1677results about "Material analysis using acoustic emission techniques" patented technology

The invention relates to the technical field of anti-cracking performance testing, in particular to an ultra-high performance concrete anti-cracking performance testing system and a dynamic monitoring method thereof.The system comprises a dynamic stress field simulation feedback module used for sensing three-dimensional distribution of a micro stress field in concrete in real time by constructing a flexible loading array to obtain dynamic stress field data; the multi-scale damage evolution tracking module is used for deploying a high-resolution acoustic emission network and a distributed optical fiber sensing layer based on dynamic stress field data of the flexible loading array to form a full-scale evolution graph from microdefects to macroscopic cracks; and the intelligent healing efficiency evaluation module obtains stress redistribution data and a full-scale evolution graph, and a repairing medium containing a tracer agent is injected into a preset crack path. According to the method, the whole process of crack resistance of the material from defect initiation to repair and regeneration can be quantitatively evaluated; and finally, outputting a dynamic evolution rule of the crack resistance and enhancing potential evaluation.

The invention relates to the technical field of structural health monitoring and intelligent diagnosis, in particular to a structural fatigue damage identification method based on acoustic emission and deep learning, and the method comprises the steps: collecting a structural response signal under a fatigue load through an acoustic emission sensor array, inputting the structural response signal to a CNN-BiLSTM-Attention mixed deep learning model, and carrying out the recognition of the structural fatigue damage through the CNN-BiLSTM-Attention mixed deep learning model; the model extracts local time domain features through a dynamic adaptive convolution kernel, captures long time sequence dependence by using a bidirectional long-short-term memory network, focuses key damage features through a bimodal space-time attention mechanism, divides damage stages based on a nonlinear dynamic threshold algorithm of fracture opening amount, constructs a training data set of physical-data fusion, and performs dynamic time domain feature extraction. The learning rate is optimized by adopting a gradient sensitive cosine annealing algorithm, and the robustness of the model is improved in combination with an anti-noise and anti-loss function. The method integrates physical characteristics and an intelligent algorithm, and has the advantages of adaptive noise suppression, strong cross-domain generalization ability, high real-time performance and the like.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

The invention relates to the technical field of rock mechanics, and discloses a rock three-dimensional reconstruction method and system based on multi-source information fusion, and the method comprises the steps: obtaining and preprocessing data, carrying out the spatial feature learning of a fusion feature vector through a 3D-CNN network, and constructing a three-dimensional voxel model of rock microscopic damage; converting the fused image data into a point cloud model of the underground cavern surrounding rock structure by adopting a three-dimensional reconstruction algorithm based on point cloud, and constructing a digital twin framework of the underground cavern surrounding rock structure based on an implicit surface reconstruction algorithm; feature parameters output by the three-dimensional voxel model and the digital twinning framework are used as input, and the optimal supporting opportunity and supporting parameters are output through an LSTM-CNN fusion model; in the underground engineering construction process, surrounding rock deformation data are collected in real time, and a supporting scheme is adjusted in real time through a depth deterministic strategy gradient algorithm; according to the method, the scientificity and timeliness of support design under complex geological conditions can be improved.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention discloses a coal and rock mass fracture instability multivariate signal fusion analysis method, and belongs to coal and rock mass analysis. The method comprises the following steps: carrying out time-frequency domain processing on an acoustic emission signal to obtain an accumulated ringing characteristic value # imgabs0 # and an accumulated b characteristic value # imgabs1 #; a main control frequency characteristic value # imgabs2 # and an amplitude characteristic value # imgabs3 # are accumulated; a spatial characteristic value # imgabs4 # of acoustic emission; fitting a fitting function # imgabs7 # from the characteristic value # imgabs5 # to the characteristic value # imgabs6 # respectively; according to the fitting function # imgabs8 #, calculating a correlation coefficient matrix R between the characteristic values # imgabs9 # to # imgabs10 #; according to the matrix R, determining a weight coefficient # imgabs11 # of each characteristic value; a first-order derivative function # imgabs13 # and a second-order derivative function # imgabs14 # of the fitting function # imgabs12 # are obtained, and according to the first-order derivative function # imgabs15 # and the second-order derivative function # imgabs16 #, a demarcation time point # imgabs17 # in the coal and rock mass damage process is determined; and the boundary time point # imgabs19 # is corrected by using the weight coefficient # imgabs18 #, and a corrected boundary time point # imgabs20 # is obtained. In order to solve the problems that an existing coal and rock mass fracture instability judgment index is single, and coal and rock mass fracture instability early warning is low in single index precision, the early warning precision is improved through multi-element signal fusion.

