4710 results about "Acoustic wave" patented technology

The invention belongs to the technical field of bridge diagnosis, and discloses a bridge crack intelligent diagnosis system based on multi-modal data fusion. By fusing multi-source data such as visual images, sound wave detection and vibration signals, comprehensive perception and characterization of crack features are realized; constructing a bridge crack characteristic spectrum diagram by adopting a cross-modal feature extraction and heterogeneous feature coding technology; generating a crack evolution situation map based on space-time correlation analysis and knowledge graph construction; the robustness of the system in a complex environment is improved through environmental adaptability feature enhancement and multi-scale characterization; constructing a bridge safety risk hypergraph in combination with multi-dimensional risk analysis and multi-agent collaborative diagnosis; analyzing and revealing a crack evolution mechanism by applying a causal relationship; and finally, through dynamic fusion and uncertainty quantification, a crack intelligent diagnosis comprehensive report is generated. According to the system, the limitation of traditional single-mode diagnosis is broken through, and dynamic prediction and accurate risk assessment of fracture evolution are realized.

The invention provides a resource dynamic monitoring and control method and system based on quota tracking. According to the pipe gallery deformation monitoring and resource optimization system based on optical fiber and sound wave sensing, underground pipe gallery structure stress field propagation data are obtained through an optical fiber sensor array, energy attenuation characteristics of stress waves at reinforcing rib intersection points are analyzed, and a dynamic mapping model of structure deformation risks and monitoring node resource requirements is established. Meanwhile, the frequency domain propagation characteristics of abnormal vibration signals are captured through acoustic sensors arranged at the expansion joints, and a resource allocation strategy matrix is formed; and constructing a quota tracking database, comparing the actual consumption rate of the frequency spectrum quota with a predicted value, generating a resource use efficiency report, and performing frequency spectrum redistribution of frequency hopping interval optimization and transmitting power adjustment on the nodes in the high stress concentration region. According to the technical scheme provided by the invention, the resource dynamic optimization of the underground pipe gallery structure is realized, and the risk early warning precision and the communication efficiency are improved.

The invention provides an unmanned aerial vehicle multi-dimensional information fusion method and system based on acousto-optic-electric composite detection. According to the method, acoustic frequency band signals, visible light image sequences and electromagnetic field intensity change data are collected, acoustic characteristic flow, optical characteristic flow and electrical characteristic flow are generated, and based on the time deviation between a low-frequency vibration mode and transient electromagnetic pulses, multi-channel dynamic calibration is carried out on an image distortion area in the optical characteristic flow. The method comprises the following steps: acquiring an acoustic feature flow, an optical feature flow and an electrical feature flow, correcting space-time coordinate parameters of the acoustic feature flow, the optical feature flow and the electrical feature flow, generating a calibration feature flow, carrying out anti-interference fusion processing on the calibration feature flow, extracting multi-dimensional coupling features, inputting the multi-dimensional coupling features into an unmanned aerial vehicle flight path planning model, and generating a three-dimensional space situation model; according to the invention, the physical space-time consistency alignment and noise suppression of the multi-modal data are realized, and the obstacle positioning precision and the dynamic trajectory prediction real-time performance are significantly improved.

An acoustic wave device includes a support, a piezoelectric layer on the support, and an IDT electrode including first and second electrode fingers. When a thickness of the piezoelectric layer is d and a center-to-center distance of the first and second electrode fingers adjacent to each other is p, d / p is less than or equal to about 0.5. When an imaginary line connecting tips of the first electrode fingers is an envelope, a direction in which the envelope extends and the direction of the Y-axis intersect each other, and about 9°≤|α|≤about 14° is satisfied, where |α| is an absolute value of a slant angle α. The slant angle α is an angle of a corner defined by the direction in which the envelope extends and the direction of the Y-axis and is an angle other than 0°.

