Optical transmitter and receiver transmission monitoring method and system

Abstract

The invention relates to the technical field of communication monitoring, and discloses an optical transmitter and receiver transmission monitoring method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring various operation data of an optical transmitter and receiver in real time, including temperature, voltage, current and signal intensity, and preprocessing the acquired data, including data cleaning, noise filtering and normalization; s2, nonlinear dynamic modeling and fault prediction, the invention also provides an optical transceiver transmission monitoring system, and the system comprises a data collection module which is used for collecting the parameters of temperature, voltage, current and signal intensity of the optical transceiver in real time; and the nonlinear dynamics analysis module is used for carrying out system modeling based on a nonlinear dynamics theory and calculating a Lyapunov index. By introducing the LQG control algorithm, the redundancy coding technology and the remote monitoring platform, optimization in the aspects of dynamic threshold adjustment, fault prediction and data transmission security is realized, and the intelligence, reliability and security of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of communication monitoring, in particular to an optical terminal transmission monitoring method and a system thereof. Background Art

[0002] In modern fiber-optic communication, broadcasting and television transmission, security monitoring and other industries, optical transceivers, as key equipment, undertake important tasks such as data transmission, signal processing and network connection. With the continuous expansion of application scenarios, the monitoring requirements for optical transceivers are also getting higher and higher. An efficient and accurate monitoring system can not only help detect equipment failures in a timely manner, but also effectively prevent failures from occurring, thereby ensuring the stable operation of the system.

[0003] In the prior art, the optical terminal monitoring system mainly relies on simple built-in sensors and static preset alarm mechanisms. The existing solutions generally judge the working status of the equipment by monitoring basic parameters such as temperature, voltage, and signal strength, and alarm through preset thresholds. This method can complete the status monitoring of the equipment to a certain extent and reduce the failure of the equipment caused by exceeding the threshold.

[0004] However, the existing technology still has some shortcomings. First, fixed threshold settings are difficult to cope with the various complex and dynamic changes that occur during the operation of the optical terminal, which can easily lead to false alarms or missed alarms, affecting the stability and reliability of the system. Second, most existing monitoring systems lack intelligent fault prediction and real-time diagnosis capabilities, resulting in the inability to warn of faults in the early stages, which in turn affects operation and maintenance efficiency. Third, traditional remote management platforms usually fail to provide data encryption and effective security protection, making information vulnerable to attacks during transmission, increasing the risk of data leakage and tampering. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an optical terminal transmission monitoring method and system thereof, which solves the problems of the existing optical terminal monitoring system in terms of dynamic threshold adjustment, fault prediction and data transmission security.

[0006] To achieve the above purpose, the present invention is implemented by the following technical scheme: a method for monitoring transmission of an optical terminal, comprising the following steps: S1. Data collection and preprocessing: real-time collection of various operating data of the optical transceiver, including temperature, voltage, current and signal strength, and preprocessing of the collected data, including data cleaning, noise filtering and normalization; S2. Nonlinear dynamics modeling and fault prediction: Use nonlinear dynamics theory to establish the system model of the optical terminal, calculate the stability of the system through the Lyapunov index, and further predict the critical point of the system through bifurcation analysis; S3, redundant coding and convolution coding: redundant coding and convolution coding are performed on the collected monitoring data to ensure that the data is not interfered by noise during transmission and to improve the reliability of data transmission; S4. Optimal control algorithm to optimize threshold adjustment: Based on the optimal control theory, the monitoring threshold of the optical terminal is optimized in real time through the LQG control algorithm, and the threshold of each parameter is adjusted according to the real-time data; S5. Fault warning and remote diagnosis: When the monitoring system detects a fault or signs of approaching a fault, it notifies the operation and maintenance personnel through an early warning mechanism and supports remote diagnosis and maintenance.

[0007] Preferably, the data collection and preprocessing includes: Use high-precision sensors to collect key parameters of the optical transceiver in real time, including but not limited to temperature, voltage, current and signal strength; De-noising the collected data, including removing sensor noise and outliers; The collected data are normalized to ensure that the values ​​of different parameters can be standardized to facilitate subsequent analysis.

[0008] Preferably, the nonlinear dynamics modeling and fault prediction include: The nonlinear dynamic model of the optical transceiver is established, and the system state is expressed by vector Indicates that it contains temperature and voltage parameters; Calculate Lyapunov exponent ,according to The positive or negative value of determines whether the system is close to an unstable state; Through bifurcation analysis, possible failure modes of the optical terminal system are identified and the critical state of the system is determined.

[0009] Preferably, the redundant coding and convolutional coding include: Perform LDPC redundant coding on the collected data to increase redundant bits, ensuring that data can be correctly restored even if it is affected by noise during transmission; Encode the data using convolutional coding to generate redundant data, and recover the received coded data using the Viterbi decoding algorithm; Adaptively adjust the coding strategy based on the channel quality of the current optical terminal system.