The invention discloses an acoustic emission intelligent detection method and system for hydrogen-induced damage of a high-pressure hydrogen system, and the method comprises the steps: collecting a system operation signal through an acoustic emission sensor, carrying out the combined preprocessing of variational mode decomposition and adaptive wavelet threshold noise reduction, restraining noise, constructing a lightweight MobileNet-TCN network, carrying out the deep feature extraction, and carrying out the detection of the hydrogen-induced damage of the high-pressure hydrogen system. GRU and a three-dimensional point cloud technology are fused to realize submillimeter-level positioning of an acoustic emission source, a big data damage case library is associated based on an acoustic emission parameter accumulation model, dynamic assessment and early warning of damage risks are realized, multi-physics field monitoring data are combined, damage classification is optimized through a GCNs (Graph Convolutional Networks), and the above processes are systematically integrated. And full-chain intelligent processing from signal acquisition to risk early warning is completed. The scheme has the advantages of strong anti-interference capability, submillimeter positioning precision, high edge end reasoning efficiency, high damage classification accuracy and dynamic early warning capability, and is suitable for safety monitoring of hydrogen energy storage and transportation equipment.

The invention relates to the technical field of construction surrounding rock stability evaluation, discloses a tunnel surrounding rock stability evaluation method and system based on multi-physics field parameter inversion, and aims to solve the problems that an existing method is insufficient in data collaboration, high in parameter inversion multiplicity and poor in surrounding rock stability evaluation accuracy and timeliness. According to the scheme, the method mainly comprises the steps that an acousto-optic electromagnetic vibration drill multi-mode sensing array is arranged, and time-space synchronization is implemented; establishing a mutual interference entropy spectral density model to realize multi-physics field collaborative excitation and acquisition; a unified feature vector is obtained through data correction, feature extraction and weighted fusion; a joint inversion objective function embedded with rock physical constraints is constructed, a three-dimensional physical property parameter field is obtained through inversion, and a dynamic permeability field is calculated in combination with acoustic emission energy; and finally, dynamically updating the model by utilizing ensemble Kalman filtering, and obtaining a final risk probability based on updated parameters and seepage-uncertainty coupling correction. According to the method, the accuracy, the real-time performance and the reliability of the stability evaluation of the surrounding rock of the deep-buried tunnel are improved.

The invention belongs to the technical field of nondestructive testing, and particularly relates to a detection method for quantitatively evaluating fatigue damage in a pipeline through a nonlinear ultrasonic technology. 316 stainless steel and SA335GRP-11 ferrite steel samples are adopted, a welding seam area is formed through V-shaped groove welding, fatigue loading is conducted, and actual pipeline fatigue damage is simulated; for the samples with different fatigue cycles, nonlinear response signals of the samples are obtained; fourier transform is carried out on the obtained signal to obtain an energy spectrum of the signal, and energy changes of the signal on a fundamental frequency band and a second harmonic frequency band are analyzed; and calculating a nonlinear coefficient, and analyzing a change rule of the nonlinear coefficient along with fatigue cycles, so that the evolution process of fatigue damage in the pipeline is evaluated. According to the method, energy is used for replacing the amplitude value, on the basis of the formula principle, integration is used for replacing the amplitude value to improve the intensity of the characterization nonlinear ultrasonic signal, the early stage of damage and the later stage of crack initiation are detected, and reliable guarantee is provided for safe operation of an industrial pipeline system.

The present invention provides a dual-level thermal runaway warning method of a lithium battery based on a sound signal, comprising: obtaining a battery sound signal sequence; performing outlier identification on the battery sound signal sequence, and providing a level-1 thermal runaway warning when an abnormal data point exists; extracting a time-frequency domain feature of the abnormal data point, and identifying a presence of a thermal runaway expansion sound through a sparrow search algorithm-optimized eXtreme Gradient Boosting (SSA-XGBoost) algorithm, to providing a level-2 thermal runaway warning. In the SSA-XGBoost algorithm, optimal parameter adjustment is performed on a number of iterations, a learning rate, and a decision tree depth of the XGBoost algorithm through the SSA. A dual-level thermal runaway warning strategy is adopted to perform grading identification on general anomalies or deep anomalies, thereby effectively improving identification accuracy of a weak abnormal sound signal in an early stage.