The invention discloses a lithium ion battery thermal runaway early warning method, and belongs to the technical field of battery safety monitoring, and the method comprises the steps: synchronously obtaining a low-frequency sound wave signal, a temperature signal, a voltage signal and a stress-strain signal of a lithium ion battery; noise reduction processing is carried out on the low-frequency sound wave signals, time-frequency feature extraction is carried out on the low-frequency sound wave signals after noise reduction processing, and feature frequency band energy corresponding to a thermal runaway early event is obtained; a thermal runaway sensitivity coefficient is calculated based on the characteristic frequency band energy, and when the thermal runaway sensitivity coefficient is larger than a first coefficient threshold value, thermal runaway early warning is triggered; and on the basis of the thermal runaway sensitivity coefficient, the temperature signal, the voltage signal and the stress-strain signal, constructing a feature vector, and inputting the feature vector into a trained long-short-term memory network and attention mechanism hybrid model for classification to obtain a thermal runaway early warning level. The method can solve the problems of installation difficulty, detection lag and high false alarm rate in the prior art.

Aspects of this disclosure relate to a bulk acoustic wave device that includes a multi-layer raised frame structure. The multi-layer raised frame structure includes a first raised frame layer positioned between a first electrode and a second electrode of the bulk acoustic wave device. The first raised frame layer has a lower acoustic impedance than the first electrode. The first raised frame layer and the second raised frame layer overlap in an active region of the bulk acoustic wave device. Related filters, multiplexers, packaged modules, wireless communication devices, and methods are disclosed.

The invention relates to the technical field of marine environment monitoring, in particular to a marine physical data intelligent fusion and optimization processing method, which comprises the following steps: collecting marine physical data in real time through various sensors, including temperature, salinity, flow velocity, tide and sound wave characteristic data; and spatial-temporal feature extraction and spatial correlation analysis are carried out by using a depth residual-graph convolution hybrid model. And performing parameter optimization on the multi-source fusion data set by using a quantum annealing optimization algorithm to generate an optimal parameter set so as to improve the fusion precision of the data. And abnormal data nodes are identified through a self-adaptive threshold detection algorithm, so that the accuracy and reliability of the data are further ensured. And finally, constructing a three-dimensional visual marine environment model according to the optimized parameter set, and realizing a dynamic mapping relationship between model parameters and physical characteristics so as to realize real-time updating and visual display. According to the method, the processing precision of the marine environment data is effectively improved, accurate marine environment prediction is provided, and the method has a wide application prospect.

The invention discloses a pile foundation integrity detection method which comprises the following steps: applying a variable amplitude sound wave excitation sequence to a pile foundation structure, collecting acoustic response signals under different excitation amplitudes, and obtaining nonlinear dynamic response data reflecting material rheological characteristics; performing frequency dispersion analysis and hysteretic characteristic analysis on the nonlinear dynamic response data, extracting a stress memory dissipation coefficient and a nonlinear memory capacity index, and establishing a time-varying characteristic curve; according to an abnormal change interval and a memory capacity fading rate of the time-varying characteristic curve, determining a defect position and a defect type, and obtaining an evolution state parameter of the defect; and based on the historical change trend of the evolution state parameters, the material degradation rate and the structure damage development rate are calculated, the health state index of the pile foundation structure is predicted, and a full life cycle performance evolution curve is formed. Through stress memory activation and nonlinear dynamic response analysis, the detection method considering the rheological property of the material is established, and the performance evolution rule of the pile foundation in the whole life cycle can be accurately evaluated.

A process for exfoliating the skin, including the scalp, and / or the nails, comprising the step consisting in subjecting a surface of said skin (K), or said nails, to a cosmetic composition and to ultrasonic acoustic waves emitted by a device comprising at least one ultrasonic transducer (13), with an acoustic intensity Isata in the region exposed to the acoustic waves that is at least equal to 0.1 W / cm2, i) the transducer being moved relative to said surface at an average sweeping speed of between 0.01 cm2 / s and 15 cm2 / s for the duration of treatment of said surface, and / or ii) the device being applied to a location on the keratin material, then moved away from the latter and applied to a different location.

