2884results about "Electromagnetic transmission" patented technology

An apparatus (100) for detecting an Electro-Magnetic, EM, field, said apparatus comprising a: Rydberg-Atom EM Field Detector (107), RAEM-FD, configured to detect an EM field (170) of a given frequency; and Multi-Plate WaveGuide (105), MPWG, comprising: at least three overlapping and non-intersecting plates (180); and at least two waveguides (190), each formed by an adjacent pair of the at least three plates, and wherein each waveguide has a separation distance suitable (h) for supporting propagation of the EM field; and wherein the MPWG is arranged so as to direct the EM field towards the RAEM-FD.

The invention relates to the technical field of communication engineering, in particular to a communication optical cable line intelligent inspection fault point rapid positioning method and device, and the method comprises the steps: obtaining optical cable line state data, constructing an intelligent fault detection model, recognizing an abnormal signal through wavelet transform and a threshold determination method, training an LSTM neural network in combination with historical fault data, and pre-judging a fault type. When a fault is detected, an unmanned aerial vehicle inspection unit is triggered, an inspection path is planned based on an improved Dijkstra algorithm according to the fault type and suspected fault area geographic information, an optical cable line is scanned and detected through a laser radar, physical fault features are recognized through a YOLOv5 algorithm, and geographic coordinates of a fault point are calibrated in combination with an inertial navigation system; and fusing sensor data and a detection result, evaluating a fault confidence level by adopting a DS evidence theory, and generating an alarm work order. Therefore, the problems of long positioning time consumption, high omission ratio, difficulty in distinguishing fault types and the like in the prior art are solved.

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.

The invention provides a hollow-core optical fiber cable fiber breakage detection method based on a reference optical fiber and an optical cable, and belongs to the technical field of optical fiber communication. The method comprises the following steps: acquiring monitoring data of a reference optical fiber in a hollow-core optical fiber cable, and performing quality evaluation on the data to generate a signal quality comprehensive score; when the score is higher than a preset threshold value, feature extraction and dynamic weighted fusion are carried out to generate a comprehensive feature vector; outputting a fiber breakage probability by using the fiber breakage state recognition model, calculating a fiber breakage risk value through a prediction model in combination with time sequence data, and obtaining a fiber breakage risk index through a risk fusion algorithm; and finally, matching and executing a hierarchical response strategy according to the risk index. According to the invention, multi-modal data fusion, intelligent state identification and active early warning are realized, and the reliability, precision and operation and maintenance efficiency of broken fiber detection are significantly improved.

The invention discloses an optical cable topology visualization monitoring analysis system and method, and relates to the technical field of optical cable monitoring. According to the optical cable topology visual monitoring analysis system and method, the complex dependency relationship between optical cable network equipment is accurately captured by using the graph neural network, and the wavelength division multiplexing, OTDR test and optical power measurement technologies are fused, so that online monitoring without interrupting service communication is realized, the real-time performance and efficiency of monitoring are improved, the positioning precision of optical cable broken fibers can be improved, and the optical cable topology visual monitoring analysis system and method are suitable for popularization and application. Through new and old OTDR curve comparison and big data analysis, optical fiber aging and degradation trends are pre-judged in advance, accurate active operation and maintenance are realized, the probability of optical cable fault occurrence is reduced, the reliability and stability of a network are improved, and by cooperating with visual display of a complex power grid optical cable map, the method has the advantages of high accuracy and high reliability. The topological structure and the geographic position of the optical cable are visually displayed by fusing multiple levels, clear visual information is provided for operation and maintenance personnel, and management and decision making are facilitated.

The invention provides a network fault node automatic detection and roundabout path planning method, which comprises the following steps of: acquiring optical power and bit error rate of an optical fiber link, and recording abnormal time point and position information; obtaining a strong electromagnetic interference influence weight map; generating a deterioration trend curve; obtaining a health degree score of each optical fiber segment, and generating a health degree distribution map; probability distribution of fault nodes is determined, whether the nodes are fault points is judged according to the probability distribution, and a fault node position set is generated; obtaining a roundabout path planning scheme; and calculating a global network health state perception score to obtain an optimized global network health state diagram. The invention further discloses a corresponding storage medium. According to the invention, the reliability and stability of the optical fiber network can be effectively improved, and the efficiency and accuracy of fault detection and processing can be improved.

