1531 results about "Optical fiber sensing" patented technology

The invention relates to the technical field of reservoir earth and rockfill dam leakage abnormity safety monitoring and early warning, in particular to an earth and rockfill dam leakage abnormity real-time monitoring and early warning system based on deep learning and a medium. The system comprises a data sensing transmission module, a data fusion processing and analysis module, an early warning evaluation module, a system management and maintenance module, a database management module and an emergency response command module. Through a well-ground collaborative full-dimensional electrical method and shallow earth surface and full-section distributed optical fiber sensing, the system collects and transmits multi-source data. And multi-mode fusion and a deep learning algorithm are adopted to realize multi-physical field feature extraction and three-dimensional modeling. The system generates graded early warning information based on dynamic threshold and multi-factor coupling, and realizes automatic real-time monitoring, intelligent early warning and efficient management of leakage abnormity of the earth and rockfill dam in combination with a database, management maintenance and emergency response functions. According to the invention, the accuracy of earth and rockfill dam leakage abnormity identification and the intelligent level of early warning are improved.

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

The invention relates to the technical field of slope settlement monitoring and early warning, in particular to a visual slope settlement monitoring and early warning method and platform. The method comprises the following steps: acquiring slope settlement monitoring data; preprocessing the acquired slope settlement monitoring data; respectively constructing a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; constructing a multi-level early warning index system; constructing a fuzzy neural network early warning model based on a multi-stage early warning index system through a multi-scale slope digital twinborn model and a hybrid intelligent prediction model; and performing visual slope settlement early warning by using the fuzzy neural network early warning model. According to the space-air-ground integrated monitoring network constructed by the invention, a satellite InSAR, an unmanned aerial vehicle LiDAR and distributed optical fiber sensing are fused, and full-scale monitoring from regional macroscopic deformation to slope surface microcracks and deep soil displacement is realized.

The invention discloses a safety monitoring system of a high transverse support system for cable-stayed bridge man-shaped tower column construction, and relates to the technical field of bridge construction monitoring, the system comprises a creeping formwork integrated sensing module used for generating a point cloud model by using a mechanical arm integrated on a hydraulic creeping formwork platform and a laser radar scanning tower column curved surface, combining with a preset building information model coordinate, adaptively adjusting the mounting posture of the sensor, and outputting the mounting coordinate position of the sensor; the optical fiber sensing network module is used for collecting original strain and temperature data by using a distributed optical fiber sensor deployed along a main stress path of the support truss; an inertial navigation fusion positioning module; a digital twinning early warning module; and an edge calculation relay module. According to the invention, a full-process monitoring chain from data acquisition to risk early warning is constructed through cooperation of multiple modules, full-period and multi-dimensional dynamic control of construction of the human-shaped tower column high transverse support system is realized, and timely perception and overall control of potential risks are ensured.

The invention discloses an optical fiber embankment underwater piping leakage event time-space correlation analysis method, and relates to the technical field of leakage event intelligent identification and risk assessment in embankment safety monitoring, and the method comprises the following steps: S1, obtaining continuous time-space monitoring data of an embankment underwater region obtained through monitoring by a distributed optical fiber sensing system; and S2, processing the continuous space-time monitoring data by adopting a feature recognition model based on a neural network, and recognizing a suspected leakage event. According to the time-space correlation analysis method for the underwater piping leakage event of the optical fiber embankment, by fusing multi-level data processing and self-adaptive feature learning, false alarms caused by environmental interference are effectively restrained, and the recognition accuracy of a real leakage event in a complex underwater scene is improved. An analysis framework combining space-time association diagram construction and physical mechanism verification is adopted, the internal relation between events can be deeply mined, and the space-time evolution rule of a seepage path is accurately restored.

The invention provides an OFDR sensing system and method for rapidly positioning strain based on a time domain difference algorithm, and relates to the technical field of optical fiber sensing. The OFDR sensing system comprises a narrow linewidth tunable laser light source, a first optical fiber coupler, a main interferometer, an auxiliary interferometer and a data acquisition and signal processing module; laser emitted by the narrow linewidth tunable laser light source is divided into two paths through the first optical fiber coupler, one path of light enters the main interferometer, and the other path of light enters the auxiliary interferometer; the main interferometer and the auxiliary interferometer respectively output a main interferometer optical signal and an auxiliary interferometer optical signal to the data acquisition and signal processing module; the data acquisition and signal processing module is used for performing nonlinear compensation on the optical signal of the main interferometer by using the optical signal of the auxiliary interferometer, and performing cross-correlation operation on a reference optical signal of the main interferometer and a measurement optical signal of the main interferometer which are based on the strain-free state and the strain state of the sensing optical fiber and are subjected to nonlinear compensation; and obtaining the strain position and the strain value of the sensing optical fiber.