A pore pressure prediction method for fine-grained hybrid sedimentary rock based on variable P-wave velocity includes: carrying out a P-wave velocity test of rock under different effective stresses, determining a function form of the relationship between acoustic velocity and effective stress, fitting the parameters in the function by optimization, and establishing an evaluation model of effective stress influence term by lithology. According to the measured formation pressure data and the effective stress influence term model, the acoustic velocity background value is deduced, the logging curve is preferred, and the acoustic velocity background value logging evaluation model is established. According to the logging curves of DT and DEN, combined with the lithology identification results, the acoustic velocity background value is calculated, and then the effective stress of the formation is obtained, and then the pore pressure is determined according to the overburden pressure.

The invention relates to the technical field of digital manufacturing, and discloses a digital twin middle station and self-healing decision system for oil and gas equipment management, which comprises a multi-source sensing terminal, a twin optimization module, a fault topology diagnosis module and an early warning execution terminal, a multi-modal synchronous fusion mechanism is constructed, and when the state of oil and gas equipment is monitored, a heterogeneous data alignment standard is formulated, and a dynamic frequency normalization rule is set for different equipment types, so that the definiteness of multi-source sensing data fusion is ensured; meanwhile, vibration, temperature, pressure and sound wave data are synchronously collected in real time, millisecond-level detection can be achieved, the data time difference problem can be eliminated, the accuracy of equipment state feature extraction is guaranteed, and the feature fusion error is further reduced; and generating a self-adaptive correction coefficient in real time by calculating the dynamic deviation of the physical model and the data model.

The invention relates to a settlement data analysis and early warning method based on a cloud platform, and the method comprises the steps: collecting settlement data of a basic part of a main transformer in real time through a high-precision electronic settlement observation device array, obtaining deformation stress data through data preprocessing and coordinate calculation, and uploading the deformation stress data to the cloud platform; transmitting an ultrasonic pulse signal to the stress concentration position of the local area through ultrasonic detection equipment, receiving reflected wave data, and identifying the depth and range of the crack initiation position according to the reflected wave data; a sensor array is arranged on the basic surface of a crack initiation position, sound wave signals released in the crack propagation process are captured, and a time sequence of the crack propagation direction and speed is determined; and detecting crack depth data of the repair priority region through ultrasonic waves, calculating an adjusted basic health state value, comparing the adjusted basic health state value with a preset safety threshold, and generating a corresponding safety early warning signal.

An ultrasonic surgical instrument includes an end effector including an ultrasonic blade, an ultrasonic waveguide, an ultrasonic transducer assembly, and a carrier. The acoustic waveguide includes at least a proximal portion thereof extending along a longitudinal axis. The ultrasonic transducer assembly includes a fixation member and is operatively connected to the acoustic waveguide such that the acoustic waveguide is in acoustic communication with an ultrasonic transducer. The carrier has at least a portion thereof engaged with the fixation member of the ultrasonic transducer assembly thereby inhibiting movement of the ultrasonic transducer assembly relative to the carrier. The carrier movably supports the ultrasonic transducer assembly along the longitudinal axis such that the ultrasonic transducer assembly is configured to move from a proximal position on the longitudinal axis to a distal position on the longitudinal axis for inserting the ultrasonic blade into a patient.

