9948 results about "Optical fiber" patented technology

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.

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 discloses a distributed computing task non-perception migration method and system under optical fiber network interruption. The distributed computing task non-perception migration method comprises the following steps: constructing a distributed task migration system and an output system architecture; collecting network state indexes, constructing a network topological graph, and analyzing the state of an optical fiber link for fault prediction; obtaining a calculation task operation state, establishing a sub-task mapping relation, constructing a directed dependency graph, and generating a state snapshot; analyzing resource requirements, reserving standby resources, and deploying a distributed cache system; analyzing a fault influence range, extracting an influenced calculation sub-graph, and selecting a migration target node; in a target node preloading environment, reconstructing an execution context, and redirecting a communication path; and setting a data change capture mechanism, synchronizing incremental data and executing consistency verification. According to the method, non-perception migration of the computing tasks is realized, task continuity and data consistency are guaranteed, and the reliability of the distributed computing system in an optical fiber network fault scene is improved.

Embodiments of the present invention provide a therapeutic device. The therapeutic device includes an inner layer including a plurality of sensing locations and a plurality of stimulating locations constructed therewithin. Furthermore, the therapeutic device includes a plurality of stimulation elements located within the plurality of stimulating locations. The therapeutic device further includes a plurality of optical fibers including a plurality of respective first optical endpoints and a plurality of respective second optical endpoints, the plurality of first optical endpoints located within the plurality of sensing locations. Furthermore, the therapeutic device includes an optical sensor configured to receive electromagnetic radiation from the plurality of second optical endpoints. The therapeutic device also includes a control module configured to determine radiation characteristics of the electromagnetic radiation received by the optical sensor and activate one or more of the plurality of stimulation elements in correlation with the determined radiation characteristics.

The invention discloses a cable optical fiber temperature measurement monitoring system based on big data AI fusion, particularly relates to the technical field of cable optical fiber temperature measurement monitoring, and aims to realize thermal-electric dynamic coupling monitoring through high-density distributed optical fiber temperature measurement and big data AI fusion modeling and introduction of current load synchronous acquisition, and realize high-density distributed optical fiber temperature measurement and big data AI fusion by means of time synchronization and space relocation processing. The method comprises the following steps: identifying a weak response signal of a thermal inertia hysteresis characteristic and an initial temperature rise stage, constructing a cross-scale time sequence recurrent neural network through multi-dimensional enhancement and vector fusion, outputting a continuous scoring sequence reflecting abnormal credibility, and constructing a risk factor group reflecting a fault evolution path in combination with a scoring fluctuation amplitude, a continuous period and a response delay characteristic, and carrying out positioning offset correction through correlation mapping of risk factors and along-cable temperature distribution and by utilizing a topological relation between optical fiber path coordinates and a cable space, and realizing space reverse projection of an abnormal peak value, thereby recovering an accurate position of a fault heat source and a latent hidden danger section.

The invention relates to the technical field of distributed optical fiber acoustic sensors, in particular to a distributed optical fiber sensor vibration identification method and system, and the method comprises the steps: obtaining original data, and carrying out the preprocessing; inputting the preprocessed vibration time sequence into a Mamba model, and extracting a time domain feature vector reflecting a time evolution rule; after obtaining a time-frequency spectrogram of the preprocessed vibration time sequence, extracting to obtain a frequency domain feature vector representing different frequency components and a global association relationship thereof; inputting the time domain and frequency domain feature vectors into a time-frequency feature fusion model, and outputting a vibration event recognition result of the distributed optical fiber acoustic sensor; training the time-frequency feature fusion model, and storing the optimal model weight for prediction; and performing prediction by using the optimal model, storing a prediction result, performing verification, calculating an evaluation index, and finally performing result comparison. The invention aims to solve the problems of insufficient performance and the like of a distributed optical fiber acoustic sensing technology in the prior art.

The invention discloses an intelligent monitoring system for an assembled net rack shear structure, and relates to the technical field of net rack structure health monitoring. A fiber grating sensor, an ultrasonic array, an electromagnetic coil and an energy assembly are integrated through a sensor integration module and a modular support; optical fiber strain, ultrasonic signals and electromagnetic impedance data are collected in real time; after the feature fusion module extracts key features through preprocessing, a fusion feature vector is obtained through an adaptive manifold projection fusion method; the shear force value prediction module takes the fusion feature vector as input, and obtains a shear force value prediction model through training by using an LSTM (Long Short Term Memory) model; and the net rack judgment module is combined with a DBSCAN algorithm to divide neighbor sensor network groups, and is used for judging the structure defect type based on a local anomaly concentration ratio LAC and an overall cooperative change rate GCR. According to the system, intelligent monitoring of the fabricated net rack shear structure is realized, and the monitoring precision and the defect identification accuracy are improved.

