1222 results about "Optical fibre sensor" patented technology

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering dam safety monitoring and early warning method and system, which realize comprehensive perception and accurate early warning of the health state of a dam structure through a composite sensing technology and an intelligent analysis algorithm. A micro-mechanical resonance sensor and a distributed optical fiber sensor are cooperatively deployed, and an interface and structure integrated three-dimensional monitoring network is constructed; a three-dimensional interface stripping characteristic spectrum is constructed based on a time-frequency conjoint analysis technology, and the bonding degradation state between the sensor and the dam body is accurately identified; a strain field anomaly distribution matrix is established through spatial correlation modeling, precise positioning of internal damage is realized, a dual-channel feature fusion network and a deep neural network evaluator based on an attention mechanism are designed, and multi-dimensional correlation analysis is performed on an interface state and structural damage features; and finally, realizing progressive response from data verification and multi-source verification to emergency linkage through a three-level linkage early warning decision tree.

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

The invention discloses a loess tunnel surrounding rock deformation monitoring method, and relates to the technical field of civil engineering and geological engineering, and the method comprises the steps: integrating a spiral winding type optical fiber sensor, a double-cavity humidity compensation vibrating wire sensor and a microseismic array, and capturing surrounding rock strain, vibration and geological activities; the vibrating wire sensor suppresses humidity interference through a silicone oil damping medium and self-adaptive excitation frequency, and the optical fiber sensor is fixed through a pre-embedded silica gel sleeve to adapt to surrounding rock deformation; edge computing nodes are deployed on the inner wall of the tunnel, an FPGA chip and a lightweight GRU model are integrated, optical fiber strain, a micro-seismic energy spectrum and laser point cloud displacement field data are fused in real time, the strain gradient is analyzed, and a crack propagation probability cloud picture is generated; redundant optical fiber link switching is combined with a six-degree-of-freedom mechanical arm to realize breakpoint self-repairing, and a self-cleaning air curtain is integrated to inhibit dust adhesion; and constructing a geological parameter library based on a BIM-GIS fusion platform, driving a finite element-discrete element coupling model to dynamically update boundary conditions, and predicting surrounding rock deformation and collapse risks.

The invention discloses a landslide hidden danger point displacement monitoring system applying multi-source data fusion, and the system comprises a multi-source sensing unit which comprises a spaceborne radar sensing module which is used for obtaining large-range ground surface deformation data through a synthetic aperture radar; the earth surface deformation sensing module is used for monitoring earth surface displacement in real time through a double-frequency GNSS receiver and an optical fiber grating sensor; the underground stress sensing module is used for acquiring the stress change of an underground rock-soil body through a micro-seismic monitoring array and a distributed optical fiber sensor; and the edge calculation unit comprises a dynamic weight fusion module which is used for dynamically distributing the weight of the multi-source sensor according to the geological state and generating high-precision fusion displacement field data. According to the invention, through multi-source data fusion, model parameter real-time updating and a closed-loop optimization mechanism, bottlenecks of a traditional monitoring system in aspects of data quality, model precision and response efficiency are broken through, and data support is provided for accurate early warning and intelligent emergency of landslide disasters.

The invention discloses a long-distance communication optical cable fault automatic detection and positioning system and method, and the method comprises the steps: collecting optical signal data in an optical fiber link in real time through the deployment of a multi-optical fiber sensor or a distributed optical fiber sensing technology; denoising processing is carried out on the collected data, abnormal fluctuation is analyzed by applying a self-adaptive algorithm, and the type of a fault which is about to occur is identified; classifying the faults by using a machine learning classification algorithm; time domain reflection and wavelength multiplexing technologies are combined, a composite algorithm is adopted to accurately position a fault point, and path optimization and error correction are carried out through sensing node data comprehensive analysis; an environment compensation algorithm is introduced, influences of environment factors on the optical signals are analyzed in real time, and errors are eliminated; and based on a fault positioning and analysis result, an AI algorithm is adopted to evaluate a fault influence range, a repair scheme is automatically generated, and priorities are scheduled for repair.

