809 results about "Optical fibre sensor" patented technology

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

The invention 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 relates to an optical fiber implantation monitoring method and device for composite material pressure-bearing equipment, and belongs to the technical field of composite material structure monitoring and intelligent sensing. According to the method, a distributed or point type optical fiber sensor and a composite material are synchronously integrated by designing a winding process, so that state parameters such as strain, temperature, pressure and the like of a key area of equipment are monitored in real time. The stability and the precision of the optical fiber in the laying process are ensured by adopting tool structures such as an embedded guide rail and an intelligent pay-off device; the tolerance and the coupling efficiency of the optical fiber are improved by combining the multi-layer structure design of the optical fiber flexible coating glue, the elastic coupling layer and the like; fBG, Brillouin and Raman sensing are fused through a temperature-stress cooperative monitoring system, and high-precision multi-physical field sensing is achieved. The system has the advantages of high integration level, low transformation cost and excellent monitoring performance, and is suitable for on-line health monitoring and intelligent early warning of composite material pressure-bearing equipment such as low-temperature high-pressure hydrogen storage cylinders, carbon fiber V-shaped cylinders and composite material pressure pipelines.

The invention relates to the technical field of sensor demodulation, in particular to a dynamic demodulation method and system for an MEMS optical fiber multi-parameter sensor. The method comprises the following steps: deploying a high-speed parallel acquisition sensor for an MEMS optical fiber multi-parameter sensor, acquiring spectral reflection and spectral time domain data, and constructing an original multi-parameter spectral data set; performing signal separation processing on the original multi-parameter spectral data set based on a preset hybrid network to obtain a wavelength-light intensity matrix, an input layer based on the preset hybrid network being the original multi-parameter spectral data set, and an output layer being the wavelength-light intensity matrix; therefore, by constructing an integrated data flow of original spectrum data acquisition, feature decoupling, parameter fusion and performance evaluation, the problems of large coupling error, weak noise suppression and uncontrollable demodulation precision in a traditional optical fiber multi-parameter demodulation model are solved; and the demodulation accuracy and practicability of the MEMS optical fiber sensor under the multi-physical-quantity complex working condition are improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable operation state real-time evaluation system based on multi-mode deep learning, and the system comprises the steps: synchronously collecting the load, partial discharge and temperature strain data of a cable through a current and voltage sensor, an ultrahigh frequency sensor and a distributed optical fiber sensor; feature extraction and cross-dimension fusion are carried out on the multi-source data, and a multi-dimensional feature matrix is constructed; a pre-trained deep learning model is utilized to analyze internal association between the features, and cable health degree scores, fault risk levels and defect type identification results are output; automatically generating and executing a load adjustment, loop switching or precise maintenance strategy according to an evaluation result; according to the method, the problems of inaccurate evaluation and early warning lag caused by data isolation analysis of a traditional monitoring method are solved, and real-time accurate evaluation and intelligent closed-loop operation and maintenance of the operation state of the cable are realized.

The invention discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

The invention relates to the technical field of power optical cable monitoring, and discloses a power optical cable operation situation sensing method, which comprises the steps of collecting temperature, strain, vibration and scattering signal data along an optical cable in real time through a distributed optical fiber sensor, and constructing a multi-source heterogeneous data set in combination with power grid operation data and environment monitoring data; carrying out denoising, abnormal value elimination and missing value filling processing on the data by adopting a time sequence synchronization algorithm, and extracting a temperature rise rate and a strain fluctuation frequency to generate a multi-dimensional feature matrix; by dynamically fusing multi-source data, situation awareness indexes such as a thermal stability score and an overload risk index are generated; an LSTM algorithm and a dynamic threshold model are combined to predict the operation risk level of the optical cable in the next 24 hours, a power grid dispatching system is linked through visual means such as a situation map and three-dimensional simulation, and intelligent alarm and operation and maintenance suggestions are automatically issued. The fault identification precision can be improved, the operation and maintenance cost can be reduced, and preventive maintenance can be realized.

