1843 results about "Acoustic emission" patented technology

The invention discloses an automatic control method and system for secondary granulation of a high-voltage zinc oxide resistor disc, relates to the technical field of intelligent manufacturing of power equipment, and solves the problems of out-of-control particle morphology caused by dynamic coupling parameter identification lag and control instability caused by multi-physical field parameter coupling in an existing method. According to the invention, a dynamic physical property parameter matrix is generated in real time based on multi-band dielectric relaxation spectrum analysis and terahertz wave tomography; predicting a fluidized phase change threshold value and an energy gathering area through multi-physics field coupling modeling; a time sequence attention deep reinforcement learning algorithm is adopted to generate a multi-field cooperative adjustment instruction; positioning a parameter conflict source and triggering decoupling compensation by combining a high-frequency vibration and acoustic emission combined monitoring module; performing closed-loop correction on the control network weight based on the laser spectrum data and a partial least squares regression model; the real-time performance of fluidization parameter identification, the stability of multi-field coupling control and the recovery efficiency of abnormal working conditions are remarkably improved, and meanwhile the batch consistency of the electrical performance of the resistor discs is guaranteed.

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 discloses a guardrail collision warning method and system, and the method comprises the steps: carrying out the nonlinear phase alignment of a multi-source heterogeneous signal through an adaptive variational mode decomposition algorithm according to the vibration acceleration, strain tensor and acoustic emission signals collected in real time through a multi-mode sensor array disposed at a guardrail key node, generating a three-dimensional dynamic strain field distribution vector; inputting the three-dimensional dynamic strain field distribution vector into a nonlinear dynamics reconstruction module, and extracting a chaotic feature fingerprint spectrum of the collision event; performing collision intensity grading processing on the chaos feature fingerprint spectrum, and outputting a quantitative evaluation matrix including a collision grade, a damage radius and a residual intensity prediction value; and triggering a multi-mode alarm protocol according to the quantitative evaluation matrix, and synchronously transmitting the multi-mode alarm protocol to a traffic management center and an adjacent vehicle OBU terminal. According to the embodiment of the invention, the state of the guardrail can be monitored in real time, and a collision event can be accurately evaluated and warned.

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 present invention provides a tool wear state monitoring method and system based on multiple types of signals, and relates to the technical field of data processing. The method includes: obtaining data of a cutting force, an acoustic emission signal and a vibration signal, and extracting a plurality of statistical features from data of the cutting force and the acoustic emission signal; extracting a singularity feature from the vibration signal by combining a singularity analysis with a wavelet transform; building a tool wear state monitoring model based on a random forest, using an obtained feature to perform preliminary training, and outputting a wear prediction result; and based on the real-time data of the cutting force, the acoustic emission signal and the vibration signal, monitoring the wear state of the tool through the refined model.

The invention discloses a seabed karst stratum tunnel shield data monitoring and analyzing method and system. The method comprises the steps that shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are synchronously collected; shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are subjected to seepage-stress coupling constraint, a hydraulic connectivity strength index is generated through fracture network seepage tensor inversion, and the filler migration risk level is judged based on pore water pressure dynamic mutation characteristics. And fusing the cutterhead torque frequency spectrum characteristic and the propulsive force fluctuation characteristic to generate a shield disturbance entropy value. According to the method, the hydraulic connectivity index is generated through seepage stress coupling inversion, the grouting path and the rheological parameters are dynamically optimized based on the fracture probability field, shield tunneling, seepage sudden change and grouting prevention and control cooperation is achieved, and the problem of prevention and control disjunction is radically solved.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.

The invention discloses a temperature control management method for an electric oven, and relates to the technical field of intelligent household appliance control, and the method comprises the steps: carrying out the offset correction of a pre-stored mode mapping table based on a calibration matrix, loading a target temperature interval and a three-dimensional temperature field partition parameter according to a cooking mode selected by a user, and triggering a dynamic learning mark; according to the dynamic learning mark, the furnace chamber temperature and the environment temperature change rate are collected in real time, the power compensation amount is calculated through a thermal inertia compensation model, and a partition power control matrix is generated in combination with the three-dimensional temperature field partition parameters; and distributing the power compensation amount and the partition power control matrix to the heating pipe according to a preset topology, dynamically adjusting power output, and implementing temperature early warning and no-load identification based on an acoustic emission spectrum entropy value. The temperature gradient change rate is calculated through the Fourier heat conduction law, the three-dimensional temperature field partition parameters are dynamically generated, and accurate control over the temperature field in the complex three-dimensional space is ensured so as to cope with environment changes and user demand changes.