The invention relates to the technical field of deformation monitoring, in particular to a mountain tunnel surrounding rock deformation monitoring method and system based on distributed optical fiber sensing. The method comprises the following steps that Brillouin frequency shift original data and center wavelength original data are collected, tunnel construction progress data and surrounding rock geological survey data are obtained at the same time, format standardization and time sequence alignment processing is carried out, and preprocessed optical fiber sensing basic data, construction time sequence data and geological attribute data are generated; surrounding rock deformation characteristic parameters are extracted based on the optical fiber sensing basic data, and surrounding rock deformation multi-dimensional characteristic vectors are constructed; performing deformation mode density clustering and surrounding rock deformation dynamic prediction based on the surrounding rock deformation multi-dimensional feature vector to generate a surrounding rock deformation dynamic prediction value; and carrying out comparison and iterative correction on the dynamic prediction values of the surrounding rock deformation, and carrying out regionalized polymerization to generate a mountain tunnel surrounding rock deformation spatial and temporal distribution visualization result. According to the invention, full-time-space accurate monitoring and early warning of tunnel surrounding rock deformation can be realized.

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

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention discloses a phi-OTDR optical cable fault diagnosis method and system based on multi-channel topological data analysis, and is applied to the technical field of optical fiber sensing and intelligent operation and maintenance. The method comprises the following steps: acquiring a backward Rayleigh scattering signal of a sensing optical cable to obtain a spatio-temporal data matrix; preprocessing and multi-scale slicing are carried out to obtain spatio-temporal data segments, and initial position information based on the optical time domain reflection principle is added to each spatio-temporal data segment; different types of spatio-temporal data fragments are routed to corresponding topology analysis channels for parallel processing; extracting topological features and positioning information, and fusing the topological features and the positioning information to form optical cable full-state vectors; and outputting a diagnosis result based on the pre-trained intelligent diagnosis model, wherein the diagnosis result comprises a fault type, accurate position information and a health degree evaluation result. According to the method, the positioning robustness in a strong noise environment is effectively improved, and accurate fault classification and early capture and early warning of progressive performance reduction of the optical cable are realized.

The invention discloses a polarization-state-based optical fiber vibration sensing system and application, and relates to the technical field of optical fiber sensing, and the polarization-state-based optical fiber vibration sensing system mainly comprises a transmitting end and a receiving end. The transmitting end comprises a laser diode, a first erbium-doped optical fiber amplifier, a first circulator, a polarization scrambler, a single-mode optical fiber link, a second circulator and a second erbium-doped optical fiber amplifier which are connected in sequence; and the receiving end comprises a polarization beam splitter, a first shunt module, a second shunt module, an optical mixer, a first balanced photoelectric detector, a second balanced photoelectric detector, a third balanced photoelectric detector and a polarization analysis module. According to the optical fiber vibration sensing system based on the polarization state and the application, the vibration monitoring performance and reliability in a long-distance and high-noise environment can be improved.

The invention provides a sensing system fusing optical fiber vibration and acoustic emission, a monitoring method and a device, and relates to the technical field of optical fiber sensing. The device comprises a light source module, a sensing optical fiber, a detection module and a controller, the light source module is used for respectively emitting modulation pulse light for vibration monitoring and continuous wave laser for acoustic emission monitoring; the sensing optical fiber is arranged along a monitored structure or area, and at least one optical fiber acoustic emission sensing head is connected to the optical fiber in series. The detection module collects Rayleigh scattering light and beat frequency signals at all positions in the sensing optical fiber through a circulator and a coherent light receiver, and optical signals are converted into electric signals; wherein the electric signals comprise vibration electric signals and acoustic emission electric signals; the signal processing unit carries out phase demodulation and envelope analysis processing on the collected vibration electric signals and acoustic emission electric signals so as to obtain vibration waveforms and acoustic emission event space-time information along all points of the optical fiber. According to the invention, a monitoring task fusing vibration and acoustic emission can be accurately completed.

