1038 results about "Wave field" patented technology

The invention belongs to the technical field of marine environment prediction, and discloses a ship navigation sea wave dynamic space-time prediction method and system based on deep learning. The method comprises the following steps: carrying out space-time alignment, missing value repair and standardization processing on acquired ship AIS data and an ERA5 reanalysis data set, and generating node feature vectors containing latitudes and longitudes, timestamps, wind speeds and significant wave heights; through node feature coding, centrality coding, space coding and time coding, ship trajectory node importance and time-space interaction relation are quantified. Constructing a SeaGraph model, and outputting an effective wave height prediction value of a target waypoint; and performing verification. According to the method, the space-time constraint of a traditional static modeling framework is broken through, the advantages of a self-attention mechanism and a graph network are integrated, the predictive modeling capability of dynamic evolution of a wave field in front of a ship navigation track is enhanced, and high-precision sea wave forecasting support is provided for intelligent ship navigation under complex sea conditions.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention belongs to the technical field of ocean observation, and discloses an ocean wave field reconstruction method and device based on binocular vision. An ocean wave field reconstruction method based on binocular vision comprises the steps that internal parameters of a camera are calibrated through a Bouguet calibration algorithm, a binocular camera provided with an IMU sensor is used for collecting images and pose information, feature points are extracted through an SURF algorithm, feature matching is completed under a game theory framework, and abnormal points are removed through an RANSAC algorithm. Then, camera parameters are calculated based on an eight-point method, and the camera parameters and three-dimensional point coordinates are optimized through an SBA algorithm; thirdly, generating a disparity map by adopting epipolar correction and an SGM algorithm, and performing morphological processing on the disparity map; and the processed image is subjected to triangulation to generate a three-dimensional point cloud, and filtering is carried out. And finally, estimating a sea level position through an RANSAC algorithm, and optimizing a result through a least square method to complete three-dimensional reconstruction of ocean waves.

The invention provides a multi-physics field cooperative regulation and control method in coal mine rock burst prevention and control, and relates to the technical field of coal mine rock burst prevention and control, and the method comprises the steps: synchronously collecting stress field, fracture field and vibration wave field data in real time through a distributed optical fiber sensor network and a micro-seismic monitoring system, and constructing a dynamically updated three-dimensional geomechanical model; based on an adaptive Kalman filtering algorithm, noise reduction multi-source data are fused, a dynamic stress concentration factor and an energy accumulation critical index are extracted, and a rock burst risk probability model is established; a multi-objective optimization algorithm is adopted to generate graded regulation and control instructions of grouting reinforcement, mining speed adjustment and pressure relief drilling, and the graded regulation and control instructions are executed in real time; and iteratively optimizing the model weight by using a transfer learning algorithm in combination with a historical case library to form a closed-loop feedback adaptive prevention and control system. According to the method, the analysis precision of rock mass fracture evolution under complex geological conditions is improved through multi-physics field collaborative perception and dynamic modeling, and risk quantification and real-time regulation and control are realized through multi-algorithm coupling analysis.