The invention relates to the field of bridge monitoring, and particularly discloses a crack monitoring system for a fixed end of a steel-concrete composite beam. The method is used for solving the problems that multi-source information fusion, hot spot stress on-line accurate calibration, fatigue life real-time accurate evaluation, S-N curve dynamic correction and remote visual early warning are difficult to realize in crack monitoring of a fixed end of an existing steel-concrete composite beam under complex load and environmental interference. Comprising a multi-sensor data acquisition module, an edge preprocessing module, a data fusion and damage identification module, a hot spot stress calibration module, an early warning decision module, a visual remote communication module and a digital twinborn calibration module. According to the invention, through integration of multi-sensor synchronous acquisition, edge intelligent preprocessing, multi-source data fusion, online hot spot stress calibration and digital twinborn calibration, Monte life prediction and early warning and hybrid energy management, high sensitivity, low power consumption and accurate early warning of steel-concrete composite beam fixed end crack monitoring are realized.

The invention discloses an aerial optical cable defect detection method based on multi-mode sensing fusion and related equipment, and relates to the field of data processing. The method comprises the following steps: acquiring multi-modal sensing data along an aerial optical cable through sensing equipment of different modals; performing time and space alignment processing on the distributed fiber bragg grating strain data, the acoustic emission signal data and the visual image data to obtain multi-mode time and space alignment data; extracting feature data of each mode based on the multi-mode space-time alignment data; performing fusion processing on the feature data of each modal through an attention mechanism to obtain fused feature data; determining a defect identification result of the aerial optical cable according to the fusion feature data, wherein the defect identification result comprises a defect type and a position of the defect on the optical cable; obtaining the result confidence of the defect recognition result; and generating a final optical cable defect detection result according to the result confidence. According to the invention, the problem that the current aerial optical cable defect detection has relatively large missed detection and false detection risks can be relieved.

The invention provides a multi-dimensional coupling water quality evaluation method and system for drainage of a sewage treatment plant, and the method comprises the steps: obtaining water response data of a drainage port of the sewage treatment plant in a flow velocity fluctuation environment, so as to generate an acoustic feature vector related to a treatment process of the sewage treatment plant; carrying out association processing on the acoustic feature vector, the chemical parameter variation and the temperature gradient variation to generate a multi-dimensional coupling data set so as to establish a mapping relation between sound wave spectrum features and water quality discharge standard grades, and identifying association between a multi-parameter variation mode and the processing process so as to generate an analysis result; according to the mapping relation and the analysis result, an evaluation control parameter set is generated, the multi-dimensional coupling data set is dynamically corrected, and a water quality evaluation result associated with the treatment process is generated. And the monitoring reliability and the evaluation accuracy under complex working conditions are improved.

The embodiment of the invention provides a real-time defect detection method and device based on carbon fiber winding and a medium, and relates to the technical field of carbon fiber laying layer detection.The method comprises the steps that real-time environment data of carbon fiber winding are obtained, and multi-mode cooperative detection is conducted on the real-time environment data of carbon fiber winding, obtaining a carbon fiber real-time defect data set; performing multi-mode space-time synchronous calibration on the carbon fiber real-time defect data set to determine carbon fiber defect synchronous data; performing strategy-level fusion on the synchronous data of the carbon fiber defects to obtain winding state preliminary judgment data; based on the winding state preliminary judgment data, multi-modal weight update data are determined through carbon fiber winding process stage analysis; and according to the modal weight updating data, determining the real-time winding defect of the carbon fiber through carbon fiber defect type analysis. According to the method, the technical problem of poor anti-interference capability in a single parameter analysis process in carbon fiber winding detection is solved.

The invention belongs to the technical field of grouting compactness detection, and particularly discloses and provides an intelligent detection system and method for the grouting compactness of a bridge prestressed pipeline, and the system comprises a signal acquisition and analysis module, a defect boundary calibration module, a defect recognition and analysis module and a compactness judgment and processing module. The method specifically comprises the steps that acoustic emission and fluctuation response signals are collected in real time through a sensor array arranged on the outer wall of a pipeline, and three-dimensional slurry density distribution is recognized; constructing an inner wall three-dimensional model in combination with a pipeline design drawing, dynamically setting a slurry density threshold value according to a grouting material formula and an environment temperature, and further calibrating a slurry defect boundary; extracting multi-dimensional features such as spatial positions, effective volumes, geometric regularity and types of the defects on the basis of boundaries, and calculating grouting compactness; and finally, according to whether the compactness reaches the standard or not, a grout supplementing early warning instruction or a detection report is automatically generated. According to the method, the reliability of defect identification and compactness calculation under complex working conditions is remarkably improved.

