2334 results about "Optical fiber sensing" patented technology

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

The invention relates to the technical field of 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 discloses an intelligent cable fault accurate positioning and early warning method and system, and the method comprises the steps: collecting the temperature gradient, strain distribution and partial discharge signals of the whole length of a cable in real time through a distributed optical fiber sensing network, and generating a multi-dimensional feature matrix of the operation state of the cable; based on the multi-dimensional feature matrix, outputting a preliminary fault positioning coordinate; generating corrected fault coordinates according to the topological structure data of the cable laying environment and the electromagnetic interference distribution diagram; historical fault data, real-time operation parameters and the corrected fault coordinates are fused, and a fault risk thermodynamic diagram in a future preset duration is output; and based on the fault risk thermodynamic diagram and real-time monitoring data, generating fault first-aid repair information by using a dynamic priority algorithm, synchronously triggering an early warning signal, and visually displaying a fault positioning result and a risk area in a three-dimensional geographic information system. According to the embodiment of the invention, rapid positioning, accurate early warning and intelligent disposal of the cable fault can be realized.

The invention discloses a photovoltaic power station intelligent remote operation and maintenance method and system based on relay protection, and the method comprises the steps: collecting the multi-mode characteristic quantity of a photovoltaic array region relay protection device in real time through a distributed optical fiber sensing network, so as to construct a dynamic protection characteristic matrix; performing feature decoupling on the dynamic protection feature matrix by adopting a space-time convolution adversarial encoder to generate a fault feature vector set; inputting the fault feature vector set into a physically constrained LSTM-GAN hybrid prediction model, and predicting a multi-dimensional failure probability tensor of the key relay protection node in a future preset duration; generating an optimal operation and maintenance strategy decision tree based on the multi-dimensional failure probability tensor; and analyzing the operation and maintenance strategy decision tree through a remote execution gateway driven by the digital twinborn, and synchronously updating the topological connection relationship of the digital twinborn bodies of the power station. According to the embodiment of the invention, the operation and maintenance efficiency and reliability of photovoltaic power station protection can be improved.

The invention provides a comprehensive pipe gallery structure health monitoring system based on distributed optical fiber sensing, and relates to the technical field of engineering detection, and the system comprises a laying module which is used for laying an optical fiber sensing optical cable with waterproof and anti-corrosion performances in a pipe gallery structure, setting a positioning reference point and a coordinate calibration reference point in the central area of the tops of the two ends of a pipe gallery, and setting a positioning reference point and a coordinate calibration reference point; a linear reference system covering the length of the whole pipe gallery is constructed; and the acquisition module is used for connecting the optical fiber sensing optical cable with the distributed optical fiber temperature measuring device and the distributed optical fiber strain device, and acquiring temperature distribution data and strain distribution data in the pipe gallery based on Raman scattering and Brillouin scattering principles to form a monitoring data set. According to the invention, the temperature and strain multi-dimensional data of the pipe gallery are collected in real time through the distributed optical fiber sensing technology, and the structure health monitoring is realized in combination with redundancy monitoring, dynamic compensation and an intelligent early warning mechanism.

The invention provides a high-negative-pressure gas extraction drill hole full-life-cycle efficiency evaluation method, and belongs to the technical field of mineral exploitation. A ground stress distribution model is established through microseism monitoring to determine drill hole arrangement parameters; a distributed optical fiber sensing system and a multi-parameter gas flow meter are used for monitoring the stress change around a drill hole and gas extraction data in real time, the coal fracture development degree is obtained in combination with sound wave testing, and all the data are input into a physical numerical value coupling model to calculate a permeability dynamic evolution curve. Establishing a permeability influence coefficient matrix and determining a key parameter weight, constructing an evaluation index system including the extraction amount, the permeability change rate, the drilling life and the coverage range, and performing multi-objective optimization configuration by applying a Pareto optimal solution theory. The technical problem that full-life-cycle dynamic monitoring and comprehensive efficiency evaluation of the high-negative-pressure gas extraction drill hole are lacked at present is solved.

The invention relates to the technical field of electric forklifts and energy storage, and discloses an intelligent battery management system and prediction method based on optical fiber sensing, the system comprises a distributed optical fiber sensing network, a data acquisition and processing module, an intelligent analysis decision module, a battery management control module and a cloud platform interaction module; the method comprises the following steps: collecting a battery signal through a snakelike-spiral optical fiber network, reconstructing a temperature / strain field through noise reduction and filtering, extracting multi-modal features, inputting the multi-modal features into an LSTM model to predict thermal runaway, generating a risk level in combination with an improved equation, dynamically adjusting charging, discharging and heat dissipation strategies, and realizing three-dimensional visual monitoring through a cloud platform. According to the invention, high-precision distributed monitoring of the temperature / strain field of the battery pack is realized through the BOTDR optical fiber sensing network, the LSTM prediction model is constructed by fusing multi-physical field characteristics, and thermal runaway is warned in advance. The improved algorithm reduces the false alarm rate, charging and discharging parameters are dynamically adjusted to prolong the service life of the battery, and a three-dimensional visual platform improves the operation and maintenance efficiency.