The invention discloses a multi-mode perception and reinforcement learning engineering machinery intelligent regulation and control method and system, and belongs to the technical field of intelligent control and industrial automation. According to the system, stratum data within the range of 20-50 meters in front of a shield tunneling machine are collected in real time through multi-mode sensing equipment such as a distributed optical fiber sensor, a cutterhead vibration sensor and an electromagnetic wave radar, and original data are processed by adopting a wavelet-Fourier combined noise reduction algorithm; and inputting the processed multi-modal data into a reinforcement learning intelligent decision-making model based on a CNN-LSTM hybrid network architecture. The model is trained through a dynamic reward function, and weight coefficient combinations can be automatically switched according to different construction scenes. According to the system, parameters such as the rotating speed, the thrust and the grouting amount of a shield cutter head are regulated and controlled in real time through the self-adaptive control module, and the slurry utilization rate is increased through the gradient pulse grouting technology. The precision, efficiency and safety of shield construction are remarkably improved, and meanwhile energy consumption and construction cost are reduced.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention discloses a tunnel convergence deformation monitoring method, and particularly relates to the technical field of tunnel monitoring, through integrating equipment such as a thermal infrared imager, a visible light camera, a distributed optical fiber sensor and a laser radar, a self-adaptive inspection system is constructed, and high-precision appearance and temperature monitoring is realized by adopting an improved ORB algorithm and a temperature field calibration technology. Performing multi-source data fusion through space-time registration and wavelet packet decomposition in combination with optical fiber strain data; and predicting a deformation trend based on an LSTM neural network, and establishing a three-level early warning mechanism. According to the tunnel convergence deformation monitoring method disclosed by the invention, the automation and intelligence level of tunnel structure safety monitoring is remarkably improved, 'perception-analysis-decision 'closed-loop management of the tunnel health state is realized, compared with a traditional method, the efficiency is remarkably improved, meanwhile, the maintenance cost can be greatly reduced, and the tunnel operation safety and economy are remarkably improved.

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 provides a tunnel deformation parameter inversion method and device based on distributed optical fibers, and relates to the technical field of tunnel deformation parameter inversion, and the method specifically comprises the steps: selecting a monitoring point in a tunnel, arranging an optical fiber sensor, and obtaining a Brillouin scattering signal generated by the optical fiber sensor under the stress effect of the corresponding monitoring point; extracting a Brillouin frequency shift corresponding to the monitoring point, and determining an environmental effect frequency shift correction value by using the temperature change data, the humidity change data and the service time of the optical fiber sensor; determining the structural strain of the monitoring point according to the Brillouin frequency shift and the environmental effect frequency shift correction value, generating a three-dimensional strain field cloud picture, performing spatial registration on the three-dimensional strain field cloud picture and the geometric topology of the model, and performing three-dimensional grid division; the structure strain of the monitoring point is mapped to the grid node at the corresponding position, the structure strain of other grid nodes is determined based on the space distance attenuation law, inversion of the whole tunnel deformation parameters is completed, and the tunnel deformation monitoring precision and efficiency are improved.

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 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 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 present application discloses a battery management method, a device, a storage medium, an electrical apparatus, a battery, and a system. The battery management method comprises: acquiring temperature change data of a change in battery charge, and central wavelength change data of an optical fiber sensor, a change value of battery charge corresponding to the temperature change data being related to a change value of battery charge corresponding to the central wavelength change data; and, according to an impact relationship of the temperature change data on the central wavelength change data, determining expansion force data of the change in battery charge. Thus, the expansion force data of the change in battery charge can be obtained by means of combining the temperature change data with one optical fiber sensor, thus alleviating the problem of high costs caused by multiple optical fiber sensors, and effectively relieving adverse risks such as battery diving, and electrode plate or battery cell rupture, caused by abnormal battery expansion force, and improving the usage safety performance of a battery.