The invention discloses a VGA (Video Graphics Array)-based 5G miniature optical transceiver module self-calibration method, which belongs to the technical field of optical transceiver module self-calibration, and comprises the following steps: S1, establishing a self-calibrated benchmark reference value through an initialization module and collecting environment parameters and benchmark optical parameters; s2, dynamically collecting and receiving optical power and environment change data, and judging whether deviation exceeds a threshold value or not to trigger model retraining by collecting actual operation data of environment parameters and driving parameters and combining a neural network model prediction target value; integration of a new data set and re-fitting of a non-linear relation enable the model to continuously adapt to the influence of long-term factors; key parameters such as a gain coefficient or a temperature compensation curve of a proportional-integral-differential control algorithm are dynamically corrected, long-term precision and reliability of a self-calibration system are guaranteed, and the problem of calibration failure caused by failure of a static model is avoided.

The invention provides a broadband tunable low-phase-noise photo-generated microwave signal source and device, and relates to the technical field of photo-generated microwaves. According to the invention, a low-frequency optical pulse and a high-frequency microwave signal are respectively input into the electro-optical sampling phase-locked loop; and the electro-optical sampling phase-locked loop determines the synchronization error of the optical pulse and the microwave signal based on the phase difference between the optical pulse center point and the microwave signal zero crossing point. And taking the synchronization error as a feedback signal, and continuously adjusting the frequency and the phase of the microwave signal by the voltage-controlled oscillation module until the frequency and the phase are synchronized with the Nth harmonic of the optical pulse. By setting different initial frequencies of the voltage-controlled oscillation module, microwave signals with different integral multiple optical pulse frequencies can be obtained, and broadband tuning of photo-generated microwave signals is realized. Furthermore, the repetition frequency of the frequency comb is locked through beat frequency of two beams of reference laser and two adjacent comb teeth, the stability of the reference laser is transmitted to the optical pulse, and the stability of the reference laser is further transmitted to the microwave signal through synchronization of the optical pulse and the microwave signal, so that the phase noise of the photo-generated microwave signal source is reduced.

The invention discloses an automatic control method and system for a bias point of a photoelectric modulator, and relates to the field of signal processing, and the method comprises the steps: generating a composite disturbance voltage signal through a digital signal processor; the bias electrode is injected into the photoelectric modulator and used for collecting the optical signal output by the photoelectric modulator and converting the optical signal into an electric signal; extracting a fundamental component amplitude and phase difference data of a second harmonic component, a third harmonic component and a fundamental wave; dynamic characteristic parameters are calculated through spectral analysis, and the mapping relation between the characteristic parameters and the offset of the offset points is established; outputting the offset direction and the offset level of the offset point through a deep learning model; a bias voltage compensation amount is generated through a fuzzy PID (Proportion Integration Differentiation) controller; and adjusting the bias electrode voltage, smoothing the filter, and eliminating high-frequency jitter. The method has the advantages that high-precision and stable bias point adjustment is realized through the composite disturbance signal, real-time spectrum analysis and deep learning optimization decision in combination with fuzzy PID control and a dynamic closed-loop mechanism, and the system performance and stability are remarkably improved.

The invention discloses a multi-operator optical fiber network fault diagnosis method and device, equipment and a medium, and relates to the technical field of deep neural networks, and the method comprises the steps: constructing a three-dimensional digital twin model, and carrying out the parameter correction of the three-dimensional digital twin model; acquiring open source library data and operation and maintenance data of a plurality of operators to generate a cross-operator network asset database; respectively deploying the corrected three-dimensional digital twin model to each operator so that the operator can calculate corresponding model training parameters; acquiring all model training parameters, training the graph neural network model based on the model training parameters, and judging whether the trained graph neural network model is converged or not; if convergence occurs, inputting the cross-operator network asset database into the trained graph neural network model to predict an optical fiber network fault point; the fault report and the repair scheme are generated based on the optical fiber network fault points, the accuracy of multi-operator optical fiber network fault diagnosis is improved, and the cross-operator cooperation efficiency and the user experience feeling are improved.

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 provides an intelligent optical fiber distribution equipment health degree analysis method and device, and belongs to the technical field of optical fiber communication. A global LSTM model is constructed, and initialized global model parameters are issued to each intelligent optical fiber distribution equipment for local LSTM model training; and executing a parameter alignment compensation and parameter aggregation strategy on the global LSTM model by using the local training parameters obtained by each intelligent optical fiber wiring device, obtaining updated global model parameters, issuing the updated global model parameters to the local LSTM model of each intelligent optical fiber wiring device, and evaluating the current health state of each intelligent optical fiber wiring device. Through a distributed federated learning framework, local data distributed training and global model aggregation of intelligent optical fiber distribution equipment are realized, data such as optical power loss and plugging times are monitored and analyzed in real time, potential health risks are found in time, equipment damage caused by problem accumulation is avoided, and adaptability and accuracy of equipment health degree monitoring are improved.