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 provides a strain and temperature synchronous calibration method and device based on distributed optical fiber sensing, and relates to the technical field of optical fiber sensing calibration. Initial measurement node spacing and distribution density are obtained, reflected signals are captured according to simulated optical fiber layout, and time delay, intensity and phase change of the reflected signals are recorded; calculating a strain value and a temperature value of each measurement node, establishing a calibration model of a strain and temperature relationship, performing simulation by adopting a polynomial fitting method, obtaining and correcting real-time data, establishing a self-adaptive measurement node adjustment mechanism, and generating a calibration deviation coefficient by analyzing the deviation between the current monitoring data and a correction value. According to the invention, through the dynamic layout of the distributed optical fiber sensors and the self-adaptive measurement node adjustment mechanism, the high-precision calibration of the strain and the temperature of the key area of the structure is 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 a roadway surrounding rock deformation monitoring method based on an optical fiber sensor, and the method comprises the steps: laying on the surface of roadway surrounding rock through employing a distributed optical fiber sensing technology, and obtaining original optical signal data; processing backscattered light data in the original optical signal data through a Brillouin optical time domain analysis algorithm, extracting frequency deviation of an optical signal, and obtaining strain distribution data; according to the strain distribution data, in combination with a pre-established surrounding rock mechanical model, performing initial inversion calculation by adopting a finite element method to obtain initial three-dimensional deformation distribution; on the basis of the initial three-dimensional deformation distribution, updating dynamic boundary conditions of the surrounding rock mechanical model by acquiring real-time geological parameter data to obtain an optimized mechanical model; carrying out inversion calculation on the strain distribution data again by adopting an optimized mechanical model to generate final three-dimensional deformation distribution; and based on the final three-dimensional deformation distribution, performing comparison judgment through a preset deformation threshold, determining the stability state of the surrounding rock, and generating an early warning signal.

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 relates to the technical field of optical fiber sensing and fault diagnosis, in particular to an optical fiber gyroscope fault monitoring method and system. The method comprises the following steps: extracting four-dimensional characteristics of phase difference, light intensity fluctuation, polarization state drift and temperature drift from an output signal of an optical fiber gyroscope, and constructing a high-dimensional characteristic matrix after unifying a time reference; establishing a dynamic prediction model based on historical data to generate a residual matrix, and orthogonally separating the residual into an internal structure abnormal signal and an environment disturbance signal through covariance characteristic decomposition; mapping the structure residual error sequence into a topological graph, calculating an evolution index in real time, and capturing a fault gradient trend in combination with sliding window gradient analysis; a multi-dimensional vector is constructed by fusing topological features, an adaptive classifier of an online Gaussian mixture model is adopted to identify a fault mode and quantify a health index, and meanwhile, a health measurement result is fed back to a prediction model and topological analysis parameters to realize closed-loop optimization. According to the invention, full-process adaptive monitoring from anomaly detection to health measurement is realized.

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.

The invention relates to the technical field of optical fiber sensing, and discloses an optical fiber sensing signal adaptive demodulation analysis method for inhibiting common-mode interference, which comprises the following steps of: synchronously acquiring an original signal of a main sensing optical fiber channel and a common-mode interference signal of a reference optical fiber channel, and performing time-frequency conjoint analysis on the signal of the reference optical fiber channel to obtain a common-mode interference signal; s2, extracting an interference spectrum component containing temperature drift and light source fluctuation characteristics, constructing a hybrid adaptive filter bank which comprises a recursive least square (RLS) filter and a variable step size least mean square (LMS) filter which are connected in parallel, and dynamically adjusting a forgetting factor of the RLS filter and a step size factor of the LMS filter based on the interference spectrum component extracted in the step S2, according to the optical fiber sensing signal adaptive demodulation analysis method for suppressing common-mode interference, a parameter adjustment strategy is continuously optimized, a pure vibration signal with an output signal-to-noise ratio is improved, and the effect of accurately suppressing common-mode interference in real time is achieved.

The invention provides a tunnel deformation monitoring system and method based on a distributed optical fiber sensing technology, and relates to the technical field of tunnel monitoring. A spiral winding type optical fiber is arranged in the longitudinal axis direction of the surface of a tunnel lining, a transverse optical fiber is arranged on a key section, and the data acquisition frequency is set to be 1Hz. Dividing the optical fiber into a plurality of data acquisition points at equal intervals, obtaining the real-time temperature and Brillouin frequency shift change of each point, measuring the temperature and strain sensitivity coefficient, and calculating the total strain; calculating long-term temperature strain compensation based on the long-term temperature component, generating an instantaneous temperature difference through the real-time temperature and the long-term temperature component, further calculating instantaneous temperature strain compensation, subtracting the two kinds of strain compensation from the total strain to obtain stress strain, and evaluating the deformation level of the tunnel based on the stress strain. And the accuracy of evaluation is judged through clustering analysis.