The invention relates to the technical field of new energy aluminum alloy precision casting multi-modal quality monitoring, in particular to a new energy aluminum alloy precision casting multi-modal quality monitoring device and method. According to the technical scheme, the new energy aluminum alloy precision casting multi-mode quality monitoring device comprises a quality monitoring device body, an infrared thermal imager probe, a germanium single crystal protection lens, an integrated spectrometer probe debugging port, an acoustic detection microphone array and a honeycomb acoustic shielding cover. An infrared thermal imager probe is arranged on one side of the quality monitoring device main body, and a germanium single crystal protection lens is arranged on the outer side of the infrared thermal imager probe; synchronous acquisition and fusion analysis of multi-physical field data are realized by integrating six modes of spectrum, infrared thermal imaging, vibration, machine vision, acoustics and pressure deformation, and meanwhile, when the shrinkage risk is predicted by the infrared thermal imaging, a die-casting process compensation mechanism is automatically triggered to kill defects in the germination stage, so that the defect rate is greatly reduced.

The invention discloses a wind power blade defect detection method based on acoustic emission, and relates to the technical field of wind power blade detection.The wind power blade defect detection method comprises the steps that multiple lasers, a multispectral camera, a thermal imaging sensor and a plurality of acoustic emission sensors are carried on an unmanned aerial vehicle, and multiple multispectral images and thermal imaging images within fixed time are obtained; a multispectral image total discrimination sequence and a thermal imaging image total discrimination sequence are obtained through processing, suspicious pixel points in the two sequences are mapped into actual three-dimensional coordinates of the wind power blade, a light reflection defect area and a thermal distribution defect area are obtained through fitting, the intersection of the two areas is obtained, and a specific area of the wind power blade defect is obtained. And positioning the region by using an acoustic emission sensor to obtain a to-be-detected acoustic signal, denoising by combining LMD with MFSE, extracting a feature vector, and substituting the feature vector into a GRU wind power blade defect judgment model to judge whether an internal fault exists or not. According to the invention, comprehensive perception and correlation judgment of internal and external defects of the wind power blade are realized.

The invention belongs to the field of rock-soil mechanical test equipment, and particularly discloses a shear slip test device for testing mechanical properties of high-temperature fractured rock mass, which comprises a shear test platform, a shear test box, a sound wave emission system, and a temperature control module and a circulating cooling system integrated in the shear test box. The shear test platform realizes independent control of normal force and shear force through a first loading block and a second loading block; the temperature control module and the circulating cooling system can simulate a high-temperature environment and perform a cold and hot cycle test; the acoustic emission system is used for monitoring acoustic emission signals in the rock sample in real time. The device can accurately simulate a high-temperature and cold-hot cycle environment, has high-precision mechanical loading capacity and a multi-parameter monitoring function, provides an effective experimental means for researching the mechanical property and damage mechanism of a high-temperature fractured rock mass, and can be widely applied to the research fields of deep geothermal resource development and enhanced geothermal systems.

The invention discloses a tunnel monitoring method and system based on a vibrating wire sensor, and relates to the technical field of tunnel engineering construction safety monitoring, and the method comprises the steps: collecting the vibration frequency data and spatial position coordinates of the vibrating wire sensor in a monitoring region; the method comprises the following steps: calculating a time difference and an energy attenuation ratio between adjacent sensors by extracting spectrum energy density distribution, and constructing a stress wave propagation field; dividing grids in the propagation field to calculate node stress values, generating a stress field distribution cloud picture and extracting a stress concentration area; calculating a gravity center position coordinate change rate and a stress time sequence curve of the stress concentration area, and determining fracture extension parameters; and calculating a surrounding rock bearing capacity attenuation curve based on the parameters, and outputting an early warning signal when the slope exceeds a threshold value. According to the invention, real-time monitoring and early warning of the tunnel surrounding rock instability risk are realized.

