28345 results about "Optical fiber" patented technology

The invention provides a technical condition prediction method for a tunnel civil engineering structure, and relates to the technical field of traffic control monitoring systems.A biological acoustic composite sensing system comprising a distributed optical fiber acoustic sensing system and microorganism sample collection and analysis is constructed, and acoustic depth mode characteristics are mined by applying a deep learning model; the microbial dynamic biomarker is analyzed and identified by using bioinformatics; multi-modal information, a domain knowledge graph and a causal inference algorithm are fused, and a dynamic causal network model for revealing an internal driving relation in the degradation process is constructed; further, training and applying an adaptive neural network prediction model based on causal driving and fusing physical and biochemical mechanism constraints, and carrying out probabilistic prediction on future technical conditions of the tunnel; and executing anti-fact reasoning by using the causal network and the prediction model, and generating and optimizing an intervention strategy of cooperation of physical measures and biochemical measures. According to the method, the accuracy and advance of tunnel structure state prediction can be remarkably improved.

The invention belongs to the technical field of water conservancy and hydropower engineering, and discloses a water conservancy and hydropower engineering construction safety supervision system based on multi-source data fusion. The system comprises a multi-source sensing acquisition module, a heterogeneous data fusion processing module, a risk identification and early warning module, a safety behavior evaluation and feedback module, and a command scheduling and visualization module. According to the invention, by fusing multi-dimensional data such as image monitoring, environment sensing, personnel positioning, equipment state and the like, a space-air-ground three-dimensional sensing network is constructed, and in a high slope area, the distributed optical fiber strain sensors are linked with thermal imaging data of the unmanned aerial vehicle, so that millimeter-level deformation and temperature field abnormity can be captured in real time; a video stream is analyzed in real time by means of a YOLOv8 algorithm, illegal operation behaviors of personnel can be accurately identified, a cross-modal fusion model of a Transform architecture is combined, the system can dynamically capture potential correlation among data, and millisecond-level response to risks such as side slope landslide, equipment faults and personnel dangerous operation is achieved.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention discloses an intelligent cable detection system and method, and particularly relates to the technical field of cable detection. High-precision harmonic analyzers are installed at the input end and the output end of a cable to capture low-order harmonic nonlinear change data in real time, a distributed optical fiber temperature sensor is used for collecting temperature rise rate data of a hot spot area, the data are preprocessed and subjected to feature extraction, harmonic distortion rate fluctuation features and temperature rise rate deviation features are extracted, and the harmonic distortion rate fluctuation features and the temperature rise rate deviation features are extracted. A data prediction model is constructed, and the accuracy of the on-line monitoring system on the potential risk of the cable is accurately evaluated; according to the method, the cable is divided into different grades according to accuracy values, targeted treatment measures are taken respectively, and especially for incomplete accuracy evaluation, accurate identification and real-time optimization monitoring of cable hidden faults are realized by dynamically predicting the accuracy deviation degree and adjusting the monitoring frequency and the detection range. And the accuracy of early warning of cable faults and the reliability of an online monitoring system are obviously improved.

The invention discloses a soft soil foundation deformation prediction method and system based on big data, and relates to the technical field of rock and soil monitoring. InSAR satellite data, Beidou GNSS displacement data, optical fiber strain data and meteorological and geological parameters are integrated through a multi-source sensing network. ERA5 reanalysis data is adopted to establish an atmospheric delay compensation function, and dynamic sliding window filtering and strain gradient constraint are combined to realize data space-time alignment and anomaly cleaning. Based on a generalized Kelvin creep constitutive model, environment coupling functions of temperature, humidity and pore water pressure are fused to dynamically correct model parameters, and the creep response characterization capability in a complex environment is enhanced. And performing distributed joint training on the regionalized geological data through a federated learning framework, fusing differential privacy encryption and a node credibility verification mechanism, realizing safety aggregation and migration optimization of cross-regional data, and generating a geological partition adaptive deformation prediction result. The method effectively improves the reliability of soft soil foundation deformation prediction.