[0010] Preferably, the optimal control algorithm optimization threshold adjustment step includes: Adopt LQG control algorithm, according to the real-time status of the optical terminal system , calculate the control input at each moment ; Adjust the threshold of the system according to real-time feedback, through the control matrix Adjust monitoring parameters to minimize system errors; Update thresholds in real time to ensure that the monitoring system can adapt to environmental changes of the optical terminal.

[0011] Preferably, the fault warning and remote diagnosis steps include: When a fault occurs or is approaching, the system will issue an early warning through LED display, sound and light alarm, and email; Remote maintenance personnel can view real-time status and historical data through the system management platform, perform remote fault diagnosis and debug equipment; The system implements remote diagnosis function through encrypted network communication channels to ensure secure data transmission.

[0012] The present invention also provides an optical terminal transmission monitoring system, comprising: Data acquisition module, used to collect the temperature, voltage, current and signal strength parameters of the optical terminal in real time; Nonlinear dynamics analysis module, which is used to model the system based on nonlinear dynamics theory and calculate the Lyapunov exponent to predict possible failure modes of the system; The encoding module is used to perform redundant encoding and convolution encoding on the collected data to ensure the reliability of data transmission; Threshold adjustment module, which optimizes and adjusts the monitoring threshold of the optical terminal in real time through the LQG control algorithm; The early warning and remote diagnosis module is responsible for issuing early warnings when signs of faults are found, and supports remote maintenance and diagnosis.

[0013] Preferably, the data acquisition module includes a plurality of high-precision sensors and has the functions of data noise removal and normalization processing, wherein: The high-precision sensors include temperature sensors, voltage sensors, current sensors and signal strength sensors, which can monitor the operating environment and status of the optical terminal in real time; The data noise removal function uses the Kalman filter algorithm to estimate and correct noise, ensuring the accuracy and stability of the collected data; The data normalization processing function standardizes the data from different sensors and converts each monitoring data into the same measurement unit and range for subsequent analysis and modeling; The data acquisition module ensures the integrity and continuity of data through real-time sampling and caching functions, ensuring that the monitoring system can still operate stably under high-frequency acquisition.

[0014] Preferably, the nonlinear dynamics analysis module determines the stability of the optical terminal system and predicts possible failure modes by calculating the Lyapunov exponent and performing bifurcation analysis, wherein: The Lyapunov index calculation module evaluates the stability of the optical terminal system through a model based on nonlinear dynamics and tracks the dynamic changes of the system in real time; When the Lyapunov exponent value is positive, it means that the system may exhibit chaotic characteristics, and the module will issue an early warning to warn of possible system instability; The bifurcation analysis module uses a numerical method to scan the control parameters of the optical terminal system to find out the bifurcation points where the system state may suddenly change; When the bifurcation analysis module detects that the system has entered a bifurcation point, it promptly adjusts the control parameters and issues a warning signal to avoid irreversible failures of the optical terminal.

[0015] Preferably, the encoding module adaptively adjusts the data encoding strategy according to the channel quality of the optical terminal to ensure the accuracy and integrity of the data in different transmission environments, wherein: The adaptive coding strategy module automatically selects a suitable coding method according to the channel quality parameters monitored in real time, including but not limited to LDPC coding and convolution coding; When the channel quality is poor, the adaptive coding strategy module will automatically enable stronger error correction coding to improve the robustness of data transmission; The adaptive coding strategy module dynamically adjusts the coding rate according to the state of the communication link and provides real-time feedback on the evaluation result of the current network quality; The encoding module ensures that errors and losses in data transmission can be minimized in different environments by updating the transmission mode in real time, thereby improving the data accuracy and real-time performance of the entire optical terminal monitoring system.

[0016] The present invention provides an optical terminal transmission monitoring method and system thereof, which has the following beneficial effects: 1. The present invention introduces the LQG control algorithm to intelligently adjust the monitoring threshold of the optical terminal and optimize the system parameters according to the real-time working status. This dynamic adjustment mechanism allows the system to automatically adapt to environmental changes and avoids the limitations brought by fixed thresholds. Compared with the traditional method, it solves the dilemma that the optical terminal cannot flexibly respond to system state fluctuations and improves the response speed and stability of the system.

[0017] 2. The remote management platform allows operators to monitor and manage the optical transceiver system in real time no matter where they are. Through the platform, maintenance personnel can remotely adjust parameters, view operating data and receive early warning information. Compared with traditional methods, this remote monitoring and operation capability greatly improves operation and maintenance efficiency, reduces dependence on on-site operations, and improves the maintainability of the system.

[0018] 3. Through redundant coding and SSL / TLS encryption protocol, the present invention greatly improves the reliability and security of data transmission. No matter how the signal is interfered or lost, redundant coding ensures that the data is not affected, and the encryption protocol protects the information during the transmission from being stolen. Compared with traditional solutions, this security measure makes data transmission more stable and confidential, ensuring the efficient operation of the optical terminal monitoring system in harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart of the method of the present invention; Figure 2 It is a system structure diagram of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Please see attached Figure 1 The embodiment of the present invention provides a method for monitoring transmission of an optical terminal, comprising the following steps: S1. Data collection and preprocessing: real-time collection of various operating data of the optical transceiver, including temperature, voltage, current and signal strength, and preprocessing of the collected data, including data cleaning, noise filtering and normalization; In order to ensure that the operating status of the optical transceiver can be accurately monitored, it is necessary to obtain the data of various key parameters through high-precision sensors and pre-process these data. The core purpose of data pre-processing is to clean the data, remove noise, and perform standardization processing, ultimately providing a reliable basis for subsequent fault prediction and analysis.