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 marine environment pile foundation damage identification method based on optical fiber distributed strain monitoring, and belongs to the technical field of structural health monitoring and ocean engineering. The marine environment pile foundation damage identification method comprises the steps that distributed optical fiber sensors are evenly distributed in the axial direction of a pile foundation and used for strain monitoring; acquiring strain response data of the pile foundation along the pile length under the action of the wave force load through a distributed optical fiber demodulator; identifying modal characteristic parameters of the pile foundation structure by applying a random subspace identification algorithm, wherein the modal characteristic parameters comprise modal frequency and modal shape; calculating modal strain energy based on the modal shape, and obtaining a modal strain energy distribution diagram; identifying a damage area, judging whether a set threshold value is exceeded or not by comparing the change of the modal strain energy between the monitoring state and the reference state, judging the damage, and obtaining a damage identification result. The method provided by the invention has the advantages of high spatial resolution, high recognition precision, good environmental adaptability, accurate positioning, high automation, wide applicability and the like.

The invention provides a chemical pipeline flange loosening leakage early warning method and system based on distributed optical fiber sensing, and relates to the technical field of chemical safety monitoring. According to the system, a double-parameter distributed optical fiber sensor is arranged on the outer circle of a flange in a spiral winding mode, temperature and vibration signals of the flange are collected in real time and preprocessed, and the real-time change value of the temperature difference between a leakage point and the environment and the vibration energy value of the 20-200 Hz frequency band are obtained. Establishing a multi-modal signal fusion criterion model which comprises a vibration criterion (the sudden increase of vibration energy is greater than or equal to 3dB) and lasts for more than 10 seconds, and calculating the looseness degree of the bolt according to the vibration criterion; the real-time change of the temperature criterion is greater than or equal to + / -0.5 DEG C and lasts for more than 10 seconds; and a gradient early warning mechanism is implemented through the data model. The technology fills the blank of flange looseness monitoring and leakage early warning, realizes the sensitivity and reliability of flange looseness and leakage monitoring, and ensures the safety of chemical production.

The invention discloses a reinforcement learning driving-based cavern group distributed optical fiber temperature field dynamic monitoring system, and relates to the technical field of underground engineering safety monitoring, and the system comprises a distributed optical fiber sensor array which is crosswise and annularly arranged along the inner wall of a cavern group and is used for collecting original optical signals containing temperature and strain coupling. According to the cavern group distributed optical fiber temperature field dynamic monitoring system based on reinforcement learning driving, through a dynamic coupling and decoupling mechanism of reinforcement learning and a physical model, the precision bottleneck of a traditional optical fiber temperature monitoring technology under complex multi-physics field interference is broken through. A dual-channel sensing network and a self-evolution interference feature library are constructed, real-time adaptive separation of strain-temperature components is realized, a dual heterogeneous verification mechanism is introduced, and the credibility of a decoupling result is guaranteed from two dimensions of a physical rule and data redundancy; and meanwhile, through closed-loop optimization and redundant fault-tolerant design, the monitoring robustness and continuity in a complex engineering environment are remarkably improved.

The invention discloses a battery cell equalization management system of an energy storage battery, and relates to battery equalization management, the battery cell equalization management system comprises an equalization control unit, a parameter coupling calculation module, a state traceability tracking module and a multi-physics field coupling module, and the state traceability tracking module stores full life cycle parameters of a battery cell by constructing a three-dimensional space-time coordinate system. According to the method, through coordinated operation of a multi-level dynamic regulator, voltage gradient spectrum analysis, nonlinear temperature compensation and convolution recursion calculation are fused for the first time, and the temporal-spatial resolution of parameter coupling modeling is remarkably improved; on the hardware architecture level, synchronous analysis of an electrode expansion strain field and an electrolyte concentration field is achieved through combined application of a distributed optical fiber sensor and a four-dimensional tensor calculation method; particularly, the improved residual memory network effectively captures weak correlation signals of dynamic processes such as capacity attenuation activation and polarization relaxation through a staged recession feature extraction mechanism.