The invention belongs to the technical field of PBT plastic particle injection molding, and particularly relates to a precise injection molding process control system for PBT plastic particles. The control system is composed of the following modules: a material characteristic sensing and modeling module, a multi-physical field simulation and parameter generation module, a distributed optical fiber global sensing module, a predictive closed-loop control module, and a digital twinning and full-period optimization module. The material characteristic sensing and modeling module is used for acquiring microscopic parameters of PBT raw materials through a near infrared spectrum sensor and constructing a material characteristic dynamic model; raw material characteristic fluctuation is pre-judged through near infrared spectrum dynamic modeling, technological parameter drift is predicted in advance in combination with an LSTM neural network, and feed-forward compensation is started before deviation is formed; compared with post correction in the prior art, the mechanism has the advantages that the parameter fluctuation control precision is improved, and the prevention efficiency for the problems of hydrolytic degradation, internal stress concentration and the like which are prone to occurring in PBT is improved.

The invention discloses a GIS online monitoring method based on multi-state quantity integration, and relates to the field of GIS online monitoring, and the method comprises the steps: deploying an array composed of an ultrahigh frequency sensor, an ultrasonic sensor, a gas density sensor, an optical fiber temperature sensor and a vibration sensor based on a GIS equipment cavity structure zone; the method comprises the following steps: synchronously acquiring an ultrahigh-frequency electromagnetic wave signal, an ultrasonic signal, a gas density signal, a local temperature signal and a shell vibration signal in an operation process of GIS equipment so as to obtain a multi-dimensional original state quantity sequence; according to the method, multiple types of sensors are accurately distributed and controlled for the GIS cavity structure partition, and through time alignment, adaptive weight fusion and time-space convolution feature extraction, hidden dangers such as a partial discharge source can be accurately positioned, and a state quantity can be predicted by means of a correlation evolution equation. Meanwhile, mechanisms such as sensor fault automatic elimination and complementation, prediction accuracy verification and the like guarantee data reliability, equipment states can be mastered in real time, and faults can be warned in advance.

The invention discloses a laser channel atmospheric turbulence fading real-time compensation device and method based on a galvanometer system, and the method comprises the steps: enabling an incident light beam to irradiate a transparent PCB integrated with a photodiode array after the incident light beam is focused through a convex lens, obtaining the light intensity distribution of a current light spot through the photodiode array, and transmitting the light intensity distribution to a signal processing unit; the signal processing unit coarsely adjusts the deviation direction of the light spots according to the received light intensity distribution data, and aligns the part with the strongest light intensity in the light spots to the end face of the optical fiber; and based on the roughly adjusted light spot deviation direction, the signal processing unit dynamically adjusts the deflection angle of the galvanometer by using a gradient descent method according to the power measurement value of the receiving coupling module, so that the fine adjustment of the light spot deviation direction is realized, and the receiving coupling power is maximized. The objective of receiving optical power maximization is achieved, optical design, electronic control and algorithm optimization are combined, and atmospheric turbulence compensation with high cost performance is achieved.

The invention provides a mountain power station current collection system state online monitoring method and device, and relates to the field of data processing. According to the method, distributed optical fiber data, environmental meteorological data and electrical measurement data are acquired to construct a multi-mode time sequence matrix, an operation state diagram is formed through graph structure representation and graph signal processing, a working condition mode cluster is obtained by combining dynamic clustering, a constraint diagram optimization framework is input, thermal, electrical and mechanical constraints are introduced, and an operation state track is generated. And constructing and fusing a non-stationary degradation path library and environment exposure history to form a dynamic degradation evolution model, and finally outputting in-service reliability and predicting residual life by a risk inference engine to realize online monitoring of the state of the current collection system of the mountain power station. By implementing the technical scheme provided by the invention, the accuracy of online monitoring of the state of the current collection system of the mountain power station is improved.

