monitoring technology is more perfect.

The invention belongs to the field of cable eccentricity detection, and particularly relates to a cable eccentricity detection method and system based on X rays. The method comprises the following steps: calculating a gradient magnitude diagram and a gradient direction diagram through an X-ray image of a cable, screening a point with the local maximum gradient magnitude and low neighborhood divergence as a contour starting point, performing contour tracking to generate a contour point set, and selecting a next contour point based on a tangential prediction direction; performing ellipse fitting on the contour point set to obtain a geometric center, long and short axis parameters and a root-mean-square error of a fitting ellipse, dividing the fitting ellipse into an inner candidate ellipse and an outer candidate ellipse according to a long axis, and matching the inner candidate ellipse meeting the condition for each outer candidate ellipse; and screening out an outer candidate ellipse and an inner candidate ellipse which meet conditions from the candidate pairs, and calculating the eccentricity of the cable to be measured based on geometric center coordinates of the outer candidate ellipse and the inner candidate ellipse. According to the invention, the accuracy and reliability of cable eccentricity measurement results can be improved.

The invention relates to the technical field of dam safety monitoring, in particular to a method and system for improving dam GNSS deformation monitoring precision through base station and observation station combined network adjustment. The method comprises the following steps: arranging base stations and monitoring points to form a GNSS monitoring network; gNSS original observation data are collected in real time and preprocessed; constructing a joint network adjustment model, and taking base station coordinates as constraints and monitoring point coordinates as to-be-estimated parameters; performing baseline resolving based on the double-difference carrier phase observed quantity to obtain a baseline vector and a covariance matrix thereof; carrying out overall adjustment on the baseline vector by adopting a robust estimation method, and solving an optimal coordinate estimated value and precision information of the monitoring point; carrying out deformation analysis on the basis of the adjusted coordinate time sequence, and extracting tendency, periodicity and abnormal deformation; and outputting a deformation monitoring result, and carrying out visual display and early warning. According to the method, the precision and reliability of dam deformation monitoring are effectively improved through combination of network adjustment and robust estimation.

The invention relates to the technical field of synthetic aperture radar interferometry, in particular to an intelligent sea reclamation site deformation monitoring system and method based on InSAR, and the system comprises a data collection module which is used for collecting a multi-temporal satellite-borne SAR image of a sea reclamation area; the data processing module is used for executing sub-pixel matching, orbit error correction, atmospheric delay correction and residual optimization; the model construction module is used for constructing a segmented orbit error model, a tide-humidity coupling model and a dynamic regression model; the verification feedback module is used for verifying leveling precision and dynamically adjusting model parameters; according to the method, by fusing terrain mutability, tide-humidity coupling and soil rheological characteristics of a reclamation scene, accurate correction of multi-source error distribution of orbit errors, atmospheric delay and creep noise is realized, and the problem of insufficient adaptability of a traditional method in reclamation area deformation monitoring is solved; and millimeter-level reliable data support can be provided for cofferdam safety, hydraulic reclamation consolidation and port engineering.

The invention provides an anti-interference sound and vibration detection method and device, and relates to the field of sound and vibration detection. The method comprises the following steps: acquiring target special equipment and an equipment type corresponding to the target special equipment; according to the equipment type, acquiring a multi-channel high-sensitivity sensing array combination corresponding to the equipment type from a preset inspection method database; acquiring sound vibration signal data corresponding to the target special equipment based on the multi-channel high-sensitivity sensing array combination; performing modal decomposition on the sound vibration signal data by applying an improved empirical mode decomposition algorithm, eliminating pseudo-modal components and reconstructing effective modal signal data; and inputting the reconstructed effective modal signal data into a sound-vibration collaborative recognition model, and outputting a structural state recognition result of the target special equipment through the sound-vibration collaborative recognition model. According to the method and the device, the problem of low sound vibration detection accuracy of a traditional sound vibration detection technology when mechanical impact, fluid disturbance, electromagnetic interference or multi-source background noise is accompanied in the environment where the detected equipment is located is solved.

The invention relates to the technical field of geological disaster monitoring, and discloses a large-gradient deformation area phase optimization method and device, a medium and equipment, and the method comprises the steps: obtaining SAR data of a to-be-monitored area, and processing the SAR data to generate a differential interference image set; phase gradients of the differential interference image set in multiple directions are calculated to obtain a direction gradient image set, time sequence stacking and multi-direction fusion are carried out to obtain a phase gradient rate diagram, and a binary boundary diagram is determined through threshold segmentation; traversing the differential interferogram by using a sliding window, and performing structural consistency screening on neighborhood pixels in the window according to the binary boundary diagram to obtain a candidate pixel set; and performing amplitude consistency check, determining a homogeneous pixel set homogeneous with the central pixel, constructing a complex covariance matrix, and decomposing characteristic values to obtain an optimized phase of the central pixel. According to the method, the monitoring stability and precision are enhanced, the reliability and interpretability of a point selection result are improved, and an accurate phase reconstruction method is provided for a large-gradient deformation region.

The invention provides a method for detecting the porosity of ores in a dump leaching process, and belongs to the technical field of detection of the porosity of the ores. The method comprises the following steps: taking a dump leaching ore body, screening, uniformly mixing, splitting, putting into a dump leaching column to obtain the volume of a sample subjected to splitting in the dump leaching column, then performing fine grinding, putting into the dump leaching column to obtain the volume of the sample subjected to fine grinding in the dump leaching column, and then performing image analysis on the sample subjected to fine grinding to obtain the pore volume among sample particles subjected to fine grinding. And finally, converting the three volumes to obtain the porosity of the heap-leached ore. Through screening treatment, the sample representativeness is effectively improved; the ore before superfine grinding and after superfine grinding is introduced into a dump leaching column, the sampling uniformity is controlled, and the accuracy and stability of data are improved through the in-pile process under the same test column; an image analysis algorithm is introduced, and meanwhile, a correction coefficient is introduced, so that small pores among particles are precisely measured and calculated. The method is simple in process and convenient to operate, and the ore porosity can be rapidly and accurately measured and calculated.

A machine learning method where, in a first step, a first (general) machine learning model is trained using a first training dataset including unlabelled optical fibre sensing data. Then, in a second step, a transfer learning process is applied to adapt or fine-tune the first machine learning model to a more specific application (e.g. to perform a specific type of detection or classification). Due to the large volumes of optical fibre sensing data available, the first machine learning model may provide a general machine learning model which has a high level of generality and is highly adaptable.