The invention relates to the technical field of pile foundation monitoring, and discloses a pile foundation state real-time monitoring and diagnosis system based on a digital twinborn technology. The system comprises a multi-source data acquisition module, a data confidence evaluation module and an acoustic emission monitoring decision module. The multi-source data acquisition module comprises a plurality of sensor groups deployed at different depths of a pile foundation, each group comprises a strain sensor, an acceleration sensor, an acoustic emission sensor and a temperature sensor, and pile foundation data can be acquired in multiple dimensions; the data confidence evaluation module receives original data, generates a correction data sequence through time sequence noise separation and reconstruction, and calculates data confidence according to correction data distribution dispersion; the acoustic emission monitoring decision module judges whether acoustic emission monitoring is started or not according to the data confidence coefficient, and controls the acoustic emission sensor array at the top of the pile foundation to collect acoustic emission signals during starting. The system can comprehensively obtain pile foundation data, improve data accuracy, achieve early damage recognition and guarantee pile foundation safety.

The invention relates to the technical field of monitoring and early warning, and discloses an automatic monitoring and early warning method and system for a pump room. The method comprises the following steps: collecting hydraulic pulsation, cavitation acoustic emission and bearing temperature rise data through multiple sensors; a pump efficiency attenuation vector is obtained based on an environment adaptive algorithm optimization feature; predicting the health state of the equipment by using a hydraulic model; carrying out deep learning classification by adopting a fault propagation network; and four-stage grading early warning is realized by combining water inflow-pump efficiency coupling judgment. According to the method and the device, the technical problem that the gradual change fault and the multi-parameter coupling fault mode of the pump room equipment under the underground complex working condition cannot be accurately identified in the prior art is solved, and the accuracy of fault prediction and the timeliness of early warning of the underground drainage pump room are improved.

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 discloses a multi-source equipment health collaborative operation and maintenance method in a complex environment based on edge federation, and relates to the technical field of equipment collaborative operation and maintenance. Vibration acoustic emission current waveforms are mapped to a unified time-frequency grid at an edge gateway, and an encrypted sparse index is generated and uploaded; training a global model by combining a graph regular base network with a gradient direction and a distribution distance, and injecting fault information by using a new working condition protection door; after being issued by a sparse adaptation layer of double-temperature-zone distillation and random projection compression, fine adjustment is carried out on site under few samples through temperature gradual fusion and reversible orthogonal mapping, and only unit gradient direction and health labels are uploaded; the center adopts entropy constraint Bayesian filtering to fuse information to generate a health index, a maintenance schedule and a spare part plan are formed by integer programming according to confidence intensity mapping risk popularity, a result is differentially pushed and audited, and a closed loop of collection, learning, evaluation and decision is realized.

The invention relates to the technical field of electromagnetic relay control, in particular to an electromagnetic relay control system, which comprises a relay execution unit, a contact state monitoring unit and an intelligent viscosity elimination control unit, and is characterized in that the contact state monitoring unit acquires quantum tunneling effect impedance spectroscopy and acoustic emission energy entropy change signals in real time; the intelligent viscosity-eliminating control unit analyzes signals through a fusion welding risk multi-dimensional evaluator, triggers a three-stage control sequence, firstly starts a magnetostriction high-frequency oscillator to inject an inverted eddy current array, activates a hysteresis loop restorer to apply a gradient demagnetization potential field if the fusion welding risk multi-dimensional evaluator is not recovered, and finally, recovers the fusion welding risk multi-dimensional evaluator; when an acoustic emission signal is abnormal, a chaotic encryption message is generated through the fault feature topology generator and is linked with the magnetic solitary wave breaking device, ultrasonic frequency mechanical energy can be captured and converted into regenerative current, an adhesion evolution path is reconstructed after diagnosis, the problems of contact adhesion risk identification lagging and lack of multi-physics field cooperative control in the prior art are solved, and the reliability of the contact adhesion risk identification is improved. And the adhesion early warning success rate and the removal efficiency are improved.