The invention relates to the technical field of hydraulic test of nickel alloy pipes, and discloses a hydraulic test system for a thin-wall nickel alloy pipe. According to the system, in the axial loading state of the thin-wall nickel alloy pipe, a closed water pressure cavity is established through an annular sealing clamp, and then strain distribution data on the surface of the pipe body is collected through a distributed optical fiber sensor array. And constructing a three-dimensional stress field reconstruction model based on the acquired strain distribution data, calculating an equivalent plastic deformation threshold value of the pipe body under different water pressure gradients, adjusting a water pressure loading curve slope parameter according to the equivalent plastic deformation threshold value, and generating a dynamic water pressure loading instruction. And the high-frequency pressure pulse generator executes the instruction, synchronously triggers the X-ray diffractometer to collect lattice distortion data of the tube body, inputs the data into the material microscopic damage evolution model, and outputs a microscopic defect spatial distribution map. And finally, material constitutive parameters of the three-dimensional stress field reconstruction model are corrected based on the atlas, closed-loop feedback control is formed, and multi-dimensional data acquisition and accurate management and control of the thin-wall nickel alloy pipe hydrostatic test are achieved.

The invention discloses a windable optical cable belt and a manufacturing method thereof, and the manufacturing method comprises the steps: distributing a plurality of parallel optical fibers in the same plane, so as to form an optical cable belt; a dispensing valve is adopted to carry out dispensing and bonding on the two adjacent optical fibers, intermittent dispensing is carried out in the first direction at a first preset interval so as to form bonding parts, and the two adjacent bonding parts of the optical cable belt in the second direction are distributed at a second preset interval in the first direction; and curing the bonding part of the optical cable tape, so that the upper surface of the cured bonding part is sunken downwards, and the lower surface of the cured bonding part is sunken upwards. According to the optical fiber manufacturing method, the adjacent optical fibers are bonded in a manner of carrying out interval dispensing at the first preset interval in the first direction, and the adjacent bonding parts in the second direction have the second preset interval in the first direction, so that compared with an existing optical fiber dipping method, the method has the advantages that the crimpability of an optical cable belt can be effectively improved; and the glue consumption can be reduced.

The invention relates to an optical cable monitoring and data analysis method and device, equipment and a medium. The method comprises the following steps: acquiring current state data of an optical fiber link, and dynamically generating adaptive pulse coding parameters and matched filter parameters based on the data by using a deep reinforcement learning agent, so as to generate and inject coded pulses into the optical fiber link; and then receiving an echo signal, performing matched filtering processing on the signal by using the generated matched filter parameter to obtain a filtering output signal, further calculating a performance index vector and a reward signal, and finally updating a strategy network of the intelligent agent by using the reward signal, thereby realizing continuous optimization of pulse coding and matched filtering parameters. Therefore, the system can automatically adapt to the dynamic change of an optical fiber link, the spatial resolution, the dynamic range and the signal-to-noise ratio are effectively improved, and the adaptive capability and the robustness of the system are enhanced at the same time.

The invention relates to the technical field of optical cable line external force damage monitoring, and discloses an optical cable external force damage intelligent identification system. The system comprises an optical fiber vibration sensing module, a multi-source feature fusion analysis module, an abnormal event association positioning module and an intelligent checking strategy generation module. The optical fiber vibration sensing module constructs a model based on historical data and outputs a reference vibration characteristic value; the multi-source feature fusion analysis module performs three-dimensional fusion analysis of time domain, frequency domain and phase on the reference value and the measured value to generate a feature difference matrix; the abnormal event association positioning module is used for generating an abnormal probability distribution cloud picture and positioning an abnormal area in combination with the topological parameters and the environment information; and the intelligent checking strategy generation module configures checking parameters according to the cloud picture, starts high-frequency acquisition for a high-probability region, and applies an environment disturbance test to adjacent lines. The system can accurately identify and position optical cable external force damage, and the monitoring efficiency is improved.

The invention relates to the technical field of computer hardware and network communication, in particular to a high-speed I / O (input / output) virtualization server based on an FPGA (field programmable gate array) and a remote forwarding method.The high-speed I / O virtualization server comprises an FPGA chip, a network processing unit, an I / O protocol processing unit, a protocol conversion and scheduling unit, a time sequence and transaction management unit and a data buffering and flow control unit are integrated in the FPGA chip; the optical fiber Ethernet interface module comprises a physical layer interface and an MAC layer processing and network configuration interface; the local I / O interface module comprises a USB interface circuit, a serial port interface circuit, a PCIe interface circuit and an interface expansion connector; the power supply and monitoring module comprises a multi-path power supply management system, a state monitoring and diagnosis system and an LED indication system; according to the self-adaptive Baud rate detection circuit, the Baud rate can be accurately locked under the condition that data with less than two characters is received, the number of data bits, the type of a check bit and the length of a stop bit are automatically identified, and compared with a traditional microcontroller scheme, the self-adaptive Baud rate detection circuit has higher precision and higher detection speed.