The invention discloses a real-time monitoring, regulating and controlling method for concrete pouring cracks of a high-rise building, and the method comprises the steps: arranging a distributed optical fiber sensor array and an ultrasonic detector in a concrete pouring region of the high-rise building, and collecting the data of a temperature field and a strain field in concrete in real time based on the distributed optical fiber sensor array; the collected data parameters are transmitted to a data processing terminal, and a crack early warning model is established in combination with concrete material characteristic parameters; analyzing the received data by using a crack early warning model, and judging whether the concrete has cracks and the development trend of the cracks; and when the crack is monitored or the crack development risk exists, the regulation and control system is started. The system has the beneficial effects that all-directional real-time monitoring of the internal state of concrete is realized through combination of distributed optical fibers and ultrasonic detection, the coverage range is wider, and data is more comprehensive. A crack early warning model constructed based on multiple parameters is combined with a deep learning architecture, so that the precision of crack identification and trend prediction is greatly improved.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality 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 third-party construction monitoring method based on distributed optical fiber sensors. The method comprises the following steps of: accessing a base distributed optical fiber sound sensing system into an existing communication optical fiber at a road side or below a road, monitoring optical fiber vibration signals acquired at different positions and at different time periods of the road section, and carrying out noise reduction on the optical fiber vibration signals to obtain the optical fiber vibration signals after noise reduction; calculating the time domain characteristics of the optical fiber vibration signal after noise reduction, screening out an area suspected to have third-party construction damage, and obtaining a screened signal; the screened signals are analyzed, and time-frequency domain characteristic indexes related to third-party construction are extracted; inputting the time-frequency domain characteristic indexes related to the third-party construction into a third-party construction damage identification and classification model, determining a construction damage identification result of an area suspected to have third-party construction damage, performing rechecking, determining the area with the third-party construction damage and damage type records, giving an alarm, and recording a processing result at the same time. And the service condition of facilities in the area can be accurately monitored.

The invention discloses a concrete anti-cracking method and system based on fiber reinforcement, and belongs to the technical field of concrete anti-cracking. The method comprises the following steps: acquiring real-time strain data in concrete through a distributed optical fiber sensor; analyzing the data to obtain a three-dimensional crack propagation path containing a crack initiation position, a propagation angle and a tip position; identifying the dominant development direction of the crack and a fan-shaped potential expansion interval which extends at an expansion angle by taking the tip as a vertex based on the path data; in the potential expansion interval of the to-be-poured area, a fiber distribution layer arranged in the dominant development direction is formed through directional arrangement equipment; and the fiber-reinforced anti-cracking structure is formed through vibrating and compacting treatment. According to the invention, directional blocking of fibers to crack expansion is realized, the problem of disordered expansion of cracks is effectively solved, and the anti-cracking efficiency of concrete is improved.

The invention discloses a kilometer deep well roadway toughness regulation and control supporting system and a dynamic design method, and belongs to the field of deep well roadway supporting. The supporting system comprises a multi-layer collaborative structure and an intelligent regulation and control module, the multi-layer collaborative structure sequentially comprises a shape memory alloy energy absorption layer used for resisting impact loads and a gradient pore foam metal buffer layer used for energy grading dissipation from inside to outside, and the multi-layer collaborative structure is connected with roadway surrounding rock in an anchoring mode through an anchor rod anchoring structure; the intelligent regulation and control module comprises a distributed optical fiber sensor array used for monitoring surrounding environment signals of surrounding rock in real time, an electrostriction driver used for regulating and controlling the rigidity of the multi-layer cooperative structure and a multi-channel data fusion terminal used for achieving underground and surface data transmission synchronization. By the adoption of the kilometer deep well roadway toughness regulation and control supporting system and the dynamic design method, the problems that a traditional supporting mode is too high in material rigidity, lacks dynamic regulation and control capacity and is insufficient in impact toughness can be solved.

The invention provides an optical fiber force-shape sensing device and method through a natural orifice, and relates to the technical field of optical fiber sensing, the device comprises an operation arm mechanism, and specifically comprises a front end connecting joint, a plurality of movable joints and a tail end connecting joint which are sequentially and rotatably connected through a ball head and a ball socket, at least one limiting bulge is distributed on the circumferential surface of the ball head, and the limiting bulge is connected with the front end connecting joint; limiting grooves in one-to-one correspondence with the limiting protrusions are formed in the ball socket, and the limiting protrusions are in sliding fit with the limiting grooves so as to limit in-situ torsion between the adjacent joints. Through mutual cooperation of the limiting grooves and the limiting protrusions, in-situ torsion is reduced, torsional strength is improved, a shape-following embedded optical fiber sensor integration method is provided, shape reconstruction errors caused by movement dislocation are reduced, and protection of optical fibers is achieved. A first driving wire, four second driving wires, two optical fiber shape sensing modules and four optical fiber force sensing modules are embedded in the operation arm mechanism, and real-time accurate measurement of interaction force and the bending shape of the operation arm mechanism can be achieved.