The invention provides a distributed optical fiber-based vibration source transverse distance determination method and system, and relates to the technical field of optical fiber sensing, and the method comprises the steps: collecting distributed optical fiber vibration signals, extracting features, constructing a multi-resolution scanning region, carrying out the traversal through employing a global optimization algorithm, and calculating the propagation speed of the vibration signals. According to the method, velocity field distribution characteristics based on Gaussian process regression are constructed, velocity field parameters are optimized in combination with a probability sampling optimization method, the transverse distance of a vibration source is accurately determined through discretization space traversal and anisotropy correction, and the precision and reliability of vibration source positioning are improved.

The invention provides a tunnel health monitoring method based on distributed optical fiber sensing, and relates to the technical field of tunnel engineering health monitoring, and the method comprises the steps: carrying out the risk assessment of a tunnel through employing an analytic hierarchy process, setting a differential optical fiber laying scheme according to an assessment result, and laying distributed optical fiber sensors on the inner surface of a tunnel lining; the method comprises the following steps: acquiring original strain data through Brillouin optical time domain analysis, and preprocessing by using a moving average filtering algorithm; building a finite element model based on actual physical data of the tunnel, inverting strain data into full-field node displacement, and obtaining a complete strain tensor of a finite element unit; performing unit-level strain state evaluation, time sequence trend risk evaluation and spatial clustering risk evaluation to obtain a comprehensive risk value; and setting a dynamic evaluation threshold interval, and completing tunnel accurate region health evaluation. Accurate monitoring and risk assessment of the tunnel health state are achieved, and the method can be widely applied to the field of safety monitoring of highway and railway tunnels.

The invention relates to a DAS noise modeling method based on a conditional diffusion model, and belongs to the technical field of distributed optical fiber sensing data processing. DAS noise data is acquired and analyzed, a DAS-conditional diffusion model is constructed, and model training and data generation are carried out. According to the method, the statistical characteristics and conditional constraints of the DAS noise are fused in the diffusion process, so that the physical consistency and diversity of the generated noise are effectively improved, and the problem of lack of high-quality mark data in the DAS system is solved. Experiments show that the generated noise and the real noise are highly similar in time-frequency domain and statistical characteristics, the performance of the denoising network based on the extended training set is remarkably improved, high-quality data support is provided for tasks such as DAS data denoising and signal enhancement, and the application capability of the DAS technology in a complex environment is enhanced.

The invention belongs to the technical field of distributed optical fiber sensing and low-altitude security and protection, and particularly relates to an unmanned aerial vehicle long-distance tracking method based on distributed optical fiber vibration sensing. The invention relates to an unmanned aerial vehicle vibration / electromagnetic fusion multi-mode identification method, a special installation formation design, a dispersion compensation and amplitude phase error compensation design, an unmanned aerial vehicle target vibration / electromagnetic fusion multi-mode identification design, a detection capability calculation method of an unmanned aerial vehicle vibration sound pressure-space attenuation law-distributed optical fiber processing equipment detection capability coupling model, a long-distance sensing optical cable and an electromagnetic monitoring node. According to the tracking method, a special optical fiber installation mode, dispersion compensation and phase error compensation of an original phase signal, a vibration / electromagnetic fusion multi-mode identification method, an unmanned aerial vehicle vibration sound pressure-space attenuation model-distributed optical fiber processing equipment detection capability corresponding method and other innovative work are adopted; a mature distributed optical fiber vibration sensing system is utilized to realize long-distance passive detection and target tracking of the low-slow-small unmanned aerial vehicle.

The invention discloses a three-dimensional tomography method and system for seismic exploration data migration, and relates to the technical field of geological exploration, and the three-dimensional tomography method comprises the following steps: laying a layered heterogeneous sensing array of adaptive terrain coupling in an exploration target area, the layered heterogeneous sensing array comprises a shallow fiber grating sensing layer, a middle MEMS sensing layer and a deep distributed fiber sensing layer, sensors are all provided with terrain adaptive coupling assemblies, and sensing shafts all point to the radial direction of an underground medium. According to the seismic exploration three-dimensional tomography imaging method, an iterative imaging mechanism of real-time geological constraint feedback is constructed, the limitation of traditional static geological constraint one-time importing is broken through, the imaging process can dynamically fuse newly added local geological data in the exploration process, the imaging model and the actual geological conditions are continuously adapted and optimized, and the imaging efficiency is improved. The integrating degree of the imaging result and the actual geological condition is enhanced, and the high requirement of deep complex area exploration for the imaging accuracy is met.