The invention relates to the technical field of power equipment state monitoring, in particular to an oil-immersed transformer distributed temperature measurement method based on a fluorescent optical fiber sensor, and the method comprises the steps: arranging the fluorescent optical fiber sensor in a key temperature rise region in a transformer in a partitioned and layered three-dimensional topological structure, and forming a distributed temperature measurement network; exciting light is injected through a pulse laser to excite a fluorescence signal; a dual-channel phase-locked amplification technology is used to collect signals, and a dual-weight adaptive attenuation model and an oil flow coupling compensation function are combined to demodulate the temperature; reconstructing a dynamic temperature field based on a three-dimensional thermodynamic inversion algorithm, and marking high-gradient hot spots; and temperature, load current and oil flow velocity data are fused to realize graded alarm. According to the invention, the limitation of traditional single-point monitoring is broken through, the strong electromagnetic interference resistance is excellent, a global temperature field can be accurately reconstructed, dynamic early warning is realized, and the operation safety and the operation and maintenance efficiency of the transformer are remarkably improved.

The invention provides an underground cavern surrounding rock parameter collaborative sensing method and system based on multi-source data fusion, and the method comprises the steps: firstly deploying a sensor group comprising an optical fiber strain sensor, a micro-seismic monitoring array, a three-dimensional laser scanner and an acoustic emission sensor, the optical fiber strain sensor is arranged along the axis of a cavern, the micro-seismic monitoring array is deployed according to a triangular network, and the three-dimensional laser scanner is arranged along the axis of the cavern; performing time synchronization on the sensor group through a synchronization pulse signal, establishing a local coordinate system at a central point of a cavern entrance, uniformly collecting data, generating a time-aligned data stream and a space-unified data set, and triggering associated sensors to enter a hierarchical response collection mode according to a strain rate and a vibration signal; and generating a multi-modal monitoring data set, finally carrying out dynamic weight distribution and conflict resolution processing on the multi-modal monitoring data set, extracting surrounding rock deformation characteristics and fracture event density, and outputting a surrounding rock stability evaluation result, thereby realizing underground cavern surrounding rock parameter collaborative perception and stability evaluation.

A shape-sensing system can include a stylet, an optical interrogator, a console, and a display screen. The stylet can include an optical fiber with fiber Bragg grating (“FBG”) sensors along a length of the optical fiber. The optical interrogator can be configured to send input optical signals into the optical fiber and receive FBG sensor-reflected optical signals from the optical fiber. The console can be configured to convert the reflected optical signals with the aid of filtering algorithms of some optical signal-converter algorithms into plottable data for displaying plots thereof on the display screen. The plots can include a plot of curvature vs. time for each FBG sensor of a selection of the FBG sensors for identifying a distinctive change in strain of the optical fiber as the stylet is advanced into a superior vena cava of a patient.

The invention relates to the technical field of wind power generation, in particular to a long flexible blade monitoring method and system based on multi-source data fusion, which adopts a multi-source data fusion technology, collects optical fiber strain, vibration, environment and image data by arranging sensor nodes on a fan blade, and generates comprehensive feature data through preprocessing; and extracting potential feature vectors by a multi-modal auto-encoder, and mapping the potential feature vectors into predicted stress data by using a physical information neural network in combination with offline finite element simulation data and physical constraint conditions. Then, a control decision is generated through the multi-modal deep fusion network and hierarchical reinforcement learning, the blade state is adjusted in real time, and the model is optimized through a closed-loop feedback mechanism; according to the invention, precise monitoring and active regulation and control of special working conditions such as blade pollution and icing are realized.

The present invention belongs to the technical field of communication optimization, and in particular relates to a data synchronous-transmission optimization method and system based on analog optical fiber communications. In the present invention, after redundant data is generated in a communication device, a temporary transient cycle can be constructed, such that whether the redundant data is instantaneous data is determined, thereby avoiding erroneous construction of a monitoring cycle. In addition, after the monitoring cycle is constructed, trend analysis and prediction analysis are performed on parameters to be assessed, which are within the monitoring cycle, and further verification is performed on a trend analysis result, such that the monitoring cycle is adjusted within a suitable amount of time, thereby ensuring the accuracy of a finally obtained estimated redundant volume. Then, the estimated redundant volume is taken as a basis, and an output time sequence of pieces of communication data is adjusted in view of an assessment model and an adaptive optimization model, thereby ensuring the timeliness and validity of data outputted by the communication device.

A placement system for tracking, placing, and monitoring a catheter assembly or other medical device inserted into a body of a patient is disclosed. The placement system utilizes optical fiber-based strain sensors to assist with catheter placement. In one embodiment, the placement system comprises a console including a processor and a plurality of optical fiber-based strain sensors included with the catheter. A light source is also included and configured to operably connect with the strain sensors and produce outgoing optical signals incident on the strain sensors. A photodetector is included and configured to operably connect with the strain sensors and receive return optical signals from the strain sensors. A processor is configured to process data from the return optical signals. The data relates to an aspect of the catheter. A user interface such as a display is configured to communicate information relating to the aspect of the catheter.