The invention relates to the field of floating type fan load simulation, in particular to a floating type fan simulation analysis method considering atmospheric stability in a stormy wave environment, which comprises the following steps of: simulating WRF wind field data; carrying out WRF-SWAN wave field simulation; calculating key parameters of wind field simulation; carrying out WRF-TurbSim wind field simulation; and carrying out OpenFAST simulation and analysis. On the basis of traditional wind field driving, a Richardson number calculation and atmospheric stability classification method is introduced, a spectrum inversion and adaptive fitting mechanism is combined, key parameters of an engineering wind speed spectrum model are extracted, and a parameter mapping relation between WRF output and TurbSim input is established. A disturbance construction module driven by Richardson numbers is developed in TurbSim, adaptive generation of wind speed disturbance under the multi-stability condition is achieved, and the physical consistency and engineering adaptability of a simulated wind speed field on the aspects of a section structure and spectrum characteristics are improved. And finally, finishing floating type fan load analysis under wind wave coupling input by combining with an OpenFAST platform, and supporting system response evaluation and structural design optimization under complex sea conditions.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention discloses a road cavity volume calculation method and device based on a three-dimensional FK migration algorithm, equipment and a medium, and relates to the technical field of road cavity detection, and the method comprises the steps: obtaining three-dimensional ground penetrating radar data, and carrying out the preprocessing of the data, and obtaining target radar data; converting the target radar data to a frequency wavenumber domain to obtain frequency wavenumber domain radar data; establishing a road hierarchical velocity model, and performing hierarchical wave field extrapolation and double weight correction based on the frequency wave number domain radar data and the road hierarchical velocity model to obtain corrected wave field data; performing sparse constraint optimization imaging processing on the corrected wave field data to obtain a target imaging result; and carrying out boundary extraction processing on the target imaging result to obtain a three-dimensional cavity boundary point set, and carrying out voxelization processing based on the three-dimensional cavity boundary point set to obtain a road cavity volume. According to the invention, high-precision restoration of the shape of the road cavity and automatic extraction of the volume can be realized in three-dimensional GPR imaging.

The invention discloses an intelligent analysis system for ultrasonic phased array detection data of a welding seam, and relates to the technical field of nondestructive testing, and the intelligent analysis system is characterized in that a four-dimensional wave field tensor is constructed by collecting full-waveform ultrasonic data under multi-channel, multi-path and multi-angle conditions; extracting reflected signals which keep time coherence in a discontinuous path, and generating a high-dimensional coherence feature matrix; inputting the features into a self-supervised contrast learning model to obtain a defect semantic embedding vector; recognizing a suspected weak defect area based on the distribution density and the boundary change trend, and performing reverse beam focusing in combination with original data to obtain a defect three-dimensional positioning map; the shape bifurcation index and the boundary stability are calculated through topological analysis, and intelligent discrimination of artifacts and microcrack defects is achieved; the method does not need label data, has high automation and robustness, and is suitable for weak defect identification of complex structure welding seams.

The invention discloses a novel noise HV spectrum ratio imaging method based on polarization. The method comprises the following steps: collecting background noise data in a predetermined monitoring area; preprocessing the acquired background noise data, wherein the preprocessing comprises data format conversion, data splicing, mean value removal, trend removal, waveform pinching and band-pass filtering processing; preprocessed data are processed by adopting a polarization analysis technology, a wave field is separated by setting a threshold value and analyzing a power spectrum, and a background noise area is divided into a linear body wave component and a nonlinear surface wave component. According to the polarization-based noise HV spectrum ratio imaging method, the precision and efficiency of underground structure analysis are remarkably improved through the precise separation of the wave field and the automatic processing flow of the HV spectrum ratio, and by means of polarization analysis optimized data processing, the automatically generated frequency and depth profile and the automatically picked excellent frequency and peak value, the imaging precision and efficiency of the underground structure are greatly improved. And the analysis of the underground velocity structure and the wave impedance interface is more accurate.

The invention discloses an urban karst water guide channel detection system and method based on earthquake Krauklis waves. The urban karst water guide channel detection system comprises a piezoelectric type vibroseis, an array type earthquake receiving system and a multi-wave seismograph. The piezoelectric vibroseis is used for exciting seismic waves; and the multi-wave seismograph is used for acquiring seismic data acquired by each seismic acceleration sensor in the array type seismic receiving system. The structure can ensure the precision of seismic wave data acquisition each time; separating and extracting Krauklis waves from the acquired seismic wave data by adopting an energy-frequency double-selection method; then, based on the frequency dispersion characteristic of Krauklis waves, a mapping model of crack opening and wave field response is established; and finally, determining the opening degree of the urban karst water guide channel according to the mapping model, and performing inversion to obtain the dynamic flow of the urban karst water guide channel. Through treatment in the mode, the opening degree and the dynamic flow of the urban karst water guiding channel can be monitored, and finally prevention and control over hidden geological disasters are effectively improved.