The invention relates to the technical field of safety monitoring, and provides an oil and gas pipeline leakage monitoring method and system, a medium and equipment, and the method comprises the steps: obtaining a leakage vibration signal of each measurement point of an oil and gas pipeline, and extracting a time domain feature, a frequency domain feature, a spatial-temporal feature and an initial distance from a leakage point to a starting end of an optical fiber; performing cross-correlation analysis on the leakage vibration signals of the adjacent measuring points to obtain a propagation time difference and a cross-correlation coefficient, and calculating a correction distance from the leakage point to the starting end of the optical fiber; if the leakage point is in the spiral section, calibrating the correction distance to obtain a calibration distance; calculating the difference value between the initial distance from the leakage point to the optical fiber starting end and the corrected distance to obtain a preliminary positioning error value; and based on the time domain feature, the frequency domain feature and the spatial-temporal feature, in combination with a cross correlation coefficient, a calibration distance, an amplitude enhancement coefficient and a preliminary positioning error value, obtaining a leakage medium type through a medium identification model. The method gives consideration to the positioning efficiency and precision, and greatly improves the accuracy and robustness of a leakage medium type recognition result.

The invention discloses an optical fiber three-dimensional shape sensor based on an optical frequency domain reflection technology and an application method of the optical fiber three-dimensional shape sensor, the optical fiber three-dimensional shape sensor comprises a base material and more than three optical fibers, and all the optical fibers are spirally wound and fixed on the surface of the base material at equal intervals and constant screw pitches to form distributed sensing units; according to the application method, measurement signals of all the optical fibers are obtained, and axial strain and strain are calculated; calculating the total curvature vector of the reconstruction curve of the measured object; calculating a bending direction angle and a curvature of the reconstruction curve of the measured object; performing differential calculation on the bending direction angle to obtain the torsion rate of the reconstructed curve of the measured object; and reconstructing the three-dimensional shape of the measured object according to the curvature and the torsion of the reconstructed curve of the measured object. The invention aims to realize synchronous measurement of bending, torsion and axial strain by using a distributed sensing unit formed by a multi-fiber structure which is spirally wound at the same interval and at a constant screw pitch.

The invention discloses a die temperature field coordinated regulation and control method based on digital twinning, and relates to the technical field of die temperature control. The method comprises the following steps: constructing a mold temperature field digital twin model covering macroscopic, mesoscopic and microscopic scales; a distributed optical fiber temperature sensor is deployed to realize continuous temperature field sensing; physical-digital real-time synchronous mapping is established through ensemble Kalman filtering; performing model prediction control based on the digital twinborn body; three-dimensional visualization and multi-modal man-machine interaction of the temperature field are realized by adopting an augmented reality technology; and reinforcement learning is used to enable the digital twinborn body to have an autonomous optimization capability. Through deep fusion of a physical space and a digital space, precise sensing, real-time mapping and intelligent control of a mold temperature field are realized, and the temperature control precision and the self-adaptive capability of the system are remarkably improved.

The invention relates to the technical field of coal mine telecommunication, and discloses a coal mine underground integrated private network coverage system based on a 5G communication and AR double-transmitting selective-receiving technology, an efficient transmission and baseband processing network is constructed by taking a macro base station as a core on the ground and matching with PTN equipment and a BBU, a 5G micro base station and an RRU are deployed under the ground for a complex environment, and low-loss signal coverage is realized through optical fibers. The AR double-transmitting selective-receiving module ensures that underground data are not lost and have no error through redundant coding and double-link transmission; the edge computing module sinks data processing to the underground, reduces transmission delay and optimizes resource allocation; and the system monitoring and interference suppression unit monitors the operation state in real time, performs multi-stage alarm and resists multipath effect and electromagnetic interference, and finally realizes integration of communication above and under the well, reliability of data transmission, high efficiency of resource utilization and stability of system operation, thereby providing solid communication support for safety production, equipment management and control and personnel scheduling under the coal mine.