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

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 discloses a coal and rock mass fracture seismic source and stress field joint inversion method and system, and the method comprises the following steps: arranging a multi-channel sonic sensor in a coal and rock mass, and collecting original acoustic emission / microseismic signal data; the head wave arrival time / amplitude of the filtered effective waveform data is automatically picked up through an improved AIC (Akaike Information Code), quantitative evaluation is carried out on the quality of a picked-up result, and high-precision positioning is carried out on waveform signals with a high signal-to-noise ratio and a low signal-to-noise ratio; constructing a tension fracture seismic source model based on a displacement discontinuous tensor, obtaining a moment tensor component of a fracture event, and calculating to obtain a spatial orientation, a tension-shear attribute, a deviation angle alpha, energy release and a moment magnitude of the fracture; establishing a stress field inversion model of the tension-shear fracture seismic source, and realizing the inversion of the stress direction and size of the tension-shear fracture seismic source; and a slip direction included angle minimization objective function is established for iteration until convergence conditions are met, and joint iteration inversion of the fracture and the stress field is 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 discloses a self-adaptive test parameter optimization method, and relates to the technical field of parameter optimization, and the method comprises the steps: collecting an original sensing signal in a mechanical test system, and carrying out the preprocessing of the original sensing signal; based on the preprocessed data set, constructing a four-dimensional space-time tensor, and executing improved parallel factor tensor decomposition to obtain a decoupled core factor and space-time feature component matrix; performing cross-modal alignment on the decoupled core factor and the time-space feature component matrix through a wear feature channel and an acoustic emission feature channel to obtain a fused cross-modal feature vector; performing crack growth rate prediction on the fused cross-modal feature vectors to obtain a crack risk level, and adjusting a strategy through dynamic parameters to obtain an optimized parameter set; the problem of signal noise and time mismatch is solved through multi-mode signal preprocessing, and high signal-to-noise ratio input is provided for subsequent analysis in combination with wavelet denoising, space-time alignment and double-domain feature extraction.

The invention relates to a concrete degradation simulation method and system under continuous load-chloride ion coupling, and solves the problems that systematic research on overall mechanical property degradation of a concrete structure under the load and chloride ion coupling effect is lacked, and the degradation condition of the concrete structure in an actual service environment is difficult to comprehensively reflect. The method comprises the following steps: applying a continuous load to a test block, preparing a solution according to a stress type to determine a chloride ion diffusion direction, applying an electric field to accelerate migration and maintain test stability, and after an experiment, measuring the penetration depth and calculating a diffusion coefficient; in the process, multi-dimensional data such as cracks and acoustic emission are collected and subjected to PCA dimension reduction and redundancy removal, then damage factors are inversed through deep learning modeling in combination with dual-stage optimization and physical constraint, and finally damage and chloride ion parameters are fused to form gradient parameters fitting actual degradation. The method has the following effects that the concrete degradation process is accurately simulated, a basis is provided for durability evaluation and life prediction, and the engineering safety and economy are improved.

The invention discloses a collaborative diagnosis method for partial discharge and overheating faults of a high-voltage switch cabinet, and particularly relates to the technical field of fault diagnosis of the high-voltage switch cabinet. Signals are synchronously acquired through ultrahigh frequency, acoustic emission and infrared sensors, and an abnormal candidate sequence is extracted through adaptive baseline segmentation; through laser calibration and time interpolation calibration, uniform event position information and timestamps are mapped; identifying potential coupling pairs of partial discharge and hot spot events based on dynamic threshold and time difference matching; generating a high-risk linkage region through spatio-temporal clustering, trajectory analysis and region fusion; updating the diagnosis model based on the high-frequency data increment, and generating a collaborative diagnosis report; the method breaks through the limitation of a static threshold value and a single sensor, and remarkably improves the diagnosis precision and adaptability of a composite fault under a complex working condition.

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 relates to the technical field of information processing, in particular to a mine microseism positioning control system and method.The mine microseism positioning control method comprises the steps that microseism signals are synchronously collected through a geophone, an accelerometer, an acoustic emission sensor and an optical fiber sensor, and data timestamps are aligned based on a GPS / Beidou clock; blind source separation is carried out on the signals after wavelet filtering, independent seismic source components are extracted, multi-sensor features are fused through a graph neural network, and a space-time joint feature matrix is constructed; classifying micro-seismic event types by using a ResNet-LSTM hybrid model, generating a seismic source coordinate initial solution based on deep reinforcement learning, and iteratively converging to an optimal solution through a particle swarm optimization algorithm; and mapping a positioning result to a three-dimensional geological grid model, simulating an energy diffusion path, triggering a three-level alarm according to the event energy density, and automatically pushing an emergency instruction to a mine safety management system. Therefore, the problem of low precision of the existing mine microseism positioning control system 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 discloses a tunnel rockburst type prediction method and system based on multi-dimensional mechanism fusion and a medium. Relates to the technical field of tunnel rockburst. Dynamically acquiring multi-dimensional basic information in a tunnel construction process; inverting first proportions of different fracture modes of the micro-seismic event according to the micro-seismic information fusion moment tensor, and performing energy evolution on the acoustic emission information to obtain second proportions of different fracture modes of each acoustic emission event; the dynamic failure weights of different fracture modes are comprehensively obtained; spatial clustering analysis is carried out based on the dynamic damage weight and the multi-dimensional basic information, rockburst type probability models of different rockburst types are constructed, and the probabilities of different rockburst types are predicted; according to the scheme, the rockburst type probability model is constructed in combination with real-time multi-dimensional basic information, dynamic judgment and prediction of rockburst types are achieved, and technical guarantee is provided for deep tunnel construction safety.