The invention relates to the technical field of deep and far sea small island health monitoring, and discloses a deep and far sea small island health monitoring system and method. A non-intrusive data acquisition system composed of a sensor network, a shore-based camera device and a vibration sensor is adopted, and total-factor real-time monitoring of open sea environment parameters, reef images and the vibration state of a protective building is achieved. Through joint operation of a wave field distribution model and a structural damage prediction model, actually measured data and numerical simulation are combined, and the temporal-spatial resolution of wave parameters of the reef and the evaluation precision of the health state of a protective building are improved; the early warning threshold value is dynamically adjusted through deviation analysis of real-time association of historical disaster data and the current monitoring value, and the timeliness and accuracy of early warning of extreme events are improved; finally, through an automatic triggering mechanism of a multi-stage early warning signal, all-weather and high-reliability safety monitoring guarantee is provided for deep and far sea islands, meanwhile, the manual maintenance cost is reduced, and a remarkable technical synergistic effect is achieved.

The invention relates to the technical field of reservoir prediction, in particular to a high-resolution reservoir prediction method under seismic full-wave information mining. The method comprises the following steps: acquiring seismic full-wave information data; performing frequency domain conjoint analysis on the seismic full-wave information data to generate a multi-scale full-wave feature tensor; performing seismic full-wave signal multi-scale decoupling on the multi-scale full-wave feature tensor to generate seismic multi-wave field separation data; implicit reservoir feature mapping is carried out on the seismic multi-wave-field separation data to generate initial reservoir feature mapping data; performing nonlinear correlation analysis on the initial reservoir feature mapping data to generate high-order reservoir feature data; and performing inversion linkage optimization on the high-order reservoir feature data and the seismic multi-wave-field separation data to generate reservoir dynamic response optimization data. According to the method, through multi-wave-field information extraction, high-resolution reservoir detail display, dynamic reservoir change prediction and confidence evaluation, the accuracy and resolution of reservoir prediction are improved.

The invention discloses a VSP wellbore wave suppression method for improving U-Net based on physical constraints, which comprises the following steps: introducing apparent velocity constraints of an F-K transform domain, constructing physical constraint conditions, simultaneously realizing time sequence feature extraction and prediction of wellbore waves in combination with a GRU network, and learning a mapping relation between a noise image and a clean image by using the network so as to improve the suppression efficiency of the wellbore waves. The details and the structure of a noise image are accurately restored through a training network, the difference between a real de-noised image and network output is minimized so as to achieve the purpose of image de-noising, the model retains effective signals through a residual learning mechanism, and meanwhile precise suppression of shaft wave noise is achieved. The method provided by the invention can accurately identify and effectively suppress wellbore waves and reflected waves thereof in a complex VSP wave field, significantly improve the signal-to-noise ratio, reduce residual noise, improve data processing efficiency while improving data quality, provide technical support for high-precision seismic imaging under a complex geological condition, and have a wide application prospect. And meanwhile, a feasible solution is provided for popularization and application in an actual work area.

The invention discloses a power prediction method and equipment of a wave energy device, a medium and a product, and relates to the field of output power prediction.The method comprises the steps that water depth topographic data and wind field data of a target sea area are obtained; solving a wave field at a wave energy device by adopting an improved wave energy balance control equation according to the water depth topographic data and the wind field data; according to the wave height and the wave period of the wave energy device, determining the output power of the wave energy device by adopting a quadratic linear interpolation method, and taking the output power as initial output power; and according to the wave direction of the wave energy device, correcting the initial output power by using a directivity weighting function to obtain corrected output power. The output power prediction accuracy of the wave energy device can be improved.