Medical systems and devices include an elongate probe configured for insertion into a patient, where the elongate probe includes an atraumatic tip and where the atraumatic tip can include one or more of a loop, a curl, coil, a flexible distal region, and a steerable distal region. The distal region can be configured to transition away from a first shape toward a second shape upon insertion of the elongate probe within the patient. The optical fiber can include sensing core fibers configured to facilitate a determination of a physical state of the elongate probe. The elongate probe can also be configured to project illuminating light away from a distal end and receive imaging light at the distal end. The elongate probe can include a catheter.

The invention provides an optical fiber communication equipment monitoring method and system based on big data and a medium, and belongs to the technical field of optical fiber communication and big data. The method comprises the following steps: synchronously acquiring operating parameters and environmental parameters of optical fiber equipment through a multi-source sensor network, and generating a standard parameter information matrix through space-time alignment and noise suppression; further extracting characteristics such as an optical signal stability index and vibration energy anomaly through time-frequency domain conjoint analysis, and constructing an equipment health state matrix; and predicting a degradation process based on a bidirectional LSTM model of an attention mechanism, and outputting a visual diagnosis result in combination with a dynamic threshold grading alarm mechanism. According to the scheme, the problems of high false alarm rate and insufficient prediction capability of a traditional monitoring method are solved, the false alarm rate is reduced, and the fault prediction accuracy is improved.

The invention discloses an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

The invention discloses an efficient cross-season energy storage energy pile, and relates to the technical field of new energy and energy conservation. The problems that in an existing energy storage system, the thermal load prediction error is large, underground thermal diffusion attenuation is caused, the heat exchange capacity is lowered, and the geological adaptability is insufficient are solved. According to the scheme, an LA mixed time sequence prediction model is adopted to optimize load prediction, a distributed optical fiber temperature measurement array and a finite element inversion algorithm are combined to accurately reconstruct a stratum temperature field, and the geological type is identified based on a support vector machine classifier to realize self-adaptive regulation and control of heat exchange parameters; meanwhile, the heat pump power, the circulating pump frequency and the heat charging and discharging rate of the phase change material are optimized through a depth deterministic strategy gradient algorithm and a gradient heat release strategy; the heat exchange efficiency, the long-term stability and the complex environment adaptability of the cross-season energy storage system are remarkably improved, and the energy-saving effect of a building energy supply system is improved.

The invention relates to the technical field of communication, and discloses an optical fiber performance automatic test method, system and device based on optical power detection. The method comprises the following steps: carrying out intelligent scanning detection on an optical fiber network, generating optical fiber resource mapping data, obtaining an OTDR measurement configuration table through dynamic analysis, executing optical pulse injection and signal acquisition to obtain a time domain response characteristic curve, obtaining an optical fiber performance parameter set through wavelet denoising and peak value identification processing, and obtaining the optical fiber performance parameter set. And performing fault analysis by using deep learning and expert rules to obtain diagnosis decision data, and finally generating an optical fiber state evaluation report through digital twin modeling. According to the invention, remote automatic testing, intelligent fault diagnosis and visual monitoring management of the optical fiber network can be realized, and the operation and maintenance efficiency and reliability of the optical fiber communication network are improved.

A system, apparatus and method directed to placing a medical instrument in a patient body, where the system includes the medical instrument having a first optical fiber, a console and an interconnect having a second optical fiber to receive incident light from the console and propagate the incident light to the medical instrument. The interconnect includes a predetermined bend along its length, such that logic of the console may determine a positioning and an orientation of the medical instrument relative to the predetermined bend. Additionally, the logic may generate a display of the medical instrument based on the reflected light signals and the determination of the positioning and the orientation of the medical instrument relative to the predetermined bend, where the display may be rendered as an overlay on an ultrasound image.

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention discloses a three-dimensional signal secure transmission method and device based on index modulation. The method comprises the following steps: generating four-dimensional chaotic sequences X, Y, Z and U from a key initial value through a chaotic model; performing XOR operation on the original data by using an encrypted sequence generated by X and U in the four-dimensional chaotic sequence to obtain encrypted data; performing binary conversion on the key initial value to obtain a binary key initial value consisting of N four bits; dividing the encrypted data into N groups, each group having 15 subcarriers, determining a to-be-inserted silent position of each group of subcarriers according to a binary key initial value, performing three-dimensional mapping after the silent subcarriers are inserted, and setting the silent subcarriers at an original point position to obtain a three-dimensional constellation diagram; performing constellation masking on the three-dimensional constellation diagram by using Y and Z in the four-dimensional chaos sequence; carrying out IFFT (Inverse Fast Fourier Transform) on the three-dimensional constellation diagram after constellation masking, adding a suffix, and carrying out parallel-serial conversion to obtain transmission data; and transmitting the transmission data to a receiving end through an optical fiber channel.