[0022] In this embodiment, the data collection process is completed by multiple high-precision sensors. The sensors include but are not limited to: Temperature sensor: used to monitor the internal and external temperature of the optical transceiver in real time; Voltage sensor: monitors the input and output voltage of the optical transceiver; Current sensor: detects current changes; Signal strength sensor: Provides real-time feedback on fiber optic signal strength to ensure the quality of optical signals.

[0023] These sensors can continuously provide real-time data at a high frequency, usually once per second. The data of each sensor enters the data processing module through the sensor interface. For each collected data, the microprocessor first performs preliminary verification and format conversion to ensure that the data is not affected by errors during subsequent processing.

[0024] Due to environmental interference, sensor errors, and other reasons, the collected data often contains noise and outliers, which may affect the monitoring accuracy of the optical transceiver. Therefore, the collected data must be denoised.

[0025] In this embodiment, the noise removal adopts Kalman filtering technology, which is a recursive algorithm that can eliminate noise in time series data to obtain more accurate state estimation. The Kalman filtering algorithm includes two steps: prediction and update.

[0026] In the Kalman filter algorithm, the prediction formula for state estimation is as follows: ; ; in: is the predicted system state estimate; is the state transfer matrix, which represents the system from time To time changes; is the control input matrix, which indicates how the control input affects the system state; is the control input (e.g. the influence of external factors such as temperature change, current adjustment, etc.); is the predicted covariance matrix, which indicates the accuracy of the system estimation; is the process noise covariance matrix, which represents the error when the system model is incomplete; is the state estimate of the previous moment; is the error covariance matrix of the previous moment; is the transpose of the state transfer matrix.

[0027] The update formula is as follows: ; ; in: is the Kalman gain, which indicates the degree of correction of the current state estimate by the new observation data; is the actual measured value; is the observation matrix, which represents how to associate states with observation data; is the observation noise covariance matrix, which represents the noise introduced during the measurement process.

[0028] Through Kalman filtering, the data affected by noise is corrected to obtain an accurate estimate of the optical terminal status, which is crucial for subsequent data analysis and fault prediction.

[0029] In this embodiment, after data collection and denoising, the collected data will be normalized. Different types of sensor data usually have different dimensions and data ranges. For example, temperature may vary from -50°C to 100°C, while voltage may vary from 0V to 5V. If these data are directly input into the subsequent analysis model, some parameters may have a disproportionate impact on the overall analysis results.

[0030] To avoid this problem, we use data normalization to map all parameter data to a unified standard range. For example, temperature data, signal intensity, and current data are scaled to between 0 and 1 to ensure that each data has the same influence on subsequent nonlinear dynamic modeling and other analyses.

[0031] Specifically, data normalization is transformed using the following formula: ; in: is the original data value, which indicates the original parameter value collected by the sensor; and Respectively represent the possible minimum and maximum values ​​of the parameter in the data set; is the normalized data, and its range is [0,1].

[0032] Normalization ensures that different sensor data can be compared on the same scale and avoids deviations caused by different data ranges. In addition, normalized data can be better input into subsequent nonlinear dynamic modeling and fault prediction algorithms to ensure the accuracy of the results.

[0033] In some embodiments, data outliers or data loss may occur during data preprocessing. These outliers may be caused by sensor failure, external environmental interference, etc. If these outliers are not processed in a timely manner, subsequent analysis and prediction will be affected.

[0034] Therefore, in this embodiment, an outlier detection and processing step is added to the data preprocessing stage. By setting a reasonable threshold range (based on historical data or empirical values), the system will monitor and identify abnormal data in real time. When the data exceeds the set range, the system will automatically mark the data as abnormal and correct or discard it.

[0035] For example, if the temperature collected at a certain moment is far beyond the normal range (such as 1000°C), the data is considered abnormal and the alarm system will be triggered for correction. Correction methods include data interpolation and missing value filling to ensure the continuity of the data flow.

[0036] The data acquisition and preprocessing module collects key parameters through high-precision sensors, uses Kalman filtering to remove noise, and combines technical means such as data normalization and outlier processing. The preprocessing solution provided in this embodiment can ensure the accuracy of subsequent analysis and modeling.

[0037] S2. Nonlinear dynamics modeling and fault prediction: Use nonlinear dynamics theory to establish the system model of the optical terminal, calculate the stability of the system through the Lyapunov index, and further predict the critical point of the system through bifurcation analysis; Nonlinear dynamics modeling and fault prediction. The core of this part is to simulate the dynamic behavior of the optical terminal by establishing an accurate mathematical model, and use nonlinear dynamics methods to perform stability analysis and fault prediction. This step can help identify potential faults in real time through accurate system modeling, thereby effectively avoiding the impact of optical terminal faults on the overall system performance.