The invention relates to an intelligent monitoring and analyzing method for the over-capacitance state of a transformer, and belongs to the field of power equipment detection. Comprising the following steps: acquiring internal temperature and oil flow velocity of a transformer through a distributed optical fiber sensor array, and fusing to generate a temperature distribution diagram and an oil flow velocity field; comparing historical data to identify dynamic dead angles, detecting oil flow velocity abrupt change to judge turbulent flow conversion, and predicting dead angle evolution based on the incidence relation between turbulent flow and dead angles; high-temperature gathering points and monitoring blind areas are identified, the acquisition frequency and density of a sensor are adaptively adjusted, the operation state of the transformer is evaluated, key risk points are extracted, and finally a precise monitoring scheme is generated. According to the method, the dynamic monitoring precision during super-capacity operation is improved, the fault risk caused by high temperature and dead angles is reduced, and intelligent support is provided for safe operation of the transformer.

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.

The invention discloses a battery performance testing system based on a deep-sea low-temperature ultrahigh-pressure environment, and belongs to the field of battery performance testing, the battery performance testing system comprises a console and a testing cabin, and a watertight connector is arranged in the testing cabin; through cooperative use of the console, the test cabin, the watertight connector, the electrochemical tester, the distributed optical fiber sensor and the macroscopic monitoring module, the limitation of single function in the prior art is broken through, the watertight connector guarantees electrochemical parameter transmission, and the distributed optical fiber sensor is implanted into a battery cell to realize internal strain temperature monitoring. And the macroscopic monitoring module covers appearance and size monitoring through integrated design of a fixing ring, and data of electrochemical performance of the battery, microcosmic material state (such as internal strain and temperature of the battery cell) and macroscopic galvanic pile structure change (such as appearance bulge and size compression) can be synchronously acquired through cooperation of the three modules, so that multiscale data support from microcosmic to macroscopic is provided for research personnel.

The invention provides a method for constructing an arch-shaped large-span shell steel roof, and belongs to the technical field of arch-shaped large-span shell steel roof construction. A temporary supporting system is built, and distributed optical fiber sensors are arranged to monitor the lifting process of a main arch; a cable force distribution scheme of a cable supporting system is calculated by adopting a double-layer game optimization model to realize cooperative control of stress uniformity and form precision, tensioning force is applied by using an intelligent tensioning system in stages, and a crane moving path is planned in combination with a path optimization algorithm based on a graph theory to realize graded unloading of a temporary supporting system. The technical problem that it is difficult to achieve structural stress uniform distribution and form accurate control of an arched large-span shell steel roof at the same time in the tensioning process of a cable supporting system is solved.

The invention relates to an intelligent method for controlling the shape of a fiber flexible body in the operation process, and belongs to the field of flexible body shape control. The method comprises the steps that point cloud data of a fiber flexible body and pose data of a manipulator tail end clamp holder are obtained; inputting the trained PIGNN model, and outputting shape prediction of the fiber flexible body; and a model prediction control method is adopted to calculate a motion track required by the mechanical arm, so that the mechanical arm controls the fiber flexible body to reach a target shape position. According to the method, the shape change predicted through the PIGNN model strictly follows the mechanical law of a fiber flexible body material, and the consistency and reliability of the optical fiber sensor can be greatly improved; model prediction control can predict and dynamically adjust the track of a mechanical arm in real time according to the shape, the processing requirements of optical fibers of different specifications are flexibly adapted, physical constraint and data driving are combined, so that the system can quickly adapt to a new scene, and a core technical foundation is laid for large-scale and high-precision application of an optical fiber sensor.

The invention relates to the technical field of power system monitoring, and discloses an intelligent power grid fault monitoring method based on artificial intelligence, which comprises the following steps: step 1, acquiring multi-modal sensing data through a multi-modal sensing network deployed on power grid equipment, the multi-mode sensing data comprises gas characteristic data acquired by an olfactory chip array, temperature data acquired by a distributed optical fiber sensor and electrical data acquired by a current transformer; and step 2, sending the multi-modal sensing data to an edge computing node, wherein a first data processing model is built in the edge computing node. According to the method, the technical scheme that the lightweight model is deployed through the edge computing nodes and graded early warning is linked is adopted, the technical effects of real-time response and rapid fault isolation are achieved, and compared with the scheme depending on cloud centralized processing in the prior art, the defect of protection action lagging caused by fault diagnosis delay is overcome.

The invention discloses a BIM-based cable construction method. The method comprises the steps of obtaining BIM model data including a cable design path, a design bending radius and design stress; in the cable laying process, the optical fiber sensors are tightly attached and fixed to the surface of an outer sheath of the cable to be laid, and the optical fiber sensors are continuously distributed in the length direction of the cable; acquiring actual strain distribution data and actual bending radius distribution data of the cable measured by the optical fiber sensor in real time, and performing spatial position correlation comparison with design stress data and design bending radius data in the BIM model; if the actual strain value of any position point exceeds the strain threshold range corresponding to the design stress or the actual bending radius value is smaller than the design bending radius value, it is judged that construction deviation exists in the position point; and generating an adjustment instruction for subsequent cable laying operation based on the position point information with the construction deviation. The cable laying precision and the construction safety can be effectively improved.