The invention discloses a composite material wind power blade damage detection method, and aims to solve the problems of difficulty in quantitative analysis and low distinguishing precision of various damage types in composite material wind power blade damage detection in the prior art. Comprising the following steps that acoustic emission signals in the operation process of the wind power blade are obtained in real time and preprocessed, and the acoustic emission signals comprise parameter data and waveform data; performing unsupervised clustering on the preprocessed parameter data to obtain a preliminary clustering result; training the preprocessed waveform data by adopting an improved multi-branch convolutional neural network, and outputting an accurate classification result; the unsupervised clustering result is verified by using the supervised learning classification result, and the accuracy of the clustering result is evaluated; and fusing the quantitative index accumulated energy, the damage type and the frequency, and constructing a comprehensive damage evaluation model to determine the damage degree. According to the method, the unsupervised clustering method and the supervised learning method are combined, and accurate identification and quantification of the damage are realized.

The invention discloses a rock damage assessment method fusing wave velocity and microscopic fracture, and belongs to the technical field of rock damage assessment, and the rock damage assessment method comprises the following steps: arranging a wave velocity testing device on a rock sample, and respectively making an unloaded rock sample and a damaged snapshot rock sample under different stress loading; microcosmic fracture information of the damaged snapshot rock sample is identified, a three-dimensional wave velocity model of the damaged rock sample under different stress loads based on a wave velocity test is constructed, and then a macro-micro scale fusion multi-dimensional data set is constructed; based on the macro-micro scale fusion multi-dimensional data set, constructing and training a rock damage identification model; and inputting the characteristic parameters of the rock sample to be evaluated into the rock damage identification model, identifying the damage distribution characteristics of the rock sample on the spatial scale, and evaluating the rock damage degree. According to the method, accurate prediction and evolution process evaluation of the rock damage degree and spatial distribution can be realized, and a reliable technical means is provided for disaster early warning, stability evaluation and risk prevention and control in complex rock mass projects such as deep mines and tunnels.

A concrete damage real-time positioning method based on laser induction and dynamic wave velocity correction comprises the following steps: constructing a damage induction platform integrated with a high-energy pulse laser, combining with a three-dimensional mobile platform to realize accurate positioning of a laser focus, and arranging a phase demodulation type optical fiber acoustic emission sensor array to construct a full-coverage monitoring network; synchronous acquisition of high-frequency acoustic emission signals is realized by adopting a multi-channel coupling phase demodulation system, and noise suppression is completed in combination with a self-adaptive wavelet threshold algorithm; a dynamic wave velocity attenuation model is established based on space-time reference information, a propagation path is optimized through a Basic Theta * path planning algorithm, a wave velocity field is reconstructed by applying an ART algebraic iteration algorithm, and a thermodynamic diagram is generated; and finally, constructing a convolution-graph neural joint network model, and realizing accurate monitoring and positioning analysis of the concrete structure damage through a damage probability distribution generation and multi-scale verification module by using a multi-modal data fusion and transfer learning technology.

The invention relates to the technical field of underground coal gasification, in particular to a coal underground gasification process stability monitoring and evaluating method which comprises the following steps: determining a space mapping relation between a gasifier temperature field and an acoustic emission event according to the temperature gradient and the acoustic emission event of a gasifier; carrying out three-dimensional inversion on a combustion cavity of the gasification furnace, and determining attenuation trends of the coal seam under different temperature gradients; according to the attenuation trend of the coal seam under different temperature gradients, the propelling direction of the boundary of the combustion cavity is determined by utilizing the spatial distribution density and the relative position of each acoustic emission event; according to the propelling direction of the fuel cavity boundary, boundary moving paths are formed, and the propelling area proportion of each position in the propelling direction is calculated according to the distance difference of the boundary moving paths; and according to the propulsion area proportion of each position in the propulsion direction and the expansion rate of the combustion cavity, determining the space stability classification of each part of the combustion cavity. The accuracy and the efficiency of monitoring underground gasification of the coal seam are realized.

The present invention relates to an in-situ micro-nano impact indentation testing instrument, falling within the technical field of material micromechanical testing. The instrument comprises a nitrogen generation module, an environmental chamber, a high / low temperature loading module, an “optical-infrared” in-situ monitoring module, an “electromagnetic-piezoelectric” coupling impact module, etc. After the nitrogen is introduced into the environmental chamber and the test area is determined by microscopic imaging, the “electromagnetic-piezoelectric” coupling impact module can drive an indenter to indent a specimen. An acoustic emission sensor embedded in the high / low temperature loading module can monitor the surface crack propagation of the specimen. The “optical-infrared” in-situ monitoring module can perform real-time high-speed optical imaging and infrared imaging on the impact indentation process. The present invention can perform micro-nano impact indentation testing on the material at high or low temperatures.