[0038] In this embodiment, the operation state of the optical terminal is regarded as a complex system with multiple nonlinear factors acting together. In order to accurately simulate the working state of the optical terminal, we established a mathematical model based on nonlinear dynamics theory. The model uses the state vector Indicates the current state of the system. The state vector contains important parameters of the optical transceiver, such as temperature, voltage, current, signal strength, etc. The evolution of the system can be described by the following nonlinear function: ; in: For the moment The state vector contains all the parameters that need to be monitored (such as temperature, voltage, current, etc.); is a nonlinear dynamic mapping function, which represents the relationship between system states and their evolution over time; It is an external control parameter, which indicates the changes of the system affected by factors such as environment and operating conditions.

[0039] The nonlinear dynamic model takes into account the complex nonlinear relationship between variables such as temperature and voltage, and can adapt to a variety of different working environments. The interaction between the parameters of the system may show nonlinear characteristics under the ever-changing operating state, so the model can well reflect the dynamic evolution of the optical terminal.

[0040] As an option, in this embodiment, the specific implementation of the model can be based on known physical laws and equipment characteristics to model the influence of various parameters. For example, temperature may be affected by factors such as current size and voltage fluctuation; the relationship between voltage and current may have nonlinear characteristics. Through this modeling method, a model that reflects all operating states of the optical terminal system can be obtained.

[0041] In this embodiment, in order to evaluate the stability of the optical terminal system, we use the Lyapunov Exponent to quantify whether the behavior of the system is stable. The Lyapunov Exponent is an important indicator for measuring the stability of a dynamic system and can reflect the sensitivity of the system to the initial conditions. Its calculation formula is: ; in: is the Lyapunov index, which indicates the stability of the system; For the moment The state disturbance vector of is the state disturbance vector at the initial moment; is a time variable, indicating the time step of the optical terminal operation; represents the natural logarithm function, which is used to calculate the logarithm of the disturbance ratio; Indicates at time The modulus or size of the state disturbance reflects the distance between the current system state and the initial state; Represents the modulus of the state disturbance at the initial time 0, which measures the magnitude of the disturbance at the initial time. Similarly, it is calculated using the Euclidean distance.

[0042] Specifically, when the Lyapunov exponent When >0, it means that the system is unstable and may enter a chaotic state; When <0, it indicates that the system tends to be stable; = 0, indicating that the stability of the system is at the limit and may fluctuate over time. By calculating the Lyapunov exponent in real time, the system can promptly determine whether the optical transceiver has entered an unstable state. Once detected, >0, the system will trigger a fault warning to remind the operation and maintenance personnel that the system is about to fail.

[0043] In this embodiment, we further enhance the fault prediction capability of the system through bifurcation analysis. Bifurcation analysis is a nonlinear dynamic analysis method that can help us identify the critical points and potential failure modes of the system. In the dynamic model of the optical terminal, some parameters of the system may change under certain conditions, causing the system to change from a stable state to an unstable state.

[0044] Specifically, assuming that the control parameters of the optical terminal are , by scanning different control parameter values, we can determine the bifurcation point of the system. The basic formula for bifurcation analysis is: ; in: For the moment The state vector of To describe the nonlinear mapping of the dynamic evolution of the system; It is a control parameter that represents the changes of the optical terminal system under different external environments or operating conditions.

[0045] By scanning different parameters The bifurcation analysis can identify the bifurcation points in the system, that is, the system may change from a stable state to an unstable state near these points. This process can help the optical terminal predict possible failures in advance and adjust the system control strategy in time.

[0046] As an option, this embodiment uses numerical methods to perform bifurcation analysis. Common numerical methods such as Newton's method can quickly and accurately find the bifurcation point of the optical terminal system. Through real-time bifurcation point detection, the system can adjust the control parameters when approaching the bifurcation point to avoid entering an unstable state.

[0047] By combining Lyapunov exponent and bifurcation analysis, the system can monitor the status of the optical transceiver in real time and predict potential failure modes. When the system detects that the Lyapunov exponent is greater than 0 or the bifurcation analysis results show that the system is close to the bifurcation point, the fault warning mechanism will automatically start.

[0048] In some embodiments, the early warning mechanism includes multiple notification methods such as sound and light alarm, LED display and email. These methods can quickly transmit fault information to operation and maintenance personnel to ensure that the system can be handled in a timely manner. Through real-time fault prediction and early warning, the operation safety of the optical terminal is greatly improved, and the downtime caused by equipment failure is reduced.

[0049] Through nonlinear dynamic modeling, Lyapunov exponent calculation, bifurcation analysis and other technologies, the system can accurately evaluate the stability of the optical terminal and issue early warning when the system is close to failure or instability. The combination of these technical means not only improves the accuracy of fault prediction, but also ensures that the optical terminal can maintain stable operation under various environmental conditions.