The invention provides a blasting vibration waveform feature extraction and damage range intelligent calibration system. The method comprises the following steps: calculating a waveform irregularity sequence based on original vibration waveform data; then, a fiber grating sensor network is used for collecting strain vibration waves on the surface of the structure, and spatial distribution information of the strain change rate of the structure is mapped into a feature enhancement channel; learning a feature distribution mode based on the waveform data processed by the feature enhancement channel by using a coding and decoding structure, and performing reconstruction operation on the current blasting vibration waveform according to the feature distribution mode; identifying and amplifying abnormal feature differences by comparing an actual waveform irregularity sequence with a reconstruction sequence, and generating reconstruction error peak distribution; and finally, when the strain gradient abrupt change point position detected by the optical fiber sensor network is overlapped with the reconstruction error peak point space, generating a blasting damage area thermodynamic diagram based on the overlapped point space density. The precision and reliability of blasting damage range calibration are improved.

The invention discloses a water conservancy management system and method based on digital twinning, and relates to the technical field of water conservancy management, and the method comprises the steps: collecting a water flow sound wave signal through vibration optical fiber sensors disposed in a water conservancy facility and a water channel, and converting the water flow sound wave signal into a digital pulse signal carrying flow velocity, impact force and flow direction information; constructing a water conservancy topology network based on a water conservancy project drawing; geographic units are divided by expanding a preset distance along the boundary of the water channel, and a geological memory capsule is created for each geographic unit; when a scheduling instruction is received, retrieving a target geological memory capsule in an influence range of the scheduling instruction, and calling an evolution operator of the target geological memory capsule to deduce geological responses of different time scales in parallel; and performing spatial overlay analysis on the position of the real-time water flow blocking point identified by the water conservancy topology network and the geological vulnerable area distribution map, and generating and executing a water conservancy facility regulation and control instruction by taking minimization of an overlapping area of the two as a target.

The invention discloses an FPI and MZI optical fiber sensor based on a PDMS microcavity and application thereof, relates to the technical field of optical fiber sensors, and aims to solve the problem of lack of an optical fiber sensor with a double-effect cooperative regulation mechanism in the prior art. According to the technical key points, the FPI and MZI optical fiber sensor based on the PDMS microcavity is composed of a first single-mode optical fiber, a multimode optical fiber, the PDMS cavity and a second single-mode optical fiber which are connected in sequence, the first single-mode optical fiber and the multimode optical fiber are welded, the microcavity exists between the multimode optical fiber and the second single-mode optical fiber, PDMS is injected into the microcavity to form the PDMS cavity, and the PDMS cavity is connected with the PDMS microcavity. One end, far away from the PDMS cavity, of the second single-mode optical fiber is a cutting end surface; the critical surface of the multimode optical fiber and the PDMS cavity is a reflector 1, the critical surface of the second single-mode optical fiber and the PDMS cavity is a reflector 2, and the reflector 1 and the reflector 2 form a Fabry-Perot interferometer. The sensor combines the working principles of FPI and MZI, and can realize accurate measurement of temperature.

The invention relates to the technical field of oil pipe leakage detection, in particular to an underground oil pipe leakage detection system, which comprises an underground permanent monitoring unit, which comprises at least one optical fiber sensor, and the optical fiber sensor clings to the outer wall of a production oil pipe or is integrated in an oil pipe coupling and extends along the depth of a whole shaft; the ground signal processing and analyzing unit is in communication connection with the underground permanent monitoring unit; wherein the optical fiber sensor is used as a distributed temperature sensor and a distributed sound wave vibration sensor at the same time, real-time on-line monitoring of oil pipe leakage is achieved, early warning can be given out at the initial stage of leakage, the accuracy of leakage recognition is improved through collaborative analysis of bimodal sensing data, the false alarm situation is effectively reduced, and the reliability of oil pipe leakage detection is improved. A distributed measurement mode provides meter-scale spatial resolution, the depth position of a leakage point is accurately positioned, normal production operation does not need to be interrupted during system operation, and the operation cost of an oil field is reduced.