The invention discloses a hybrid nonlinear floating fan hydrodynamic load coupling calculation method. The method comprises the steps of obtaining a velocity potential of a wave field in a computational domain through OceanWave3D numerical simulation (a wave field calculation model), and performing post-processing to obtain wave field data; dividing an instantaneous wet surface according to the six-degree-of-freedom motion, calculated based on an OpenFAST (floating fan calculation model), of the floating fan in each time step, and performing pressure integration to obtain a Front-Krylov force; updating the hydrostatic stiffness matrix according to the real-time position change of the buoyancy center of the fan, and calculating hydrostatic restoring force; linear radiation force and diffraction force are calculated through time domain convolution of frequency domain data, nonlinearity of the radiation force and the diffraction force is considered based on an instantaneous wetting volume change coefficient, and hydrodynamic load and structural motion response calculation in the current time step is completed after the OpenFAST is coupled. According to the method, coupling calculation between the open source code OpenFAST and OceanWave3D is achieved, and compared with commercial software at the present stage, the method is more accurate in wave load calculation under the extreme wave sea condition.

The invention relates to a complex medium inversion imaging method and system guided by a physical information neural network, and the method comprises the steps: constructing a two-dimensional grid model of a block structure with different medium distributions, obtaining out-of-plane displacement data at grid points through staggered grid finite difference, and forming a wave field full-waveform data set; constructing a physical information neural network, wherein the physical information neural network comprises a first feed-forward network for modeling a mapping relation of a displacement field of the sample, a second feed-forward network for modeling a velocity field of the sample, and a residual network; based on the wave field full-waveform data set, training a physical information neural network by taking minimization of compound loss as a target; and taking the space-time coordinates of the sample as input, and utilizing the trained physical information neural network to obtain predicted displacement and sound velocity of the corresponding position of the space-time coordinates, so as to realize inversion imaging of sound velocity medium distribution. According to the method, the ultrasonic propagation model of the complex medium can be quickly and accurately established under the condition that a small amount of marked data is used.

The invention provides an electromagnetic ultrasonic bolt stress detection system based on a space-time complementation algorithm, and the system comprises the steps: carrying out the multi-channel ultrasonic signal collection of a to-be-detected bolt, and obtaining a multi-channel echo signal group; performing time-varying attenuation compensation and adaptive entropy weight denoising on the multi-channel echo signal group to obtain a denoised echo signal group; extracting echo time-domain features from the de-noised echo signal group, performing segmented time-frequency feature extraction on the de-noised echo signal group based on a short-time sliding window to obtain echo time-frequency features, and fusing the echo time-domain features and the echo time-frequency features into ultrasonic time-frequency features; performing wave field reconstruction on the de-noised echo signal group to obtain a spatial ultrasonic energy diagram, and extracting ultrasonic spatial features of the spatial ultrasonic energy diagram based on a self-attention map convolutional network; performing correlation detection and space-time complementary modeling on the ultrasonic time-frequency features and the ultrasonic space features to obtain ultrasonic space-time joint features, and performing stress analysis on the ultrasonic space-time joint features to obtain bolt stress data.

The invention discloses a sound wave full waveform inversion method and system based on optimization effective boundary storage, and the method comprises the steps: constructing an initial geological velocity model, and obtaining seismic wave parameter information; performing forward modeling according to the initial geological velocity model and the seismic wave parameter information to obtain seismic simulation data and forward seismic source wave field data; acquiring actual seismological observation data of the geologic model to be measured, and constructing an objective function according to the actual seismological observation data and the seismic simulation data; re-sampling the wave field data of the target area according to the forward seismic source wave field data; restoring the resampled wave field data of the target area to an original data state by using an interpolation method, and then reconstructing a forward transmission seismic source wave field; zero-delay cross-correlation is carried out according to the reconstructed forward transmission seismic source wave field and the reconstructed backward transmission residual wave field to obtain a gradient; and carrying out iterative updating on the target function by adopting a gradient optimization algorithm until a final geological velocity model is output.