[0050] S3, redundant coding and convolution coding: redundant coding and convolution coding are performed on the collected monitoring data to ensure that the data is not interfered by noise during transmission and to improve the reliability of data transmission; Redundant coding and convolutional coding are used to enhance the reliability of data during transmission. Since the signals transmitted by the optical terminal may be affected by noise interference or channel quality fluctuations, redundant coding and convolutional coding play a key role in ensuring the correctness and integrity of data transmission.

[0051] In this embodiment, redundant coding and convolutional coding technologies increase the anti-interference capability of data by adding redundant information, so that the receiving end can detect and repair errors in transmission. The combination of the two ensures that the data in the optical terminal monitoring system can maintain high transmission reliability in an unstable network environment.

[0052] Generally, in order to ensure the reliability of data transmission, the system performs low-density parity check (LDPC) redundant coding on the original monitoring data. LDPC code is an error correction code widely used in modern communication systems. It adds redundant information so that the receiving end can detect and correct a certain number of errors when receiving coded data with noise.

[0053] In this embodiment, the system collects the original data vector Encoded by LDPC encoder, where Indicates the length of the original data. LDPC coding is done by generating the matrix Map the data into a longer encoding vector , the generated encoded data Contains redundant information, which can help the receiving end detect and correct transmission errors. The specific formula is as follows: ; in: is the encoded data vector, containing redundant information; is the original data vector, representing the collected monitoring data; is the generator matrix of the LDPC code, which defines how to map data into the coding space; Represents matrix transpose.

[0054] Specifically, LDPC codes can maintain good error correction capabilities at relatively low signal-to-noise ratios (SNRs). Through LDPC redundant coding, the receiver can perform error detection and correction after receiving the transmitted data, greatly reducing system operation anomalies caused by data transmission errors.

[0055] As an option, in addition to LDPC redundant coding, the system also uses convolutional coding to process data. Convolutional coding generates a set of redundant information by convolution operation on the data stream, thereby enhancing the signal's anti-interference ability. When the channel quality is poor, convolutional coding can significantly improve the data integrity during transmission.

[0056] In this embodiment, the input data stream After passing through the convolutional encoder, the output data stream is generated , which is done by convolution transfer matrix To achieve this, the formula is as follows: ; in: The input data stream includes real-time monitoring data from the optical terminal; is the output coded data stream, including redundant bits; is the transfer matrix of the convolutional encoder, which represents the state transition rule of the data stream.

[0057] Generally, convolutional coding adds redundant bits so that even if the received signal is subject to noise or interference, the receiving end can still recover the original information through decoding. The receiving end decodes the coded signal through the Viterbi decoding algorithm to recover the original monitoring data.

[0058] Specifically, the Viterbi decoding algorithm uses dynamic programming technology to calculate the cumulative distance of each possible path and select the most likely path to recover data. The process of Viterbi decoding can be expressed as the following formula: ; in: is the path distance in the decoding process, which represents the cumulative error of each path; is the received coded signal; As the distance measure between the received signal and the encoded signal, the Hamming distance or the Euclidean distance is usually used; For the assumed input data, the goal of Viterbi decoding is to find the shortest path and recover the original data; is the input data vector, which represents the original data sequence to be decoded; G is the state transition matrix of the convolutional encoder, which defines the input data How to convert to encoded data ; is the time step (TimeStep), which indicates the sequence position of the data during the transmission process; Represents the input data Perform the minimize operation.

[0059] Through Viterbi decoding, the system can recover the original data from the received signal based on the principle of minimizing path distance. Even if the signal is lost or damaged, the integrity of the data can still be guaranteed.

[0060] As an option, in order to further enhance the reliability of data transmission, the system adopts an adaptive coding strategy. This strategy dynamically adjusts the coding scheme based on the real-time evaluated channel quality. During the transmission process of the optical terminal, the channel quality may change, such as fluctuations in signal strength, noise interference and other factors. Therefore, the use of an adaptive coding strategy can automatically increase the coding strength when the channel quality is poor, ensuring high reliability of data transmission.

[0061] Specifically, when the channel quality is poor (for example, when the signal-to-noise ratio is low or the bit error rate is high), the system automatically switches to a stronger coding scheme. At this time, the system will increase the redundant bits of the LDPC code or use a stronger convolutional coding method to enhance the data's anti-interference ability. Through this adaptive mechanism, the system can maintain high data transmission quality in different communication environments.

[0062] The reliability of data transmission has been significantly enhanced through the application of redundant coding and convolutional coding. LDPC coding provides powerful error correction capabilities for data, ensuring that errors in data transmission can be effectively corrected even under harsh channel conditions. Convolutional coding improves the signal's ability to resist noise and reduces data loss during transmission by adding redundant information. The adaptive coding strategy automatically adjusts the coding scheme based on the real-time assessed channel quality, further improving the robustness and reliability of data transmission.