The invention provides an unmanned aerial vehicle full-band countering method and system based on acousto-optic-electric composite detection. According to the invention, through synchronous cooperative detection of sound wave sensing, optical imaging and electromagnetic spectrum analysis, a composite detection signal is generated; based on the relevance between the acoustic characteristic frequency band and the electromagnetic control frequency band, extracting the working mode characteristics of the unmanned aerial vehicle and generating dynamic interference parameters; generating a synchronous interference instruction according to the time domain modulation characteristic of the dynamic interference parameter; in combination with abnormal noise distribution characteristics captured by the acoustic module and flight path offset identified by the optical module, power distribution and frequency band coverage of the interference instruction are corrected in real time, and optimized interference parameters are generated; and finally, a dynamic interference map is generated through overlapping frequency spectrum fusion of the dynamic interference parameters and the optimized interference parameters, accurate cooperative suppression is realized, full-band real-time accurate suppression of navigation, communication and power control signals of the target unmanned aerial vehicle is realized, and the countering efficiency in a complex scene is remarkably 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 relates to the technical field of pipeline safety monitoring, in particular to a pipeline water leakage detection platform and method.The pipeline water leakage detection platform comprises a sensor layer, an edge calculation module and a pipeline water leakage detection system of a cloud analysis platform, the sensor layer comprises a pressure sensor, a sonic sensor, a flow sensor and an environment sensor, and a distributed installation structure is adopted; the edge calculation module is used for collecting pipeline state data in real time, the edge calculation module comprises an equipment box, a built-in power supply module, a data processor (ARM Cortex-M series + FPGA acceleration unit) and a communication module (NB-IoT or LoRa), and sensor data is subjected to localization preprocessing and then uploaded to a cloud analysis platform. A cascade neural network model (CNN + LSTM) and a leakage positioning engine are deployed on the cloud platform, leakage judgment and accurate positioning are achieved through multi-source data fusion and a deep learning algorithm, and efficient and accurate pipeline leakage detection and positioning are achieved through data collection of a sensor layer, preprocessing of an edge calculation module and intelligent analysis of the cloud platform.

The invention discloses an unmanned aerial vehicle positioning method based on a Bayesian network, and the method comprises the steps: collecting unmanned aerial vehicle data, carrying out the preprocessing, obtaining sound wave data, motion data, infrared image data and environment data, carrying out the spectrum analysis based on the sound wave data, obtaining sound spectrum time sequence data, and carrying out the positioning of the unmanned aerial vehicle. Constructing a probability density thermodynamic diagram and extracting a first range through a sparse variational Gaussian process according to time, frequency and energy three-dimensional information of the sound spectrum time sequence data and environment data, and obtaining first positioning data through a Bayesian network according to the first range and motion data, and performing adaptive wavelet down-sampling on the infrared image data and the sound spectrum time sequence data in the lateral direction to obtain lateral positioning data, and performing deviation correction on the first positioning data by using the lateral positioning data to obtain second positioning. According to the method, through space-time synchronization, adaptive processing and probabilistic reasoning of sound spectrum time sequence data, the positioning precision and stability in a complex environment are improved.

The invention discloses a building sound wave reflection path analysis and noise source positioning method and system, and the method comprises the steps: S1, arranging a plurality of sound sensors according to the geometric structure of a target building, collecting sound signals, carrying out the signal sampling of the sound signals, and storing the sound signals in the form of a time sequence; s2, preprocessing the sound signal, extracting a time difference matrix, a frequency spectrum feature and an energy feature, merging into a three-dimensional time sequence feature, and normalizing to form a three-dimensional time sequence feature matrix; s3, establishing an LSTM network, and predicting a three-dimensional position and a sound wave reflection path of an output noise source; s4, optimizing and calculating a sound wave reflection path by using the geometric structure data of the target building, and generating a three-dimensional reflection path diagram; and S5, dynamically displaying a noise source position and a sound wave reflection path according to the three-dimensional reflection path diagram. According to the invention, high-precision three-dimensional positioning of a noise source can be realized in a complex building environment, accurate modeling is realized, and propagation and reflection paths of sound waves in a building are analyzed.

Aspects of this disclosure relate to bulk acoustic wave devices that have a raised frame structure, and filters that utilize the bulk acoustic wave devices. The raised frame structure can include a first raised frame layer that has a relatively low acoustic impedance. The raised frame structure can include a second raised frame layer that has a relatively high acoustic impedance. The first raised frame layer can extend inward further than the second raised frame layer. A width of the first raised frame layer that overlaps the first and second electrodes is between about 1.5 times to about 4 times larger than the combined thickness of the first electrode, the piezoelectric layer, and the second electrode.