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 invention discloses a phi-OTDR optical cable fault diagnosis method and system based on multi-channel topological data analysis, and is applied to the technical field of optical fiber sensing and intelligent operation and maintenance. The method comprises the following steps: acquiring a backward Rayleigh scattering signal of a sensing optical cable to obtain a spatio-temporal data matrix; preprocessing and multi-scale slicing are carried out to obtain spatio-temporal data segments, and initial position information based on the optical time domain reflection principle is added to each spatio-temporal data segment; different types of spatio-temporal data fragments are routed to corresponding topology analysis channels for parallel processing; extracting topological features and positioning information, and fusing the topological features and the positioning information to form optical cable full-state vectors; and outputting a diagnosis result based on the pre-trained intelligent diagnosis model, wherein the diagnosis result comprises a fault type, accurate position information and a health degree evaluation result. According to the method, the positioning robustness in a strong noise environment is effectively improved, and accurate fault classification and early capture and early warning of progressive performance reduction of the optical cable are realized.

The invention relates to the technical field of road slope safety monitoring, in particular to a slope large deformation timely sensing and graded warning method based on a distributed optical fiber technology, and the method comprises the steps: arranging a first-stage sensing optical cable, a second-stage sensing optical cable, a third-stage sensing optical cable and a positioning optical fiber which are designed in a mechanical property gradient manner on a slope platform, and achieving the cooperation with the deformation of a rock-soil body; a double-threshold self-adaptive denoising algorithm is adopted to process optical signals, environmental interference is eliminated, and breakpoints are accurately recognized; three-level alarm is triggered based on the broken optical cable level, the slope sliding acceleration is calculated through a catastrophe evolution prediction model, advanced early warning is achieved, and when specific conditions are met, special-level alarm is triggered, and emergency broadcast is linked; high-precision position determination is achieved through stake mark matching positioning, and stable operation of the system is guaranteed by combining design of a temperature compensation optical fiber, an independent monitoring route and the like. According to the invention, deformation of different degrees of the slope can be accurately sensed, graded warning and intelligent decision making are realized, and the accuracy and reliability of slope disaster prevention and control are effectively improved.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention relates to the technical field of data processing, and provides a data processing method for testing avionics equipment, which comprises the following steps of: testing a communication optical cable of an aircraft communication system through an optical time domain reflectometer; analyzing the deviation between the loss value of the reflection intensity of the optical signal at each moment and the reflection loss threshold value to obtain the fault probability of the optical cable in the monitoring time period; analyzing the difference between the moment when the optical signal reaches the bending position of the optical cable and the moment corresponding to the peak point, and obtaining the reflection unpredictability of the optical signal in the monitoring time period; combining the light source power difference to obtain an optical cable performance error in the monitoring time period; obtaining the extra loss degree of the optical cable in the monitoring time period by combining the change condition of the temperature data; determining a test result correction coefficient of the monitoring time period; and correcting the reflected signal oscillogram, and testing the communication optical cable. The invention aims to solve the problem that the actual performance judgment of the optical cable is influenced by the fine change interference of various factors in the optical signal propagation process of the communication optical cable.

The invention relates to the technical field of hydraulic engineering control, and discloses a local anti-error software system applied to gate pump station remote control, which comprises a remote control center, the remote control center is in wireless communication connection with the local anti-error host through double-channel communication, and the local anti-error host performs double verification on a control instruction sent by the remote control center and a local equipment state through the real-time state double verification unit so as to ensure real-time synchronization of the remote instruction and the local state. The anti-error decision-making unit carries out real-time monitoring and intelligent decision-making on the operation state of the gate pump station through a real-time state monitoring module, a preset water level warning value, an instruction pre-calculation module and a self-locking feedback module so as to cope with dynamic changes such as water level abrupt change, so that the system can adjust the gate opening degree and the operation state of a water pump in time according to the water level abrupt change; the safety and the stability of the gate pump station under complex working conditions are improved, and disasters such as flood caused by the fact that water level changes cannot be handled in time are effectively avoided.