The invention relates to the technical field of machining, and discloses a lathe tool wear self-inspection alarm system which comprises a multi-source sensing module, an intelligent analysis module, a dynamic response module, an interactive display module and a self-adaptive energy supply module. The multi-source sensing module synchronously acquires the surface abrasion loss, the vibration spectrum and the acoustic emission signal of the cutter through an optical sensor, a capacitive sensor and an MEMS triaxial accelerometer, and outputs the surface abrasion loss, the vibration spectrum and the acoustic emission signal to the intelligent analysis module; and the intelligent analysis module performs fusion analysis on the multi-source data by adopting a mixed feature extraction and double-model decision mechanism. High-precision monitoring and dynamic evaluation of lathe tool wear are realized through optical, vibration and acoustic emission sensors of the multi-source sensing module in combination with mixed feature extraction and a double-model decision mechanism of the intelligent analysis module. By means of the mode of combining multi-dimensional monitoring and intelligent analysis, machining defects and equipment failure risks caused by tool abrasion can be reduced.

The invention discloses a transformer equipment state real-time monitoring method and system based on multi-source data fusion, and particularly relates to the technical field of transformer equipment state monitoring, and the method comprises the steps: firstly obtaining a unified time reference through the multi-channel time alignment driven by an event anchor point, and employing a strategy of combining rough alignment with refined alignment; on the basis, the channel alignment confidence coefficient is constructed according to the main peak sharpness ratio, the group delay consistency, the phase locking consistency and the geometric consistency of temperature compensation; then acoustic emission spectrum energy and pulse number, vibration impact characteristics and cross-sensor consistency characteristics are extracted under a unified time reference, and dynamic threshold setting is carried out in combination with load and environment context; performing gating weighting, intra-group decorrelation and probability calibration on the multi-source evidence according to the channel health factor to obtain calibrated fault probability and inconsistency; and finally, outputting a grading result under the constraint of multi-stage judgment and physical consistency rechecking controlled by the error discovery rate.

The invention provides a coaxial unequal force-based gas-containing coal and rock mass true triaxial indoor test method, which comprises the following steps of: preparing a sample, injecting CH4 into a confining pressure cavity, synchronously starting an acoustic emission device and a binocular miniature high-speed camera, and testing the gas-containing coal and rock mass true triaxial indoor test. Acoustic emission monitoring software and DIC analysis software are used for recording acoustic emission and strain field evolution data, stiffness loading systems in the sigma 1, sigma 2, sigma 3 + and sigma 3-directions are controlled through a controller and a servo valve, confining pressure is loaded in a force control loading mode, and sample data are processed and analyzed; according to the invention, the condition of unequal coaxial stress load in the free face direction in the mining process of an underground working face can be simulated, and the actual condition of a mine can be better reflected; a video probe side pressing plate is arranged, and a binocular miniature high-speed camera is embedded into the clamp, so that the structural change of a test surface in the loading process can be recorded in real time, three-way surface strain field dynamic monitoring is realized, and the change conditions of a crack field and a strain field of a coal sample under the gas-containing condition are better reflected.

The invention discloses a concrete box girder defect automatic detection method and system based on deep learning, and the method comprises the steps: obtaining a visible light image, a laser point cloud and an acoustic emission signal of a box girder, carrying out the data fusion operation, generating new box girder data, setting a defect detection model, and according to the new box girder data, carrying out the automatic detection of the defect of the concrete box girder. Identifying cracks and damages on the box girder; setting a risk assessment model, and calculating a risk index of the box girder according to the new box girder data and cracks and damages on the box girder; and setting a plurality of preset risk grading thresholds, and performing defect risk grading on the box girder in combination with the risk index so as to complete defect detection.

The invention provides a house structure safety detection method and system based on multi-modal sensing fusion, and relates to the technical field of house structure safety detection. The method comprises the following steps: acquiring multi-modal sensing data such as vibration, acoustic emission and temperature of a key part of a house structure; performing noise reduction, normalization processing and time sequence alignment on the data; extracting dynamic response features and environmental response features; calculating a dynamic stress index (DSI) and a composite stability factor (CSF); generating a real-time safety score of the house structure based on a multi-modal data fusion algorithm; judging whether potential safety hazards exist or not according to the safety score; after the hidden danger is confirmed, alarm information is generated and pushed to a user terminal. Through multi-modal data fusion, the safety of the house structure can be accurately evaluated, real-time monitoring and early warning are provided, and the long-term stability and safety of the house structure are enhanced.