The invention discloses an urban road multi-risk evaluation method based on DAS and acoustic emission signal characteristics, and relates to the technical field of urban road safety monitoring. The method comprises the following steps: receiving voiceprint signals around existing communication optical fibers of urban roads acquired based on a plurality of channels of a DAS (Data Acquisition System); extracting a time domain, a frequency domain and composite features based on the acquired voiceprint signals around the existing communication optical fiber of the urban road, removing redundant features of the time domain, the frequency domain and the composite features through a Pearson correlation coefficient matrix, and generating optimized time domain, frequency domain and composite features; and for the optimized time domain, frequency domain and composite features, performing anomaly judgment by adopting an area outside a confidence interval of single feature density distribution and two-dimensional Gaussian distribution, and positioning an abnormal DAS channel in combination with principal component analysis dimension reduction and a DBSCAN clustering method. According to the invention, underground cavity hidden dangers of urban roads can be effectively detected, ground risk events can be classified, and urban road safety risk monitoring and evaluation are realized.

The invention relates to the field of optical material defect detection, and discloses a PC lens defect classification and identification method and system, and the method comprises the following steps: asynchronously collecting lens multi-modal data through polarized light imaging, thermal imaging and an acoustic emission sensor, and constructing a five-order asymmetric tensor containing polarization angle, spectrum, time, space and defect features; performing dynamic dimension reduction on the tensor by using an improved Tucker decomposition method, and extracting a core tensor and a factor matrix; establishing a polarization angle-time correlation coding model based on inverse problem solution of a light field state equation, deducing defect distribution characteristics and optimizing a core tensor; mapping the optimized tensor to the QUBO Hamiltonian of a quantum annealing machine, and carrying out optimization solution; and finally, fusing the multi-mode confidence coefficients of polarized light, thermal imaging and acoustic emission signals, generating defect classification labels and inverting geometric and mechanical parameters of the defects. The lens flaw detection efficiency and the classification accuracy are remarkably improved, and the method is suitable for industrial-grade high-precision quality control scenes.

The invention provides an on-line automatic test method for the compressive capacity of a corrugated carton, and belongs to the technical field of corrugated carton production. A micro-deformation loading system is constructed to apply a test load which does not exceed 10% of the expected maximum bearing capacity, and a laser displacement sensor array is adopted to measure micro-deformation displacement data of six surfaces of the carton; and synchronously acquiring acoustic emission signals and extracting spectrum characteristic parameters. Micro-deformation data is analyzed based on a carton fiber structure physical mechanism model, stress field distribution is calculated, analysis is carried out in combination with a hierarchical attention carton strength evaluation model, a rigidity coefficient is calculated and compared with a reference threshold value, a structural weak area is identified through a strain energy distribution diagram, and a damage risk index is quantified. And comprehensively evaluating the compressive strength prediction value and the confidence interval of the corrugated carton, and outputting a prediction result, thereby realizing accurate evaluation of the compressive strength of the corrugated carton under a non-destructive condition.

The invention discloses an intelligent identification method for a rock tension-shear rupture mechanism based on acoustic emission RA-AF parameters, and belongs to the technical field of intelligent identification. The method comprises the following steps: carrying out indirect tension test and variable-angle shear test on rock, acquiring RA-AF data of the rock under tension and shear loads by adopting an acoustic emission technology, and respectively constructing RA-AF standard databases corresponding to tension and shear fracture mechanisms based on a DBSCAN clustering algorithm; wherein 4 / 5 is used as training set data, and 1 / 5 is used as test set data. A probabilistic neural network is used for learning RA-AF data feature information of a pulling and shearing fracture mechanism, an intelligent classification model of the pulling and shearing fracture mechanism is established, the accuracy rate of the model is detected, and the model which reaches the detection standard through adjustment is applied to quantitative recognition of the fracture mechanism of the same rock under different static loads. According to the method, the data processing workload of workers can be reduced, and the classification efficiency is improved.

A method for monitoring pipeline integrity is provided. The method includes obtaining, using at least one hardware processor, acoustic signal captured by at least one optical fiber arranged along a pipeline. The method includes inputting, using the at least one hardware processor, the acoustic signal to a machine learning model. The machine learning model is trained to execute a first analysis on a first representation of the acoustic signal and a second analysis on a second representation of the acoustic signal, the first analysis being independent to the second analysis. An output of the trained machine learning model is at least one of a first result based on the first analysis or a second result based on the second analysis. The method includes determining, using the at least one hardware processor, an indication of pipeline integrity based on the at least one of the first result or the second result.