The invention relates to the technical field of structural stress precision measurement, in particular to a distributed optical fiber sensing-based steel structure stress monitoring method, which comprises the following steps of: 1, constructing a sensing optical fiber array in a steel structure node area; 2, injecting optical pulses into the sensing optical fiber array, and synchronously collecting Brillouin frequency shift signals and Raman scattering signals of all points; 3, temperature and mechanical strain are separated based on the reference optical fiber signals, global temperature compensation is adopted for the first optical fiber set, and temperature gradient compensation along the optical fiber path is adopted for the second optical fiber set; 4, decomposing the compensated mechanical strain into multidirectional strain components according to the spatial orientation of the optical fiber, and reconstructing a three-dimensional stress field through strain component fusion; and 5, dynamically judging the damage risk according to the local stress concentration coefficient and the change rate thereof in the stress field. And through the temperature compensation technology, the influence of the environment temperature change on the strain data is eliminated, and the reliability of the monitoring result is improved.

The invention relates to the technical field of optical cable monitoring, and discloses an optical cable early warning method based on passive sensing integration, and the method comprises the steps: carrying out the passive sensing signal collection of an optical cable disposed in a target region, so as to obtain a disturbance signal transmitted along the optical cable; performing preprocessing operation on the disturbance signal to obtain a normalized disturbance signal sequence; meanwhile, environment data are collected through a multi-parameter sensor network; preprocessing the disturbance signal and the environment data, constructing a multi-dimensional feature vector containing gas concentration sudden change features, temperature distribution abnormity and vibration frequency-amplitude, performing disturbance event identification based on the multi-dimensional feature vector, and determining corresponding disturbance event data; and combining the disturbance event data with the spatial distribution of the optical fiber sensing signal and the vibration wave propagation time difference, positioning the positioning result data corresponding to the occurrence of the abnormal event, generating an early warning instruction based on the positioning result data, and pushing the early warning instruction to the user terminal through the monitoring platform. According to the invention, the accuracy of optical cable safety monitoring can be improved.

The invention discloses a coal pillar damage dynamic monitoring system and method based on multi-physics coupling and distributed optical fiber sensing, and relates to the technical field of dynamic monitoring. Multi-physics coupling data of a coal pillar is collected in real time through a distributed optical fiber sensing network, and a health index of a coal pillar structure is obtained based on a trained time sequence multi-scale mapping model; the damage evolution rate of the coal pillar is calculated, and if the damage evolution rate is lower than a preset first threshold value, the sampling frequency of the distributed optical fiber sensing network is adjusted, and the data acquisition density of the distributed optical fiber sensing network is reduced; increasing the sampling frequency of the distributed optical fiber sensing network and constructing a trend prediction model to generate evolution prediction of the coal pillar damage if the coal pillar damage is not lower than the preset first threshold value but lower than the preset second threshold value; and if not, switching the distributed optical fiber sensing network to a high-frequency sampling mode, and generating the evolution acceleration of the coal pillar damage. The optimal configuration of the monitoring resources and the dynamic adjustment of the monitoring intensity are realized.

The invention relates to the technical field of intelligent buildings and energy management, and particularly discloses an intelligent building heating intelligent optimization operation method and system based on deep learning, and the method comprises the steps: collecting building environment parameters and equipment operation data in real time through a distributed optical fiber sensing network and an infrared thermal imaging system; extracting minute-level fluid transmission and distribution parameters and hour-level building thermal inertia characteristics by adopting wavelet packet transformation and a graph neural network; then, constructing a neural differential equation prediction model fused with physical constraints, and outputting high-precision thermal load demand prediction through a differentiable heat conduction operator coupling multi-time scale feature; then, a mixed integer optimization model considering equipment life loss is established, and boiler start-stop combination and pipe network flow distribution are synchronously optimized by adopting a hierarchical decision-making mechanism; and finally, closed-loop control is realized through a multi-mode actuator network, and model parameters are dynamically adjusted in combination with an online learning mechanism.