The utility model provides a special-shaped sealing ring installation device which aims at efficiently and accurately installing a special-shaped sealing ring (2) into a piston workpiece (1), a jacking mechanism (8) and an inner supporting expansion mechanism (6) are arranged in the center of a supporting installation plate (3) of the device, a displacement hole movable plate (61) is driven to rotate through a telescopic air cylinder (10), the inner diameter of the special-shaped sealing ring (2) is expanded and enlarged, and the piston workpiece (1) is installed in the piston workpiece (1). And the expansion range of the special-shaped sealing ring (2) is controlled through the adjustment of the adjustable limiting assembly (9). The jacking mechanism (8) drives the special-shaped sealing ring jacking ring (5) through a jacking air cylinder (11) so that the special-shaped sealing ring (2) can be separated from the inner supporting expansion mechanism (6) and installed in a groove of the piston workpiece (1). A penetrating type notch groove (51) is formed in the special-shaped sealing ring top ring (5), the effect that the optical fiber sensor (12) detects the in-place installation condition of the piston workpiece (1) is achieved, and the accuracy and efficiency of sealing ring installation are guaranteed.

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 discloses a seismic signal-based soil-structure co-evolution seismic liquefaction analysis device and method, and the method comprises the steps: obtaining geological and seismic data of a target region, and constructing a geometric ratio, time ratio and acceleration ratio co-scaling system based on a Bockingham Pi similarity theory; a seismic liquefaction multi-field coupling evolution device is constructed to evolve the soil liquefaction process of a target area, a distributed optical fiber sensor array and a micro pore pressure meter group are adopted to form a millimeter-level spatial resolution monitoring network, and a high-speed camera system is matched to synchronously capture pore water pressure, structural strain and a soil shear band expansion process. Total element time sequence correlation analysis of vibration triggering, seepage developing and structural catastrophe is achieved, the influence degree of the total element time sequence correlation analysis on the earthquake is further analyzed, and a direct basis is provided for engineering restoration priority and a reinforcement scheme.

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 provides a communication equipment fault prediction method and system based on big data and an optical fiber sensor. The method comprises the following steps: generating a dynamic physical topology model according to a multi-source heterogeneous data matrix in combination with a light attenuation characteristic value and a signal transmission direction identifier of a distributed optical fiber sensor; based on a spatial constraint condition of the dynamic physical topology model, separating an abnormal fluctuation component associated with at least one of temperature abnormal data and vibration abnormal data of the communication equipment from the optical signal data to generate a cross-modal abnormal associated feature set; and through a collaborative constraint rule, analyzing the cross-modal abnormal association feature set, and generating a communication equipment fault prediction result for early warning in advance. According to the technical scheme provided by the invention, the multi-source heterogeneous matrix and the dynamic topology model are constructed based on the distributed optical fiber sensing data, and accurate early warning is carried out on the fault position and type of the communication equipment in advance through cross-modal abnormal association feature analysis.

The invention relates to the technical field of underground structure management, in particular to an underground structure full life cycle management method and system based on fusion sensing data. According to the technical scheme, the system comprises a sensing layer, a data transmission layer, a data processing and analysis layer and a decision support and application layer. Through cooperation of the distributed optical fiber sensor and the multiple sensors, all-around and multi-parameter monitoring of the underground structure is achieved, the monitoring accuracy and reliability are improved, historical data and operation rules of the structure are automatically learned through a data driving prediction model, different geological environments and load conditions are adapted, a scientific basis is provided for maintenance decision making, and meanwhile, the system has good application prospects. The self-adjusting capability is realized; monitoring system application and data accumulation and utilization in each stage are considered, seamless connection and cooperative work are achieved, full-life-cycle integrated management is achieved, the overall performance of the structure is improved, the service life of the structure is prolonged, the maintenance cost is reduced, and sustainable development is promoted.

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.