The present disclosure provides an acoustic output device. The acoustic output device may comprise a bone conduction speaker configured to generate bone conduction acoustic waves. The acoustic output device may also comprise an air conduction speaker configured to generate air conduction acoustic waves, the air conduction speaker being independent of the bone conduction speaker. The acoustic output device may further comprise at least one housing configured to accommodate the bone conduction speaker and the air conduction speaker.

The invention discloses a karst region water and soil loss monitoring method and system. According to the monitoring method, a water and soil loss risk prediction result of a target area is obtained based on a deployed acoustic sensor array, the acoustic sensor array is deployed at a key monitoring point of the target area to be monitored, and acoustic wave signals of the earth surface and underground environment are collected at the same time; acoustic wave signals detected by the acoustic sensor array are collected, and the acoustic wave signals are converted into frequency domain characteristic data; inputting the frequency domain feature data into a pre-trained acoustic mode recognition model, and recognizing acoustic features related to water and soil loss; based on an output result of the acoustic mode recognition model, judging the water and soil loss type, degree and development trend of the monitored area, and generating a water and soil loss risk prediction result; according to the invention, real-time and continuous monitoring of the water and soil loss process in the karst region is realized through an acoustic monitoring means, the monitoring precision and early warning timeliness are improved, and a scientific basis is provided for water and soil conservation management.

Aspects of this disclosure relate to an acoustic wave device having an overtone mode as a main mode. The acoustic wave device is sufficiently asymmetric on opposing sides of a piezoelectric layer over an acoustic reflector such that the main mode of the acoustic wave device is the overtone mode.

An underwater acoustic sensor network node positioning error correction method combined with acoustic velocity inversion, which method relates to the technical field of acoustic velocity inversion. The method comprises: deploying an acoustic source point and underwater acoustic sensor nodes which are at fixed positions, the underwater acoustic sensor nodes collecting acoustic velocity data sent by means of the acoustic source point, and by means of acoustic velocity data at different underwater acoustic sensor nodes, using an acoustic velocity field inversion algorithm to construct an acoustic velocity field model; collecting real-time acoustic velocity data at the different underwater acoustic sensor nodes, and obtaining acoustic velocity characteristic information at the different underwater acoustic sensor nodes and water characteristic information at the different underwater acoustic sensor nodes; performing comprehensive analysis on the acoustic velocity characteristic information and the water characteristic information, and evaluating the positioning accuracy of the acoustic velocity field model in respect of the underwater acoustic sensor nodes; and comparing quantized positioning accuracy results for the underwater acoustic sensor nodes with a preset threshold, and when there is a relatively large error in the positioning accuracy, generating an error signal. The method is conducive to improving the precision of underwater acoustic wave propagation and the accuracy of underwater acoustic sensor network node positioning, and evaluating the positioning accuracy of an acoustic velocity field model in respect of underwater acoustic sensor nodes.

Various embodiments of an ear-wearable electronic device are disclosed. The device includes a housing having a top shell and a bottom shell, a microphone disposed within the housing, and an acoustic path disposed at least partially within the housing and extending between an acoustic port disposed at an outer surface of the housing and an outlet disposed within the housing that acoustically couples the acoustic path to an inlet of the microphone. The device further includes an acoustic filter disposed at least partially within an inner surface of the top shell and includes a neck and a resonance cavity acoustically coupled to the neck. The acoustic filter is acoustically coupled to the acoustic path via the neck. Further, the acoustic filter is configured to reduce an intensity of acoustic waves sensed by the microphone in a first frequency range.

A packaged acoustic wave component has a device substrate and a metal layer disposed over the device substrate. An acoustic wave device is disposed over at least a portion of the metal layer so that the metal layer is interposed between the device substrate and at least a portion of the acoustic wave device. A cap substrate is spaced above the device substrate, and peripheral wall that is attached to and extends between the device substrate and the cap substrate, the peripheral wall surrounding the acoustic wave device. One or more vias extend through the device substrate and are disposed under the metal layer.