The invention discloses an optical terminal intensity instrument and an earthquake monitoring service system and method, and the optical terminal intensity instrument comprises an optical fiber modem and an MEMS sensor module. The optical fiber modem comprises an MCU (Microprogrammed Control Unit) and an optical communication module electrically connected with the MCU; the optical communication module is used for realizing network access and data uploading; the MEMS sensor module is electrically connected with the MCU; the MEMS sensor module is used for collecting seismic intensity data in real time; and the optical fiber modem is used for providing a dual data transmission channel comprising broadband communication and the seismic intensity data. According to the technical scheme provided by the invention, the MEMS sensor is embedded in the optical fiber modem, and a more dense, more efficient and more economical solution is provided for earthquake early warning and intensity rapid reporting.

The invention discloses a multi-channel high-precision water level dynamic real-time monitoring system for a drilling water pumping test, and the system comprises a sensing layer which is used for deploying a multi-source sensor array, inhibiting interference through distributed layout and electromagnetic shielding, collecting multi-dimensional data in real time, and carrying out the preliminary filtering; according to the transmission layer, an optical fiber and twisted pair redundant network is constructed, an EtherCAT protocol is adopted for low-delay transmission, AES-128 encryption tampering prevention is integrated, and edge nodes compress data and verify integrity; the processing layer is used for carrying out real-time preprocessing on edge calculation and switching a standby sensor in abnormal detection; the cloud end predicts the water level trend through Spark Streaming analysis and an LSTM model, distinguishes interference through a random forest algorithm, and outputs calibration data and an analysis report; the application layer is used for constructing a three-dimensional drilling model based on Unity, dynamically displaying water level changes, synchronizing a multi-parameter curve and supporting historical backtracking; the early warning module notifies abnormity through sound and light and short messages, the decision module generates a report and supports multi-drill-hole comparison, and the test strategy is optimized.

The invention relates to an optical fiber longitudinal power anomaly detection method and device, equipment and a storage medium. The method comprises the following steps: acquiring input optical power data of an input end of an optical fiber and actual output optical power data of an output end of the optical fiber; inputting the input optical power data into an optical power evolution physical model, and obtaining predicted output optical power data through optical power attenuation parameters of different positions of an optical fiber in the optical power evolution physical model; based on the difference between the actual output optical power data and the predicted output optical power data, using an artificial intelligence optimizer to optimize each optical power attenuation parameter until the difference satisfies a first preset condition; and obtaining a longitudinal power anomaly detection result of the optical fiber according to each optimized optical power attenuation parameter. By adopting the method, the efficiency and precision of optical fiber longitudinal power anomaly detection can be improved, and the detection cost is saved.

The embodiment of the invention provides an optical fiber pickup mode recognition method and device, electronic equipment and a storage medium, and relates to the field of signal processing. The method comprises the following steps: acquiring an original time domain vibration signal, and filtering the original time domain vibration signal to obtain an effective signal; inputting the effective signal into a one-dimensional convolutional neural network to obtain a local time domain feature; performing frequency domain feature extraction on the effective signal to obtain a physical frequency domain feature; performing dynamic weighted fusion on the local time domain features and the physical frequency domain features by fusing a multi-head attention mechanism to obtain fusion features; and inputting the fusion features into a preset classification network to obtain an event classification result. The method is used for achieving the effect of improving the classification accuracy and robustness of the optical fiber pickup event.

The invention belongs to the technical field of geological engineering monitoring and multi-physics field intelligent sensing, and discloses a separation grouting slurry migration monitoring method and system, and the method comprises the steps: constructing an acoustics-optics-electricity-electromagnetism multi-physics field coupling monitoring system; a grouting hole and a monitoring drilling hole array are arranged in a target stratum, a distributed optical fiber sensor, a drilling hole ultrasonic imager, a resistivity measuring device and a transient electromagnetic detection device are installed, and a three-dimensional monitoring network is formed; synchronously acquiring sound field, light field, electric field and magnetic field data; four-field monitoring data and overlying strata multi-parameter observation data are fused, a coal seam mining three-dimensional overlying strata separation model is constructed, and a slurry diffusion three-dimensional visualization model is generated through numerical simulation; and based on the four-field monitoring response difference of the separation space before and after grouting, in combination with a monitoring frequency domain correction model of multiple indexes of grout diffusion, the diffusion range, compactness and filling effect of the grouting grout are evaluated, and abnormal early warning is triggered.