[0063] S4. Optimal control algorithm to optimize threshold adjustment: Based on the optimal control theory, the monitoring threshold of the optical terminal is optimized in real time through the LQG control algorithm, and the threshold of each parameter is adjusted according to the real-time data; Optimal control algorithm optimizes threshold adjustment. The optimal control algorithm (LQG control algorithm) dynamically adjusts the system's monitoring threshold, thereby improving the system's responsiveness and stability.

[0064] In this embodiment, in order to optimize the performance of the optical terminal monitoring system, the system uses the LQG control algorithm to adjust the monitoring threshold. The LQG control algorithm minimizes the cost function through the feedback mechanism while ensuring the stability of the system, thereby optimizing the monitoring threshold of the optical terminal. The key to this process is to optimize and adjust the monitoring threshold in real time, so that the system can maintain efficient and stable operation under a changing working environment.

[0065] Generally, the LQG control algorithm is an optimal control method based on feedback. Its core idea is to optimize the control input of the system by minimizing a cost function. In the present invention, the LQG control algorithm dynamically adjusts the monitoring threshold of the optical terminal by minimizing the cost function, so that the system can adapt to different working conditions and maintain optimal performance.

[0066] Specifically, the state vector of the system It contains all the key parameters that need to be monitored, such as the temperature, voltage, current, etc. of the optical transceiver. The LQG control algorithm calculates the optimal control input at each moment. , to minimize the cost in the objective function. The relationship between the system state and control input is expressed by the state equation: ; in: For the moment The state vector includes various parameters monitored by the optical transceiver (such as temperature, voltage, current, etc.); is the state transfer matrix, which represents the system from time arrive Status changes; is the control input matrix, which represents the control input Impact on system status; is the control input, i.e. the adjusted monitoring threshold.

[0067] Through these equations, the LQG control algorithm is able to predict future system behavior based on the current state and calculate the optimal control input to ensure that the system always remains in a stable state.

[0068] In LQG control, optimal control is achieved by minimizing a cost function. The cost function usually consists of two parts: one part represents the error between the system state and the target state, and the other part represents the cost of the control input. The cost function is defined as follows: ; in: is the overall cost function, which represents the cost in the entire control process; For the moment The state vector of For the moment The control input (i.e., the optimized threshold adjustment amount); is the state weighting matrix, which indicates the importance attached to the system state error; is the control input weighting matrix, which represents the cost of adjusting the threshold; is the length of the control cycle; : State error cost, which is used to quantify the deviation between the current state of the system and the expected target state, ensuring that the system operates in the optimal working area; :Control input cost, measure the size of control signal, and prevent excessive control from causing increased energy consumption or system instability.

[0069] As an option, in actual application, the matrix can be adjusted according to the working environment and requirements of the optical terminal. and Make adjustments. Larger The weight will force the system to reduce the state error, while the larger The weights reduce excessive threshold adjustments in the system and avoid possible system instability caused by excessive adjustments.

[0070] Specifically, the LQG control algorithm dynamically calculates the optimal control input through a state feedback mechanism , thereby adjusting the monitoring threshold of the optical terminal. The state feedback formula is: ; in: For the moment The control input is the optimized monitoring threshold; is the state feedback gain matrix, which represents the influence of each monitoring parameter (such as temperature, voltage, etc.) on the control input; For the moment The system state vector.

[0071] By adjusting the state feedback gain matrix The system can adjust the monitoring threshold in real time according to the current operating status of the optical terminal, ensuring that the optical terminal can maintain optimal performance under different operating environments. This process makes the monitoring system of the optical terminal adaptive and can automatically adapt to changes in different working environments.

[0072] Generally speaking, as the system status continues to change, the monitoring threshold of the optical terminal also needs to be adjusted in real time to adapt to environmental fluctuations. For example, when key parameters such as temperature and voltage are close to the warning threshold, the LQG control algorithm will calculate a new monitoring threshold based on the system status to avoid unnecessary fault response or over-adjustment.

[0073] As an option, the LQG control algorithm in this embodiment also considers the impact of historical data. Based on real-time feedback, the system can make predictions based on past trends and further optimize threshold adjustments. By introducing the trend of historical data, the system can take measures in advance to avoid failures or instability caused by sudden fluctuations.

[0074] In one possible implementation, the LQG control algorithm adaptively adjusts the gain matrix To enhance the robustness of the system. As the working environment changes (such as changes in temperature, humidity and other factors), the system can calculate the feedback control input in real time based on the new state information, so that the optical terminal always maintains high efficiency and stability in different working environments.

[0075] By introducing dynamic adjustment and adaptive mechanisms, the LQG control algorithm effectively solves the shortcomings of traditional fixed threshold control strategies and can significantly improve the response speed and stability of the optical terminal monitoring system in complex environments.

[0076] By minimizing the cost function, LQG control not only balances the system stability and the cost of threshold adjustment, but also adjusts the monitoring parameters in real time through the state feedback mechanism. The adaptive adjustment of the gain matrix enhances the robustness of the system, allowing the optical terminal to maintain optimal performance in a complex and changing environment.