The invention discloses a distributed OFDR sensing signal demodulation method and system based on local ridge line tracking, relates to the technical field of optical fiber sensing, and is used for solving the problem that a traditional cross-correlation algorithm is difficult to consider robustness and real-time performance at the same time. The method comprises the following steps: respectively processing a reference beat frequency signal of a sensing optical fiber to be measured in a strain-free state and a measurement beat frequency signal of the sensing optical fiber to be measured in a strain state to obtain a reference complex Rayleigh scattering signal and a measurement complex Rayleigh scattering signal containing a plurality of sensing positions along the optical fiber; calculating a cross-correlation function based on the two signals; stacking the cross-correlation functions corresponding to all sensing positions along a distance domain, and constructing a two-dimensional cross-correlation thermodynamic diagram matrix; performing filtering processing and weighting processing on the two-dimensional cross-correlation thermodynamic diagram matrix to obtain an optimal frequency drift distance; and demodulating based on the optimal frequency drift amount to obtain strain distributed along the to-be-measured sensing optical fiber. According to the invention, high-robustness and rapid demodulation of the signal in an application scene with extremely high real-time requirement can be realized.

The embodiment of the invention discloses an insulation aging early warning method and device for a medium-high frequency transformer and a medium, and relates to the technical field of transformers, and the method comprises the steps: collecting a multiband light intensity distribution signal of a target resin region in the transformer according to a preset collection frequency through a preset multispectral distributed optical fiber sensing array; the insulation state of the target resin area is predicted through a pre-constructed neural network identification model and a multi-spectral distributed optical fiber sensing array, and the predicted insulation state health degree of the target resin area is determined; acquiring real-time environment parameters of the target resin area, dynamically correcting a preset early-warning discharge threshold according to the real-time environment parameters, and determining a current early-warning dynamic discharge threshold; and extracting a plurality of current light intensity parameters in the multiband light intensity distribution signal, predicting an insulation state health degree and a current early warning dynamic discharge threshold through the plurality of current light intensity parameters, and performing insulation defect early warning on the insulation state of the target resin area.

The invention provides a parameter monitoring method and system in a composite material preparation process based on optical fiber sensing, computer equipment and a medium, and belongs to the field of composite material detection. Center wavelength signals in the resin diversion stage are continuously collected through optical fiber strain sensors in a distributed optical fiber grating sensing array, and the distributed optical fiber grating sensing array is embedded into the multiple layers of dry carbon fiber cloth in the fiber preform in advance; the flowing position and time of the resin in the fiber prefabricated body are determined according to the minimum value of the central wavelength signal offset in the resin flow guide stage, and the flowing front edge of the composite material is constructed according to the flowing positions and time of the resin corresponding to the optical fiber strain sensors in the fiber prefabricated body. According to the method, the change of the structure state of the composite material in the resin diversion process can be continuously monitored in a complex variable-temperature environment, and the accurate monitoring of the three-dimensional resin flowing front edge in the composite material liquid forming process is realized.

An optical frequency domain reflectometer with environmental disturbance resistance and large strain measurement capability belongs to the field of distributed optical fiber sensing and comprises an opposite sweep frequency light frequency modulation continuous wave signal generation module, a light splitting device, a modulation device, a circulator, a polarization controller, an optical coupling device, a photoelectric conversion module and a data acquisition and processing module. An optical signal generated by an opposite sweep frequency light frequency modulation continuous wave signal generation module is divided into probe light and local oscillation light through a light splitting device, the probe light is modulated through a modulation device and sent to an optical fiber through a circulator, and a Rayleigh back scattering light signal generated by the optical fiber and the local oscillation light interfere in an optical coupling device to obtain interference light; the photoelectric conversion module mixes beat frequency signals of interference light, and the data acquisition and processing module analyzes and processes the beat frequency signals. According to the invention, the position information of the Rayleigh backscattering point is marked by using the modulated probe light, the Rayleigh scattering signal at the corresponding position is restored, crosstalk between channels is inhibited, and high-precision measurement in a large dynamic range and resistant to environmental disturbance is realized.