The invention provides an ionic type rare earth ore bed rock crack detection method and system, and the method comprises the steps: laying an active seismic source at a preset position of a target detection region, and enabling the active seismic source to emit an elastic wave with a preset frequency and a preset amplitude; a micro-seismic sensor network arranged in a target detection area collects sound wave signals which are emitted by the active seismic source and are scattered and polarized by the crack; extracting a plurality of crack characteristic parameters related to the bedrock crack from the bedrock crack microseismic sound wave signal, and performing preliminary analysis to obtain a possible crack region and preliminary key parameter information of the crack; based on the multiple fracture characteristic parameters, a multi-parameter joint inversion method is adopted to construct a fracture model containing fracture geometric parameters and physical parameters, and regularization optimization is carried out on the fracture model; and finally, based on the optimized crack model, carrying out inversion to obtain key parameters of high-precision spatial positions and distribution of the cracks. On the basis that the ore body is not damaged, the bedrock crack detection precision can be improved.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention relates to the technical field of termite monitoring and killing, in particular to a termite monitoring and killing method based on multi-modal sensing and a dynamic threshold value, and adopts the technical scheme that multi-source signal acquisition is adopted, and biological activity signals of a target area are synchronously acquired through a vibration sensor, a sonic sensor, a chemical gas sensor and a thermal imaging sensor; the anti-interference performance is improved, so that the false alarm rate is reduced; environmental parameter compensation is carried out, temperature, humidity and soil conductivity data are collected in real time, a dynamic calibration coefficient is generated to correct sensor original data, and environmental adaptability is improved; the method comprises the following steps: acquiring data of a current area, performing dynamic threshold calculation, inputting the acquired data into an LSTM-Transformer hybrid network, and outputting a self-adaptive early warning threshold of the current area so as to reduce the possibility of leak detection; through multi-stage early warning judgment, the real-time performance is improved, and the early warning delay is reduced; and starting a corresponding disposal process according to the early warning level, performing response execution, and narrowing the spraying range, thereby reducing pollution.

The invention discloses a method and a system for estimating the pile length of an engineering pile. The method comprises the following steps: acquiring a multi-source detection signal reflecting the striking response of a pile hammer and stratum prior information; on the basis of stratum prior information, carrying out physically-driven adaptive analysis on sound wave components in the multi-source detection signals, and extracting preliminary sound wave reflection features; performing correlation verification on the preliminary sound wave reflection characteristics by using vibration components in the multi-source detection signals to generate corrected reflection characteristics; and the signal quality of the corrected reflection characteristics is evaluated, an intelligent hammering control instruction is generated according to the signal quality so as to execute closed-loop enhanced signal excitation, finally, multi-modal information before and after enhancement is fused, and a final pile length estimated value is obtained through calculation. Through sound and vibration coupling verification and intelligent active excitation, the accuracy, reliability and detection depth of pile length estimation under the conditions of strong noise, complex stratum and large depth are improved.

The invention relates to the field of civil engineering structure health monitoring, in particular to a subsea tunnel damage intelligent monitoring and identification method based on acousto-optic collaborative perception. The method sequentially comprises the steps of deploying a three-dimensional acousto-optic sensing network, actively detecting sound waves, reconstructing a local sound velocity distribution cloud picture, identifying a sound velocity abnormal region, performing optical fiber response focusing sampling and establishing an intelligent damage diagnosis model. According to the method, the three-dimensional acoustic array and the distributed optical fiber grid are simultaneously deployed in the subsea tunnel structure, functional complementation is realized between the three-dimensional acoustic array and the distributed optical fiber grid, seamless cooperative sensing of crack coarse positioning and strain fine positioning is further realized, acoustic active detection, trigger type optical fiber high-frequency sampling and a temperature-strain decoupling algorithm are adopted, and the positioning precision of the subsea tunnel structure is improved. Seawater noise and thermal expansion interference are effectively eliminated, 'dry cracks' and'water seepage cracks' are accurately distinguished, real-time monitoring is carried out on a subsea tunnel structure, whole-process identification of micro-damage-expansion-failure is achieved, and an active early warning mechanism is established.