[0077] S5. Fault warning and remote diagnosis: When the monitoring system finds a fault or signs of approaching a fault, it notifies the operation and maintenance personnel through the early warning mechanism and supports remote diagnosis and maintenance; In steps S1 to S4, the monitoring data of the optical terminal has been effectively collected, processed, modeled and optimized. Next, it is necessary to ensure that this data can be transmitted stably and reliably, and be monitored and managed in real time through the remote monitoring system. The following introduces the technical details of data transmission mechanism, communication encryption, remote management platform and data security to ensure the efficiency, reliability and security of the transmission process.

[0078] In this embodiment, data transmission relies on an efficient and reliable communication interface, combined with redundant coding, encryption technology and an intelligent remote management platform to achieve real-time transmission and remote control of optical terminal monitoring data. Through this mechanism, remote operators can view and adjust system parameters at any time and any place, respond to any abnormalities in a timely manner, and ensure the stable operation of the optical terminal monitoring system.

[0079] Generally speaking, the stability of data transmission depends on efficient protocols and communication interfaces. In order to ensure the reliability of data transmission, the system in this embodiment adopts multiple network protocols and communication interfaces, which can achieve stable data transmission in multiple communication environments.

[0080] Specifically, the optical terminal sends data to the remote management platform through a communication module that supports Ethernet, optical fiber, Wi-Fi and other interfaces. During the transmission process, the data uses redundant coding and convolution coding technology to ensure that the data can be fully restored even if network jitter, packet loss or signal attenuation occurs during the data transmission process.

[0081] In a possible implementation, the communication protocol is TCP / IP protocol, which provides reliability guarantee for network transmission. In the case of data packet loss, the system can recover data through automatic retransmission request (ARQ) mechanism to ensure the integrity of data transmitted by the optical terminal.

[0082] In order to ensure the security of data during transmission, the system adopts SSL / TLS encryption protocol during data transmission to ensure that all transmitted data is protected to prevent data from being maliciously intercepted, tampered or leaked.

[0083] The data encryption process is as follows: ; in: It is an encrypted data packet, which has been encrypted by SSL / TLS; Indicates the process of encryption using SSL / TLS protocol; It is the original data packet, including the monitoring data of the optical terminal.

[0084] As an option, at the receiving end, the decryption process uses the corresponding key to decrypt and restore the original data. This ensures that the system can ensure the integrity and confidentiality of the data even if the data packet is attacked during transmission.

[0085] Specifically, the remote management platform is an important part of this system, which enables operators to monitor the various operating parameters of the optical transceiver in real time. The platform receives data transmitted by the optical transceiver through the API interface, and technicians can view the health status, real-time data and fault warning information of the optical transceiver through the platform.

[0086] In one possible implementation, the platform provides multiple interaction modes, and operators can choose to view real-time data, receive alarm information, or perform remote control through a graphical interface. The platform supports the following functions: Real-time monitoring: Operators can view the current working status of the optical transceiver (such as temperature, voltage, current, etc.) and display historical data through data charts; Fault warning: The platform analyzes the received transmission data and issues real-time alarms for abnormal situations. The alarm information can be delivered to technical personnel through email, SMS, APP notifications, etc. Remote adjustment: Operators can directly adjust the working parameters of the optical transceiver through the platform, such as threshold setting, working mode switching, etc. Maintenance recommendations: Based on historical data and trend analysis, the platform will also automatically generate regular equipment maintenance reports and fault prediction recommendations.

[0087] Generally, the platform can effectively identify the health status of the equipment by analyzing the real-time data of the optical terminal equipment. In this embodiment, the system not only relies on real-time data, but also combines historical data, equipment operation trends and failure modes to perform fault prediction and diagnosis.

[0088] Specifically, the system uses machine learning algorithms (such as support vector machines, decision trees, etc.) to evaluate the health of the optical transceiver. Based on the historical operating data of the equipment, these algorithms can identify potential failure modes and issue warnings in advance.

[0089] For example, the system may analyze the relationship between temperature and current and find that when the temperature continues to rise, the current of the equipment also changes abnormally, which may be a precursor to equipment failure. Through fault pattern recognition, the system can take measures in advance, such as issuing warnings, suggesting technicians to check, or even automatically adjusting operating parameters.

[0090] As an option, in order to ensure high availability of the system, dual-machine hot standby and load balancing mechanisms are introduced in this embodiment. The dual-machine hot standby mechanism shares the load between two servers. When one server fails, the other server can immediately take over the work to ensure high availability of the system. The load balancing mechanism distributes the load to multiple servers through an intelligent algorithm, thereby avoiding overload of any single device and ensuring the performance and stability of the system.

[0091] Specifically, the load balancer will dynamically select the most suitable server to handle requests based on the server's current load and response time, preventing any one device from becoming a bottleneck. The combination of dual-machine hot standby and load balancing improves the stability of the entire system and avoids the impact of single point failures on the system.

[0092] Redundant coding and convolutional coding technologies ensure the reliability of data transmission, while SSL / TLS encryption protocols provide data security protection. Through the remote management platform, technicians can monitor the system, diagnose faults and adjust parameters anytime and anywhere, effectively improving the system's response speed and maintenance efficiency.