The invention relates to the technical field of optical fiber communication monitoring, in particular to a passive optical fiber multi-parameter digital twin drive abnormal root cause positioning method and system. Collecting temperature, stress, acoustics and polarization signals, and constructing a time domain, frequency domain and energy domain coupling feature tensor and time sequence data set; performing nonlinear dimension reduction and feature decoupling by using a beta-VAE model, and dynamically quantifying contribution of each parameter to anomaly by using an integral gradient to form a contribution degree vector group; constructing a PINN digital twinborn model embedded with heat conduction and elasto-optical effects, and predicting a normal fluctuation range under contribution vector weighting and data-physics dual constraints; modeling measurement and prediction deviations under the guidance of contribution vectors, and outputting an abnormal measurement score, confidence, a position and a time sequence; and a causal graph neural network is constructed, topology and abnormal events are fused for tracing causes, a fault source is identified, and the model is subjected to closed-loop calibration. According to the invention, data driving and a physical mechanism are fused, and accurate detection and root cause positioning of the abnormity of the optical fiber system are realized.

Disclosed herein is a system, apparatus and method directed to placing a medical instrument in a vasculature of a patient body, where the medical instrument includes an optical fiber having one or more core fibers. The system also includes a console having non-transitory computer-readable medium storing logic that, when executed, causes operations of providing an incident light signal to the optical fiber, receiving a reflected light signal of the incident light, processing the reflected light signals associated with the optical fiber and determining a location of a distal tip of the medical instrument within the patient body. The medical instrument may be steerable in one of various methods including having a predetermined curvature, a distal tip that is magnetic, magnetized, metallic or ferrous and is steerable by an external magnetic device or a having a variable stiffness at a distal tip.

The invention relates to the technical field of distributed optical fiber sonic sensors, in particular to a monitoring and early warning method and system for a distributed optical fiber sonic sensor, and the method comprises the steps: obtaining historical sound wave data and early warning data in a preset time period from a database; forming a monitoring and early warning sample database; broadband chirp continuous light is used as detection light, single-frequency light is used as local oscillation light, a Rayleigh scattering light field of any frequency at any moment is reconstructed through coherent detection, and the response bandwidth of the distributed optical fiber sonic sensor is improved; constructing a monitoring model, using the trained monitoring model to analyze the sound wave signals collected by the distributed optical fiber sound wave sensor, and identifying normal sound wave signals and abnormal sound wave signals; a delay summation beam forming algorithm is introduced, and the accuracy of weak high-frequency sound wave signal detection of the distributed optical fiber sound wave sensor is improved; sound wave signals collected by the distributed optical fiber sound wave sensor are analyzed and processed through the early warning model, and a vibration event is recognized.

The invention discloses a PLC-based optical fiber winding tension control method and system, and the method comprises the steps: obtaining the remaining length of an optical fiber according to a set and real-time winding length through a length decreasing model, switching a winding unit to an ending preparation state and triggering a generator to decelerate when the remaining length is smaller than a threshold value, and obtaining the position information of a winding mechanism through a photoelectric sensor after the ending speed is reached; a motor speed correction coefficient is obtained through a position loop speed correction algorithm according to the angle difference and the target angle difference, and a control instruction is generated. And if the angle reaches or exceeds the target angle, starting the neural network prediction model to obtain a shutdown advance, and outputting a shutdown signal by the PLC according to the shutdown advance and a preset path model to stop the motor with low vibration. The PLC is utilized to integrate various sensing and intelligent algorithms, high-precision tension control in the full-winding process is achieved, the problems that in the traditional technology, tension control is not stable, and problems are prone to occurring at the end are effectively solved, the optical fiber winding quality and efficiency are improved, and the method adapts to various types of optical fibers and technological requirements.