The invention discloses an optical fiber adapter control method and system based on artificial intelligence. The method comprises the following steps: acquiring multi-dimensional data acquired in real time by a multipoint sensor network deployed in an optical communication environment; according to the multi-dimensional data, a filtering algorithm is adopted to remove noise interference, and a cleaned environment state data set is obtained; inputting the environment state data set into a pre-established support vector machine model, and generating an abnormal state identifier and a corresponding environment parameter value if detecting that the temperature data or the vibration data exceed a preset threshold range; predicting a control parameter adjustment scheme of the optical fiber adapter by adopting a deep neural network algorithm according to the abnormal state identifier and the environmental parameter value, and generating a control signal set containing an adjustment instruction; and transmitting the control signal set to an automatic execution module, executing mechanical displacement and optical path calibration operation according to the adjustment instruction, and obtaining adjusted optical signal transmission state data. The reliability, the stability and the operation and maintenance efficiency of the optical communication system are remarkably improved.

The invention relates to an electric power communication optical fiber fault monitoring and visualization method and system, and belongs to the technical field of optical fiber communication monitoring, and the method comprises the following steps: carrying out the real-time monitoring of an electric power communication optical fiber through a non-intrusive monitoring system, collecting an optical power signal in real time, and constructing an optical fiber resource database; when the non-intrusive monitoring system detects that optical power attenuation exceeds a preset threshold value, an optical time domain reflectometer OTDR is started to perform intrusive detection on an abnormal optical fiber, and an OTDR echo curve is obtained; extracting a composite feature vector of an OTDR echo curve, and identifying a fault type through a multi-stage fusion algorithm in combination with historical fault record data; according to the OTDR detection point coordinates, the optical path distance and the geographic information data, the position of a fault point is calculated, and the optical fiber topology, the position of the fault point and the fault type of the fault point are displayed in a visual interface. According to the invention, accurate fault identification and quick response are realized, and the operation and maintenance efficiency is improved.

The invention discloses an optical communication network operation and maintenance fault reporting, positioning and tracking system, which relates to the technical field of optical communication network operation and maintenance, and comprises a coarse positioning module for performing phase demodulation and interference signal matching on optical fiber echo data, identifying a sudden change point and an abnormal interval, and generating a potential fault interval and a coarse positioning coordinate; the fine positioning module is used for transmitting a coded pulse signal to a link where the optical fiber echo data is located according to the potential fault interval and the coarse positioning coordinate, and performing matched filtering and deconvolution processing to generate a fine positioning echo characteristic curve; the fault identification module is used for carrying out deep learning classification on the fine positioning echo characteristic curve, identifying a fault type and generating a diagnosis result; and the visualization module is used for mapping the diagnosis result to the topology and geographic position information of the link section where the optical fiber echo data are located, and generating a visual interface. According to the invention, the manual troubleshooting time is obviously reduced, and the fault processing efficiency and reliability are improved.

The invention relates to the technical field of optical network operation and maintenance, and particularly provides an optical network fault prediction method and system based on MEMS characteristic embedding and digital twinning, and the method comprises the steps: collecting multi-physics field coupling parameters of optical network operation in real time through an MEMS multi-physics field coupling sensing unit which is disposed at an optical network key node and is embedded in a physical layer device; constructing an optical network cross-scale digital twin, inputting parameters to realize real-time synchronization and fault rehearsal of a physical entity and a virtual mirror image, and outputting a rehearsal result; constructing a fault prediction model based on an optical network physical mechanism, inputting parameters and rehearsal results to complete feature fusion and trend analysis, and outputting a fault prediction result; an intervention strategy is generated and subjected to twinborn virtual simulation verification; and after the verification is passed, driving the physical layer to execute intervention, and feeding back effect data to dynamically update twin model parameters. A closed loop of perception, modeling, prediction, intervention and feedback is formed, and the problems of prediction lag and low operation and maintenance efficiency in the prior art are solved.

In some embodiments, a fiber optic cable may be monitored while reducing false positives from disturbances in trusted areas. In some embodiments, a system includes a light source, a first remote termination unit in a trusted area, and a second remote termination unit in another trusted area. The light source emits light through the fiber optic cable, which passes through the first remote termination unit, an untrusted area, and then the second remote termination unit. The remote termination units reflect portions of the light back towards the light source. The system uses a prediction model to detect disturbances in the untrusted area based on the reflected light data.

The invention relates to the technical field of optical communication, and discloses an optical module adaptive test and rapid convergence method based on reinforcement learning, which can dynamically generate and execute an optimal test sequence for optical modules of different types or states through an off-line trained RL intelligent agent module, avoids redundant steps in a fixed process, and improves the test efficiency. On the premise of ensuring the test coverage, the test time is obviously shortened, and meanwhile, the detection probability of potential defects is improved; through the closed-loop test of the integrated RL intelligent agent module, the test action can be adjusted in real time according to the state of a single optical module, so that the test process can quickly adapt to the individual difference and process fluctuation of the device, the online quick convergence of the test strategy is realized, and the accuracy and stability of the test are guaranteed; and through online iterative optimization of the RL agent module, new data and new changes in the production process can be continuously learned, so that a test strategy is continuously evolved, and the optimal performance is kept for a long time.