The invention belongs to the technical field of tunnel engineering geophysical prospecting, and particularly relates to a full-wave-field tunnel advanced prospecting method based on rock physical constraint, which comprises the following steps of: constructing an initial geologic model, sampling on an upper interface of a layer section where a tunnel is located and carrying out longitudinal and transverse wave velocity measurement to obtain a longitudinal wave velocity Vp and a transverse wave velocity Vs; combining all sampled longitudinal wave velocities Vp and transverse wave velocities Vs, calculating longitudinal wave velocities and transverse wave velocities of a target layer section, obtaining elastic parameters of the target layer section, importing the obtained elastic parameters into an initial geologic model, obtaining a parameter inversion initial model, constructing a forward wave field and a simulated seismic record, and making a difference between an actual observation record and a simulation record, so as to obtain an initial geologic model; and conjugate gradient inversion is carried out. According to the method, all wave field information excited by the shield tunneling machine is fully utilized, and high-precision parameter inversion is carried out under rock physical constraints, so that more accurate tunnel front geological prediction can be provided, and a scientific basis is provided for safe and efficient construction of the shield tunneling machine.

The invention belongs to the technical field of intelligent sensing, and relates to a micro-seismic monitoring method for a ground moving target in a sensitive area, which comprises the following steps: collecting five types of moving target seismic signals, and constructing a data set; preprocessing the data; constructing an interpretable convolution kernel based on a scalar wave field equation, and realizing adaptive extraction of key physical parameters and nonlinear dimensionality reduction of a high-dimensional feature space by embedding a partial differential operator to form compact feature representation with physical traceability; forward diffusion is adopted to carry out progressive Gaussian noise enhancement on the noisy features; designing a Transform architecture with a multi-head self-attention mechanism and position coding, capturing time-space correlation characteristics of vibration signals through context modeling, and realizing end-to-end mapping from physical characteristics to classification decision in combination with a multi-layer perceptron; and carrying out training and event decision making on the model, and outputting a decision classification result. According to the method, the identification accuracy of the seismic oscillation signal of the moving target can reach 95.2%, and the robustness is remarkably improved.

The invention relates to the technical field of tunnel engineering, and discloses a tunnel earthquake three-dimensional full-waveform inversion method, which comprises the following steps: S1, establishing a three-dimensional initial velocity model: establishing the three-dimensional initial velocity model based on a common reflection point migration method of ellipsoid expansion; s2, forward modeling three-dimensional full-space wave field characteristic analysis: performing forward modeling in the established typical unfavorable geologic body model, setting a seismic source point on a tunnel wall or a tunnel face, setting a detection point on the tunnel wall, and analyzing three-dimensional full-space wave field signal characteristics received at the positions of the tunnel wall and the tunnel face; s3, full-waveform inversion numerical simulation: realizing a full-waveform inversion numerical model experiment on the basis of the established initial velocity model to obtain simulation data; s5, performing three-dimensional full-waveform inversion imaging; and S6, result verification. The tunnel earthquake three-dimensional full-waveform inversion precision can be improved, and the tunnel earthquake three-dimensional advanced prediction method which is low in cost, safe, efficient and high in precision is achieved.

The invention discloses a method and a system for predicting the propagation direction of a seismic wave field excited by a high-speed rail seismic source. The method comprises the following steps: simplifying the high-speed rail seismic source into vertical downward impact force of wheels on the ground; taking the Ricker wavelet as a deformation function of force, summing all wheels of the high-speed rail train to obtain a high-speed rail sub-seismic source function, and performing Fourier transform to obtain an amplitude spectrum; the method comprises the following steps: modeling uniformly arranged sleepers or piers of a high-speed rail line into a uniform linear array, exciting seismic waves at each array element in the uniform linear array, and summing seismic wave signals of each array element at a far-field receiving point to obtain an array factor; a radiation factor formula of the P wave and the S wave of the seismic wave field of the high-speed rail seismic source is obtained by combining the radiation pattern factors of the seismic P wave and the S wave excited by the single-point concentrated force; drawing a theoretical interference directional diagram of seismic waves; and establishing a model for simulation, comparing a theoretically predicted interference pattern with a simulation data wave field snapshot, and keeping the predicted direction consistent with the simulated wave field direction, thereby verifying the rationality and correctness of the proposed method.