The invention provides a fan running state identification method and system based on multi-mode optical fiber sensing monitoring, and the method comprises the steps: obtaining a continuous sensing signal collected by a distributed optical fiber sensor, carrying out the time-space correlation filtering of the continuous sensing signal, generating a multi-physical field micro-variation feature vector set, and carrying out the recognition of the running state of a fan through the multi-physical field micro-variation feature vector set; inputting the multi-physical field micro-variation feature vector set into a pre-trained coupling response modeling network, and performing correlation modeling on the strain field micro-variation feature vector and the temperature field micro-variation feature vector through a space-time cross attention mechanism in the coupling response modeling network to generate a dynamic response fingerprint spectrum; calling a fault evolution path set in a historical fault database, performing similarity matching on the dynamic response fingerprint spectrum, and generating an operation state deviation index; and querying a preset health state evaluation strategy library, determining the overall health state level of the fan and the corresponding potential fault risk probability, and generating an operation state evaluation result containing the fault area positioning coordinates. The accuracy of fan operation state recognition can be improved.

A distributed composite geotechnical sensor disclosed by the present invention comprises a multi-core cable, an optical fiber sensing member, at least two composite sensing members and a plurality of connectors, the multi-core cable is used for transmitting charge signals, and the optical fiber sensing member and the multi-core cable are arranged in parallel and at an interval and are used for monitoring temperature and stress. The at least two composite sensing pieces are sequentially arranged at intervals along the length direction of the optical fiber sensing piece, are arranged in the same core with the optical fiber sensing piece and are used for monitoring dynamic stress waves or vibration, and the plurality of connecting bodies are respectively arranged at two ends of the composite sensing pieces and are connected with the multi-core cable, the optical fiber sensing piece and the composite sensing pieces. The electric connector is used for electrically connecting the multi-core cable with the composite sensing piece. According to the invention, the problems of relatively single monitoring index and incapability of positioning due to the fact that a traditional piezoelectric geotechnical cable can only monitor external disturbance information and cannot realize distributed monitoring can be effectively solved.

The invention provides a safety risk control method for geological storage of carbon dioxide and electronic equipment. A distributed optical fiber sensing system is arranged in at least one injection well of the target sealing and storage area in the axial direction of a shaft and penetrates through a main cover layer and a reservoir; the method comprises the following steps: acquiring background data through a distributed optical fiber sensing system, and establishing a multi-parameter baseline model including a temperature field, a strain field and background noise; temperature, strain and sound wave vibration signals are collected in real time, and key features are extracted; the key features extracted in real time are compared with a preset risk feature library, the types of risk events are identified, and the identified risk events are graded according to the spatial range and intensity of the abnormal signals; and according to the type and the grade of the risk event, generating and executing a corresponding control instruction. According to the embodiment of the invention, the optical fibers are distributed in the whole area along the shaft and the target stratum, a three-dimensional monitoring network covering the sealing body is constructed, and the hysteresis quality of traditional point type measurement and periodic exploration is overcome.

The invention provides a multi-merchant hardware equipment supply chain demand intelligent matching method, and relates to the technical field of supply chain management, and the method comprises the steps: obtaining a demand order and supply capability data through a supply chain platform, and enabling the supply capability data to obtain production line test data in real time through an Internet of Things interface; performing preliminary screening based on equipment types and necessary authentication standards; calculating the comprehensive reliability of the optical fiber sensing system by adopting a comprehensive evaluation model fused with nonlinear transformation; calculating the multi-dimensional demand integrating degree of the supplier and the demander through multi-dimensional difference analysis and normalization processing; intelligent sorting is carried out based on weighted scores of configurable weight factors; implementing global productivity monitoring and conflict resolution optimization, and outputting an optimal matching pair; and continuously iteratively optimizing the evaluation model parameters according to the performance feedback data by adopting an incremental learning mechanism. According to the method, accurate matching and dynamic optimization under multi-target constraints are realized, and the supply chain resource configuration efficiency and the system autonomy capability are effectively improved.

The present application relates to a kind of on-chip photo-thermal gas sensor and on-chip photo-thermal gas sensor assembly, the refractive index of the upper waveguide material layer of sensor ridge waveguide layer is greater than the refractive index of lower waveguide material layer, can support TM mode pump light and TE mode probe light simultaneously. That is, both high absorption of pump light and weak influence of probe light on the gas to be measured are considered, and the detection accuracy of the gas is improved. Moreover, a simpler waveguide structure is used, and the waveguide preparation process is simple, and the waveguide loss is relatively low. Compared with the photo-thermal interference method in optical fiber sensing, the interaction between pump light and the gas to be measured is greatly improved, the length of the detection waveguide is greatly shortened while ensuring high sensitivity, and the device size can be reduced to micrometer level. In addition, the waveguide structure of the on-chip photo-thermal gas sensor disclosed in the present application is prepared from chalcogenide glass, which has more obvious advantages for gas detection in the mid-infrared waveband.