The invention relates to the field of disaster monitoring, and provides a slope disaster real-time monitoring and early warning method and system, and the method comprises the steps: laying a distributed optical fiber sensing network, a Beidou GNSS receiver, a miniature pore water pressure gauge and an inclinometer at a target slope earth surface position and a deep position, and obtaining internal and external cooperative monitoring data; extracting and calculating a plurality of branch characterization values in the internal and external cooperative monitoring data; calculating and generating multi-source fusion feature data based on the plurality of branch representation values; establishing a dynamic threshold monitoring model, and calculating and generating a real-time early warning threshold; establishing a multi-level linkage early warning mechanism based on the real-time early warning threshold value; and internal and external cooperative monitoring is realized through a multi-mode sensor network, and slope state information is comprehensively captured. Multi-source data fusion processing improves data quality and reliability, a dynamic threshold model accurately adapts to different working conditions, and a multi-level linkage early warning mechanism ensures timely response. The accuracy and timeliness of slope disaster early warning are greatly improved on the whole, and disaster loss is effectively reduced.

The invention provides an automatic assembling method and system for an inductor magnetic core. Wherein continuous images of the surface of the magnetic core are captured through multi-band rapid scanning, and geometric deformation characteristics are extracted; meanwhile, a distributed optical fiber sensing array is adopted to collect pressure data of the contact surface, and a real-time distribution diagram is generated through a three-dimensional field reconstruction algorithm. And then carrying out space coordinate alignment on the edge contour offset of geometric deformation and the pressure gradient, and constructing a comprehensive quality index. And based on the dynamic deviation of the index, a contact angle compensation value and an assembly speed optimization value are solved in real time, and a parameter adjustment instruction is generated. And finally, through parallel calculation of instruction data, a pressure tolerance threshold value and a deformation compensation parameter are synchronously updated, dynamic balance of deformation correction and pressure control in the assembly process is realized, and a self-adaptive closed-loop precision control system is formed. According to the technical scheme, through multi-dimensional sensing data fusion and real-time parameter adjustment, dynamic matching of deformation and stress states in the magnetic core assembling process is achieved.

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 discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.

The invention provides a decoupling and Kalman filtering temperature strain demodulation method and system for double fiber gratings, and relates to the technical field of optical fiber sensing. The method comprises the following steps: firstly, designing an FBG sensing module for measuring environment temperature change and strain information; the FBG sensing module comprises a two-channel sensing system composed of an FBG1 sensor and an FBG2 sensor, and is used for measuring environment temperature change and strain information at the same time. Then, aiming at optical signals which are acquired by the two FBG sensors and contain physical quantity information, a wavelength demodulation module is used for acquiring the central wavelength drift distance of reflection spectrums of the two FBG sensors; temperature sensitivity coefficients and strain sensitivity coefficients of the two FBG sensors are obtained, and a linear equation set is constructed for independent variation of temperature and strain to perform matrix decoupling so as to distinguish the influence of the temperature and the strain on the change of the central wavelength; and performing optimization estimation on the decoupled temperature and strain by using the prior information of the temperature and the strain and combining a real-time measurement value by adopting a Kalman filtering method.

The invention belongs to the technical field of deformation measurement, and provides a distributed optical fiber sensing casing damage monitoring system and monitoring method based on an OFDR (Optical Frequency Domain Reflectometer) technology. An armored optical cable consisting of a multi-core single-mode optical fiber and two multi-mode optical fibers of which the tail ends are welded into a U shape is spirally wound on the outer wall of a casing; a single-channel DSS / dual-channel DTS modulation-demodulation system of a dynamic distributed optical fiber strain and temperature composite sensing technology is used for real-time acquisition of strain and temperature sensing data of the casing pipe under the action of ground stress, and a cylindrical casing pipe three-dimensional model reconstruction algorithm is combined to automatically generate a spiral line. The three-dimensional model of the cylindrical casing is in one-to-one correspondence with the position of the optical fiber on the real casing, casing damage possibly occurring to the underground casing under the action of ground stress is monitored and measured in real time for a long time, early warning and forecasting information about that the casing damage is about to occur or is occurring is issued in time, and well site operators can conveniently handle and repair the casing damage in time.

The invention discloses a dam leakage monitoring system and method based on optical fiber sensing and time-space fusion. The dam leakage monitoring system comprises an optical fiber sensing system, a data processing and analyzing system, a dam leakage data management system and a real-time monitoring platform. According to the invention, the distributed optical fiber realizes continuous strain and temperature monitoring of the whole length of a dam, a leakage area can be roughly positioned, an FBG sensor is deployed at a key position, local tiny deformation is accurately monitored, a cooperative mode is formed by combining wide-area screening of the distributed optical fiber and key diagnosis of the FBG, and the monitoring efficiency is improved; the all-fiber passive design is adopted, the environmental adaptability is high, the anti-interference performance and the long-term stability are high, frequent maintenance is not needed after the fiber is embedded into the structure, and the operation and maintenance cost is reduced; finally, the leakage risk is displayed through a trend curve of a visual data platform, alarm pushing and emergency plan functions are automatically triggered, the closed-loop time of monitoring, decision making and response is shortened, and the real-time performance and decision-making force are improved.

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.