The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a floating type photovoltaic array power generation performance evaluation method, which comprises the steps of constructing an offshore area environment model; a Boussinesq model is adopted to calculate a real near-shore wave field; constructing a matching surface on the outer side of the array, extracting wave field time domain information, and converting the wave field time domain information into a frequency domain component; solving the six-degree-of-freedom motion response of the array through a boundary element method by taking the component as a boundary condition; calculating the dynamic attitude of the assembly, the solar incident angle, the out-of-plane irradiance, the working temperature and the instantaneous output power in real time based on the motion response; and finally evaluating the wave-induced power generation loss. According to the method, the Boussinesq model and the boundary element method are coupled in a domain nested mode, balance is achieved between calculation efficiency and precision, key environmental factors such as non-constant water depth, topographic relief and shoreline effect are synchronously considered, 'hydrodynamic force-light receiving-power generation 'chain coupling is achieved for the first time, and reliable support is provided for engineering design and performance evaluation of a near-shore floating type photovoltaic array.

The invention discloses a deep rock mass fracture imaging method and system based on micro-seismic monitoring, and belongs to the technical field of geophysical exploration, and the method comprises the steps: collecting multi-channel micro-seismic event waveform data, obtaining a main seismic phase direct waveform and a scattering tail waveform through wave field separation, and carrying out the imaging of a deep rock mass fracture. Determining a three-dimensional space coordinate and an energy distribution form of the seismic source through joint inversion, carrying out clustering analysis, and constructing an initial three-dimensional space distribution form; each seismic source is regarded as an active excitation source, a scattering tail wave waveform is regarded as a reflection signal, scatterer three-dimensional point cloud data is generated through reverse time migration processing, analysis is carried out in combination with an initial three-dimensional space distribution form, and an enhanced fracture structure is formed; and finally, through topology analysis and orientation clustering, discrete scatterers are eliminated, and a fracture development map is generated. According to the method, a technical path of combining a main seismic phase direct wave inversion seismic source and a scattering tail wave inversion scatterer is adopted, high-precision and high-fidelity imaging of a deep rock mass movable fracture can be realized, and physical activity is revealed.

The present invention relates to the field of nondestructive testing technology and provides a composite material damage imaging method based on the spatiotemporal cross-correlation of ultrasonic guided wave wavefields. The method comprises the following steps: S1, acquiring a wavefield signal of the plate being tested; S2, performing narrowband signal extraction on the wavefield signal; S3, filtering the wavefield signal after narrowband signal extraction; S4, shifting the wavefield signal to obtain wavefield signal cross-correlation features; and S5, using a convex hull algorithm to obtain damage imaging results. This method innovatively extracts the difference coefficient between wavefield signals as a basis for damage location, providing a new approach to feature extraction algorithms based on the time-frequency domain. This method enables automated identification of composite material delamination damage and is sensitive to both surface and internal damage.

The invention belongs to the technical field of advanced detection, and provides a shield tunnel optical fiber earthquake forecasting method and system, and the method comprises the steps: obtaining the carrying position and number of seismic wave excitation seismic source equipment based on a shield tunneling machine structure type, installing a pneumatic hammering seismic source, calculating the dynamic correlation between the air pressure and the hammering force, and determining the shield air pressure range. Determining the relative spatial position of the seismic source and the optical fiber sensor, and establishing an observation mode; according to the relative spatial position and the observation mode, arranging an optical fiber sensor in a spatial range behind the shield tail of the shield tunnel, controlling a pneumatic hammering seismic source to hammer surrounding rock to generate seismic waves to obtain geological forecast data, preprocessing the obtained geological forecast data to obtain geological wave field data, and calculating the geological wave field data according to the geological wave field data. And calculating rock mass mechanical parameters, a reflection coefficient and a stress gradient of the shield excavation surface, obtaining a three-dimensional image, carrying out feature analysis, obtaining a disaster-causing structure position, a disaster-causing structure scale and an underground water occurrence condition, and completing prediction of the shield tunnel optical fiber earthquake.