The invention discloses a phase generated carrier demodulation method based on an end-to-end neural network, belongs to the technical field of optical fiber interference sensing signal processing, and is used for a phase demodulation process of an optical fiber interference signal. According to the method, a data set with a physical label is generated through simulation, a deep neural network is trained to realize direct regression prediction of the phase, and dependence on multi-stage parameters and a complex demodulation process in a traditional phase generation carrier demodulation method is avoided. By constructing a regression network model comprising an input layer, a hidden layer and an output layer, high-precision effective demodulation can be performed on interference signals under different parameter conditions. The method has strong robustness, is suitable for various optical fiber sensing application scenes, and especially has good practical value in a dynamic environment.

The invention relates to an automatic test method of an FTTR optical link, which comprises the following steps: acquiring specified parameters of the FTTR optical link to be detected, establishing communication connection between a test control center and FTTR equipment, dynamically generating an optical link test instruction and sending the optical link test instruction to the FTTR equipment, and testing the optical link according to the received optical link test instruction. The test control center performs comparative analysis on the received test data and preset standard data, inputs the test data into a machine learning model for training and updating, judges whether the optical link has a fault based on a comparative analysis result and a prediction result, and marks a fault type and a fault position by using a fault positioning algorithm if the optical link has the fault; and generating a test report based on the comparative analysis result, the fault judgment result, the fault type and the fault position, and automatically generating a fault solution according to the fault type. According to the method, automatic and intelligent testing of the FTTR optical link is realized, and the fault diagnosis efficiency and accuracy are improved.

The invention relates to the technical field of optical cable monitoring, and discloses an intelligent optical cable monitoring system and method. The method comprises the following steps: firstly, acquiring an optical fiber transmission loss value and a temperature fluctuation value of an optical cable laying environment at each monitoring moment; dividing the monitoring parameters into stable parameters and fluctuation parameters according to the transmission loss values at the plurality of monitoring moments; for the stable parameters, a reference fluctuation interval of the stable parameters is obtained according to the transmission loss values at all moments, and for the fluctuation parameters, the reference fluctuation interval of the fluctuation parameters is obtained by combining the temperature fluctuation values and the transmission loss values at all moments; and then judging whether the transmission loss value of each monitoring parameter at the current moment accords with the corresponding reference fluctuation interval, if so, taking the current transmission loss value and the temperature fluctuation value as an optical cable state monitoring data source, and if not, selecting corrected data in the reference fluctuation interval as the optical cable state monitoring data source. The method can judge the transmission loss of the optical cable more practically, and guarantees the effectiveness of a monitoring data source.

The invention provides a communication method and device. The method comprises: a transmitting end device transmits a first optical signal, the first optical signal carrying a tracking and pointing signal, the tracking and pointing signal being used for calibrating an optical transmission channel between the transmitting end device and a receiving end device, the tracking and pointing signal comprising a low-frequency component and a high-frequency component, the low-frequency component being used for the receiving end device to determine a spot position of the first optical signal, and the high-frequency component being used for the receiving end device to determine a spot position of the first optical signal; the high-frequency component is used for the receiving end equipment to determine the power of the first optical signal; the sending end equipment receives first feedback information from the receiving end equipment, wherein the first feedback information is used for indicating to end the calibration of the optical transmission channel; and the sending end equipment responds to the first feedback information, stops sending the first optical signal and sends a second optical signal to the receiving end equipment, and the second optical signal carries the service data. By adopting the method, tracking and pointing can be realized by using the tracking and pointing signal, and the power of the receiving end for receiving the optical signal can be determined, so that the calibration of the optical transmission channel is completed.

The invention provides a method and a device for keeping butt joint of optical module channels. The method comprises the following steps: setting a mapping port of each local optical module channel to be in an MAC layer forced working state; sending a local speed reduction capability negotiation message; receiving an opposite terminal speed reduction capability negotiation message from an opposite terminal; when it is detected that a receiving end signal of a mapping port of any local optical module channel is abnormal, generating a fault channel notification message carrying a local fault optical module channel identifier, and sending the fault channel notification message through any local optical module channel; and according to the channel combination corresponding to each local support speed reduction rate and the channel combination corresponding to each opposite end support speed reduction rate, selecting an optical module channel combination which is commonly supported and has the maximum support speed reduction rate.