The invention provides a time division duplex multi-carrier synchronous transmission method based on 5G, which is used for solving the problems of inflexible carrier time slot allocation and inaccurate time delay compensation in synchronous transmission. By dynamically adjusting time slot allocation and working frequency bands of carriers in a network control center, the method optimizes network loads and reduces cross interference; the time delay of each carrier is compensated in real time by adopting a synchronous signal calibration algorithm, so that strict alignment of transmission time slots is ensured; the transmitting end dynamically adjusts the transmitting power according to link state information, and the receiving end reduces interference and signal attenuation through a multi-carrier cooperative interference management and multi-path delay compensation mechanism; and a receiving end performs joint decoding based on channel estimation and integrates demodulation signals of all carriers, so that the transmission efficiency and the signal quality of the 5G network are improved. The method is suitable for a base station system and a base station and repeater extension coverage system, and is especially suitable for a distributed wireless system including an optical fiber remote distributed system, a cable remote frequency shift repeater system and the like.

The invention relates to the field of physiological status real-time monitoring, provides a virtual patient physiological status real-time monitoring method and system based on digital twinning, and is used for solving the problems of non-linear combination deviation and multi-source data synchronization lag of virtual patient physiological status monitoring caused by dependence on a static model and one-way feedback. The method comprises the steps that a valve three-dimensional model is built based on aorta CT data, and a simulation model containing motion conditions is built in combination with a fluid mechanics equation; the blood flow pressure difference and vortex parameters of valve opening and closing are calculated in real time, an optical fiber virtual sensor is embedded in the topological surface of the valve to simulate blood flow deformation, and a valve state mechanical signal is output; parameters and signals are input into a digital twin engine, particle trajectories and refractive indexes are adjusted to generate a visual interface, blood flow velocity-pressure gradient spatio-temporal data are extracted, model boundary conditions are reversely corrected, and valve movement is synchronized with the physiological state of a patient. According to the technical scheme, heart valve movement is simulated in real time, and the real heartbeat rhythm of the patient is automatically matched.

The invention discloses a stress analysis method for bridge incremental launching construction, and particularly relates to the field of stress analysis. According to the method, a mechanical state matrix is generated by combining wavelet noise reduction and Kalman filtering through distributed optical fibers, a piezoelectric force sensor, a vibrating wire stress meter and other equipment and real-time data; a pushing system-temporary support-beam body coupling dynamic mechanical model is established, and a generalized alpha method is adopted for solving and Bayesian optimization calibration parameters are adopted; key parameters are identified on line through a three-level progressive framework, boundary conditions are dynamically updated in combination with a total station and a laser tracker, and supporting rigidity self-adaptive adjustment is achieved through fuzzy PID control. Model confidence evaluation adopts a residual norm-modal confidence criterion-Bayesian update three-level mechanism, finally, stress distribution, support reaction and displacement deviation are calculated based on a correction model, and a safety index, threshold alarm and risk analysis report is generated through a fuzzy comprehensive evaluation method and fed back to a construction control center in real time.

The invention relates to the field of power supply equipment monitoring, in particular to a distributed power supply state monitoring method and system based on optical fiber communication sensing. The method comprises the following steps: acquiring a power supply equipment distribution diagram; performing spatial layout distribution analysis on the power supply equipment distribution diagram to obtain power supply node layout distribution data; performing distributed topology reconstruction based on the power supply node layout distribution data to construct a power supply node topology network; acquiring real-time monitoring data of the power supply equipment based on the optical fiber communication sensing node; performing time window fine-grained division on the real-time monitoring data of the power supply equipment to obtain a plurality of time windows; performing multi-layer time-frequency local decomposition on the real-time monitoring data of the power supply equipment based on a plurality of time windows, and constructing a multi-period spectrum feature map; and extracting a power supply equipment time sequence voltage sequence based on the power supply equipment real-time monitoring data. According to the invention, efficient and accurate power supply equipment state monitoring is realized.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention relates to a high-single-mode ultra-low-loss hollow-core anti-resonance optical fiber which comprises an outer cladding and an inner cladding, the inner cladding is composed of nested structure units, the nested structure units are arranged in the circumferential direction of the inner wall of the outer cladding and are connected with the inner wall of the outer cladding, a fiber core is formed by a central cavity wrapped by the inner cladding, and the fiber core is connected with the inner cladding through the nested structure units. The embedded glass tube is characterized in that each embedded structure unit comprises three or more layers of embedded glass tubes with different radiuses, at least one embedded glass tube with at least one layer of embedded glass tubes deflects towards one side relative to the adjacent outer embedded glass tube, and the deflection included angle is 5-55 degrees. According to the invention, through the change of the cladding structure, the reasonable nested structure unit shape capable of being prepared, the optimized nested size combination and the deflection of the embedded glass tube, the leakage loss of the LP11 mode is obviously improved, the influence on the fundamental mode performance is small, and finally the single-mode performance of the optical fiber is effectively improved.