The present disclosure provides a method of processing data obtained from distributed optical fiber sensors to detect acoustic energy generated by a poroelastic effect of fractures in a structure, such as a rock formation. The sensing fiber of an optical fiber distributed sensing system may be deployed in the vicinity of the region where fracturing is occurring, for example, along a well that is offset from a treatment well undergoing hydraulic fracturing. The DAS data obtained from along the sensing fiber is processed to measure changes in the low-frequency strain caused by the poroelastic effects in the rock as the fractures open and close. This measured strain rate data is iteratively processed at each instant time to identify fracture opening features (characterised as compression-tension-compression) that are correlated with fracture closing features (characterised as tension-compression-tension) as a function of depth, to thereby identify and locate fracture hits in the vicinity of the sensing fiber.

The invention discloses a seepage monitoring method and system based on an optical fiber sensor, and relates to the technical field of slope seepage monitoring, and the method comprises the steps: distributing distributed optical fiber sensors along an inclinometer pipe in a slope; laying an optical fiber heating system; a distributed temperature sensor DTS is used for collecting scattered light signals of the distributed optical fiber sensor; the scattered light signals collected by the DTS are demodulated; acquiring strain data in the slope; performing temperature compensation on the obtained displacement data; according to the obtained distributed temperature data and the displacement data after temperature compensation, the position and range of seepage in the slope are determined; calculating the seepage rate and the seepage flow of the corresponding position through the seepage model and the heat conduction equation; and judging the seepage degree of the side slope. According to the invention, aiming at the low precision of slope seepage monitoring in the prior art, the distributed temperature sensor DTS is utilized to collect the temperature distribution data of the optical fiber along the way, and temperature compensation and the RBF neural network model are combined, so that the precision of judging the slope seepage is improved.

The invention provides an FPI optical fiber temperature sensor based on a PDMS filled capillary glass tube, which comprises a capillary glass tube, one end of the capillary glass tube is filled with cured polydimethylsiloxane, the other end of the capillary glass tube is inserted with a single-mode optical fiber, in the capillary glass tube, the end surface of the polydimethylsiloxane is a concave surface, and the end surface of the single-mode optical fiber is a convex surface. The end face of the single-mode optical fiber is a plane, the end face of the polydimethylsiloxane and the end face of the single-mode optical fiber form two reflecting faces of the Fabry-Perot cavity, light input is carried out in the single-mode optical fiber, and light output is carried out in the Fabry-Perot cavity. Displacement of the end face of the polydimethylsiloxane as a function of a temperature change causes an interference wavelength in the Fabry-Perot cavity to be adjusted to map the temperature change. The invention provides the PDMS-filled SCT-based micro-air cavity FPI optical fiber sensor which is simple in structure, easy to manufacture, low in cost, small in size and ultrahigh in precision, and is used for high-precision temperature detection.

The application discloses a kind of EFPI optical fiber sensor multi-sensor multiplexing temperature-pressure demodulation system and method, including ASE broadband laser, optical circulator, EFPI optical fiber sensor, 2-way wavelength division multiplexer, N-way wavelength division multiplexer, N-way wavelength division demultiplexer, photoelectric detector, FBG, 1*2 optical fiber coupler, optical spectrometer, data processing system;The input port input of the optical circulator is connected with ASE broadband laser, the output port cir2 is connected with two cascaded FBGs, the output port cir3 is connected with EFPI optical fiber sensor, the input port of 2-way wavelength division multiplexer is connected with the output port cir4 of optical circulator, and the output port is connected with N-way wavelength division multiplexer;N-way wavelength division multiplexer is accessed via the input port of 1*2 optical fiber coupler, and outputs two-way signals, one-way is connected with N-way wavelength division demultiplexer via N-way photoelectric detector and data processing system, and the other way is connected with optical spectrometer;N channels are connected by cascaded FBG, and the cascade of N EFPI optical fiber sensors is realized.

The invention relates to the field of deformation monitoring, in particular to an underground pipeline deformation real-time monitoring method and device, equipment and a medium. The method comprises the steps that through real-time connection with equipment such as an optical fiber sensor and a three-dimensional laser scanner, spectral signals can be received in real time, three-dimensional point cloud data can be collected, and data collection delay is avoided. In a data processing link, whether a strain distribution map is generated based on a spectral signal or a deformation displacement vector is determined from three-dimensional point cloud data, an efficient algorithm is adopted to quickly complete calculation and analysis, and the data processing time is greatly shortened. Moreover, the electronic equipment continuously monitors the strain distribution map and the deformation displacement vector in real time, and once it is monitored that the strain gradient and the displacement change rate of the monitoring node in the pipeline deformation field model exceed threshold values, an early warning mechanism is triggered immediately, deformation early warning information is generated, and the real-time performance of underground pipeline deformation monitoring is improved.