The invention provides an aero-engine blade damage assessment method based on an elastic metamaterial structure, and belongs to the technical field of nondestructive testing, and the method comprises the steps: S1, determining the size of a detection region according to an actual detection demand; s2, determining the size of the metamaterial array; s3, determining the structure of the metamaterial unit; s4, calculating a meta-material unit energy band diagram through a finite element method, and determining an excitation signal frequency; s5, arranging a metamaterial array and a sensor array; and S6, calculating pixel values of all the detection areas by adopting a DAS algorithm, carrying out positioning evaluation on the damage according to the pixel value distribution condition of the detection areas, and generating an image. According to the method, the wave field of the Lamb wave in the variable-thickness blade is controlled by using the elastic metamaterial structure with a special size, so that efficient and accurate positioning and damage identification of various types of damages are realized.

The invention provides a cable fault diagnosis method and system based on multi-modal composite fusion. The method comprises the following steps: synchronously acquiring and performing time-space alignment on distributed acoustic, temperature and high-frequency pulse signals along a cable path; the acoustic signal is decoupled into an internal event channel and an external interference channel by time domain polarization analysis. And calculating the acoustic nonlinear index of the candidate event in the internal channel, dynamically correcting the judgment threshold value of the candidate event by using the real-time temperature signal, and performing cross-modal correlation verification with the high-frequency pulse signal so as to confirm that the candidate event is an electrical discharge event. And if the judgment result is true, an acoustic wave field data array of the event is extracted, and a passive synthetic aperture focusing algorithm is applied to reconstruct a two-dimensional morphological acoustic image of the fault source. And finally, integrating all information to generate a comprehensive diagnosis report. The technical problem that a traditional distributed sensing method can only provide one-dimensional fault positioning and cannot perform high-confidence qualitative and multi-dimensional morphological representation on the fault is solved.

The invention provides a mine vibration safety detection method and system during mining blasting. The method comprises the following steps: acquiring three-component signals of explosion fluctuation at a plurality of sampling points in a warning range through an intelligent sensor network during mine blasting; decomposing each three-component signal into a plurality of attenuation waves, determining cross correlation coefficients of the attenuation waves in the same component direction among different sampling points, and performing reverse time back propagation on each sampling point according to all the cross correlation coefficients to obtain a reverse time wave field of blasting vibration at each sampling point; inputting all the reverse time wave fields into a pre-trained multi-seismic-source positioning model to obtain a plurality of explosion points; and determining the risk value of miss-fire of each blast hole in the detonation network according to the coordinate positions of all the explosion points, and performing explosion aftershock evaluation on the mine according to all the risk values. According to the scheme, the miss-fire risk of each blast hole can be evaluated after mine blasting.

The invention provides a karst cave group detection method and device in shield construction, and the method achieves the comprehensive detection of karst caves through a transient electromagnetic and sound wave array composite wave field detection mode, and improves the detection accuracy and detection efficiency of the karst cave group. And feature extraction is performed on the composite wave field detection data through a cross-physical coupling model, a karst cave probability model is established, and quantitative evaluation of a karst cave detection result is realized. When the karst cave possibly exists in the construction direction of the shield tunneling machine, the distribution prediction direction of the karst cave is further determined, and the shield tunneling machine is prevented from entering the karst cave area blindly in the tunneling process. Through the multi-source sensing array carried on the shield tunneling machine, equipment parameters during operation of the shield tunneling machine are collected in real time, potential construction anomalies can be found easily, and the accuracy and timeliness of karst cave detection are improved. Combined analysis is carried out on the composite wave field detection data and the operation parameters of the shield tunneling machine through the risk assessment model, a karst cave risk prediction value is determined, and the accuracy of karst cave risk assessment is improved.