The invention discloses a rain and sewage pipeline defect detection method and system based on distributed optical fibers. The method comprises the following steps: 1, laying sensing optical fibers in a pipeline; 2, calibrating an excitation position; 3, collecting and demodulating optical fiber vibration data; 4, performing defect identification and defect positioning on data processed by an intelligent algorithm; vibration signals are sent out through the excitation device, signal collection is carried out in combination with the distributed optical fiber sensing module, efficient recognition and accurate positioning of pipeline defects are achieved in cooperation with an intelligent detection algorithm, and hidden diseases which cannot be perceived by naked eyes can be recognized. By means of an intelligent defect recognition algorithm, signals collected by the optical fiber sensing module are deeply analyzed, the positions and the disease degrees of the defects in the pipeline can be automatically judged, dependence on manual image interpretation is avoided, and automation and intelligentization of pipeline defect detection are achieved. The system has the advantages of being high in adaptability, wide in coverage range, efficient, accurate, high in intelligent processing capacity, low in cost, high in efficiency and the like.

The invention provides an intelligent stomach tube based on optical fiber sensing and image monitoring, and the stomach tube comprises a stomach tube body which is internally integrated with an optical fiber and a miniature camera assembly; at least three FBG (Fiber Bragg Grating) sensing nodes which are respectively positioned in the stomach, the esophagus and the throat section are inscribed on the optical fiber and are used for acquiring pressure and temperature signals; the miniature camera assembly is arranged on the inner wall of the throat section and used for collecting images. A demodulation-edge AI processing terminal is arranged outside the body, connected with the sensing nodes and the camera shooting assembly and used for demodulating signals and conducting multi-mode fusion analysis on the basis of pressure signals and image data, real-time judgment of the situation that the catheter enters the airway by mistake, intelligent evaluation of the risk of aspiration before catheter drawing and early warning output when the risk exceeds the standard are achieved. According to the invention, the FBG sensing node and the miniature camera shooting assembly are integrated in the stomach tube, and the in-vitro demodulation-edge AI processing terminal is combined, so that precise and automatic monitoring and early warning of the whole process from tube indwelling to tube drawing evaluation are realized, and the safety and the medical quality of a patient are fundamentally improved.

The invention discloses a ground surface settlement and ground fracture monitoring device and method based on distributed fiber sensing (DSS) and fiber bragg grating (FBG). The device comprises a GFRP composite material substrate which stretches across a ground fracture and is fixed on a stable stratum through an anchoring structure, a distributed sensing and temperature measuring optical cable which is pre-embedded in a groove of the substrate, an FBG displacement meter which is connected through a plug-and-play interface, a static force level gauge, an osmometer and other point type sensors, and the data processing unit has the functions of multi-source data acquisition, temperature compensation, data fusion and multistage early warning. According to the method, multi-parameter, distributed and high-precision monitoring of the ground fracture development state is achieved through the steps of field investigation, substrate installation, sensor deployment, system calibration, real-time monitoring and the like. The method has the advantages of high measurement precision, strong anti-interference capability, convenient installation, low cost, capability of predicting the crack propagation trend and the like, and is suitable for long-period safety monitoring of geological disaster frequent areas such as mining areas and landslide areas.

The invention discloses an end slope mining side slope real-time stability testing system and method, and relates to the technical field of open pit coal mine end slope mining safety monitoring and early warning, and the method comprises the steps that a distributed optical fiber sensing network arranged on a side slope surface and a mining chamber top plate collects acoustic emission and strain time sequences; a solid-state laser radar array arranged on the stope platform collects slope surface point cloud data of the stope platform; during data acquisition, the solid-state laser radar array and the distributed optical fiber sensing network are in time alignment; carrying out slope displacement increment extraction on the slope point cloud data of the monitored area by the edge GPU node; the data analysis cloud platform is used for performing microfracture identification according to the acoustic emission and strain time sequence; and inputting the slope displacement increment, the micro-fracture event count and the strain time sequence into the trained stability evaluation model to obtain a stability evaluation result, and capturing the internal micro-fracture, surface micro-displacement and shear-expansion coupling characteristics in real time, and providing high-reliability instability prediction.