The invention discloses an optical fiber and wireless fused power communication network fault intelligent switching and self-healing method, which comprises the following steps: constructing a multi-mode link health portrait, arranging four-in-one sensors at all nodes, and generating a health vector through edge calculation; mining a double-domain fault propagation chain, constructing a dynamic heterogeneous graph, and predicting fault propagation through memory attenuation GNN; pre-generating a hybrid redundant path, and selecting a main and standby path pair based on a game algorithm; establishing a programmable self-healing decision engine, and outputting an adaptive instruction; and zero-interruption service migration and closed-loop verification are realized. The method solves the problems of lack of physical constraints, insufficient dynamic adaptation and low efficiency of edge deployment in the prior art, and improves the reliability of the power communication network.

The invention belongs to the technical field of stator slot wedge detection, and discloses a generator stator slot wedge online detection method and system based on acousto-optic monitoring fusion. The method comprises the following steps: acquiring laser ranging data, acoustic monitoring data and generator working condition parameter data of online looseness detection of a generator stator slot wedge; preprocessing the laser ranging data, the acoustic monitoring data and the generator working condition parameter data to obtain laser ranging features, acoustic features and working condition parameters; and inputting the optical ranging features, the acoustic features and the working condition parameters into a pre-trained auto-encoder for anomaly detection, and obtaining an online looseness detection result of the generator stator slot wedge. By fusing the optical ranging technology, the optical fiber acoustic monitoring technology and the unsupervised deep learning algorithm, the important technical breakthrough in the field of generator stator slot wedge looseness detection is realized, and online stator slot wedge looseness detection can be carried out.

The invention belongs to the technical field of civil engineering monitoring, and discloses an optical fiber strain sensor performance verification method and system for highway infrastructure monitoring, and the method comprises the steps: analyzing the influence rule of the parameter change of a coupling layer on the strain transmission efficiency of a measured sensor based on a layered material coupling mechanical model; based on the three-dimensional entity numerical model, analyzing an influence rule of layout process parameters of the tested sensor on the strain transmissibility; verifying the performance of the sensor in a laboratory environment by using a reduced scale model; on a real highway infrastructure or a constructed full-scale model, the performance of the sensor on an actual structure is verified through monitoring data. According to the invention, by constructing an integrity performance verification system from a theoretical model to numerical simulation and then to a reduced scale model test and entity structure verification, multi-dimensional and progressive performance verification of the performance of the optical fiber strain sensor for highway infrastructure monitoring is realized.

The invention discloses a rapid communication and redundancy control method for an AGC substation of a distributed photovoltaic power station, and the method comprises the following steps: S1, receiving an automatic power generation control instruction from a power grid dispatching system in parallel and uploading real-time operation data of the photovoltaic power station through two independent communication links; according to the invention, the reliability and robustness of the system are fundamentally improved; by constructing a physical basis of parallel work of two paths of independent communication links and combining real-time state monitoring and a millisecond-level rapid switching mechanism, the method thoroughly changes the fragile situation that the whole substation is disconnected and the function is paralyzed due to a single communication channel or a power supply fault of a traditional system; when the main optical fiber link is interrupted due to construction damage or equipment failure, the system can automatically and seamlessly switch all communication services to the standby Ethernet or wireless link without depending on manual intervention, and it is ensured that the transmission path of the scheduling control instruction is always smooth.

The invention relates to a method for constructing and visualizing a knowledge graph of a secondary virtual loop of an intelligent substation, which solves the problems of incomplete visualization, incomplete relevance, poor readability and the like in the prior art, and adopts the technical scheme that the method comprises the following steps of: acquiring an SCD configuration file and an optical fiber link table of the intelligent substation; reading and analyzing the SCD configuration file, and obtaining virtual loop configuration information of the whole station; matching the virtual loop with the soft pressing plate based on the configuration information of the virtual loop of the whole station; matching the physical loop with the virtual loop based on the total station virtual loop configuration information and the optical fiber link table; constructing a knowledge graph based on the total station virtual loop configuration information; and constructing a total station secondary equipment virtual loop configuration database based on the knowledge graph. The method has the advantages that the configuration information of the virtual loop of the whole station can be completely presented in a graphic form, the visualization degree of the secondary virtual loop of the intelligent station is effectively improved, and the working efficiency of on-site technical improvement, defect elimination, verification and the like is improved.