[0093] The optical terminal transmission monitoring system described below and the optical terminal transmission monitoring method described above can be referred to each other.

[0094] Please see attached Figure 2 The present invention also provides an optical terminal transmission monitoring system, comprising: Data acquisition module, used to collect the temperature, voltage, current and signal strength parameters of the optical terminal in real time; Nonlinear dynamics analysis module, which is used to model the system based on nonlinear dynamics theory and calculate the Lyapunov exponent to predict possible failure modes of the system; The encoding module is used to perform redundant encoding and convolution encoding on the collected data to ensure the reliability of data transmission; Threshold adjustment module, which optimizes and adjusts the monitoring threshold of the optical terminal in real time through the LQG control algorithm; The early warning and remote diagnosis module is responsible for issuing early warnings when signs of faults are found, and supports remote maintenance and diagnosis.

[0095] The system of this embodiment can be used to execute the above method embodiments, and its principles and technical effects are similar, which will not be repeated here.

[0096] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring transmission of an optical terminal, characterized in that: The following steps are involved: S1. Data collection and preprocessing: real-time collection of various operating data of the optical transceiver, including temperature, voltage, current and signal strength, and preprocessing of the collected data, including data cleaning, noise filtering and normalization; S2. Nonlinear dynamics modeling and fault prediction: Use nonlinear dynamics theory to establish the system model of the optical terminal, calculate the stability of the system through the Lyapunov index, and further predict the critical point of the system through bifurcation analysis; S3, redundant coding and convolution coding: redundant coding and convolution coding are performed on the collected monitoring data to ensure that the data is not interfered by noise during transmission and to improve the reliability of data transmission; S4. Optimal control algorithm to optimize threshold adjustment: Based on the optimal control theory, the monitoring threshold of the optical terminal is optimized in real time through the LQG control algorithm, and the threshold of each parameter is adjusted according to the real-time data; S5. Fault warning and remote diagnosis: When the monitoring system detects a fault or signs of approaching a fault, it notifies the operation and maintenance personnel through an early warning mechanism and supports remote diagnosis and maintenance.

2. The optical transmission monitoring method according to claim 1, characterized in that: The data collection and preprocessing include: Use high-precision sensors to collect key parameters of the optical transceiver in real time, including but not limited to temperature, voltage, current and signal strength; De-noising the collected data, including removing sensor noise and outliers; The collected data are normalized to ensure that the values ​​of different parameters can be standardized to facilitate subsequent analysis.

3. The optical transmission monitoring method according to claim 1 is characterized in that: The nonlinear dynamics modeling and fault prediction include: The nonlinear dynamic model of the optical transceiver is established, and the system state is expressed by vector Indicates that it contains temperature and voltage parameters; Calculate Lyapunov exponent ,according to The positive or negative value of determines whether the system is close to an unstable state; Through bifurcation analysis, possible failure modes of the optical terminal system are identified and the critical state of the system is determined.

4. The optical transmission monitoring method according to claim 1, characterized in that: The redundant coding and convolutional coding include: Perform LDPC redundant coding on the collected data to increase redundant bits, ensuring that data can be correctly restored even if it is affected by noise during transmission; Encode the data using convolutional coding to generate redundant data, and recover the received coded data using the Viterbi decoding algorithm; Adaptively adjust the coding strategy based on the channel quality of the current optical terminal system.

5. The optical transmission monitoring method according to claim 1 is characterized in that: The optimal control algorithm optimization threshold adjustment step comprises: Adopt LQG control algorithm, according to the real-time status of the optical terminal system , calculate the control input at each moment ; Adjust the threshold of the system according to real-time feedback, through the control matrix Adjust monitoring parameters to minimize system errors; Update thresholds in real time to ensure that the monitoring system can adapt to environmental changes of the optical terminal.

6. The optical terminal transmission monitoring method according to claim 1 is characterized in that: The fault early warning and remote diagnosis steps include: When a fault occurs or is approaching, the system will issue an early warning through LED display, sound and light alarm, and email; Remote maintenance personnel can view real-time status and historical data through the system management platform, perform remote fault diagnosis and debug equipment; The system implements remote diagnosis function through encrypted network communication channels to ensure secure data transmission.

7. An optical terminal transmission monitoring system, characterized in that: A method for monitoring transmission of an optical terminal according to any one of claims 1 to 6, comprising: Data acquisition module, used to collect the temperature, voltage, current and signal strength parameters of the optical terminal in real time; Nonlinear dynamics analysis module, which is used to model the system based on nonlinear dynamics theory and calculate the Lyapunov exponent to predict possible failure modes of the system; The encoding module is used to perform redundant encoding and convolution encoding on the collected data to ensure the reliability of data transmission; Threshold adjustment module, which optimizes and adjusts the monitoring threshold of the optical terminal in real time through the LQG control algorithm; The early warning and remote diagnosis module is responsible for issuing early warnings when signs of faults are found, and supports remote maintenance and diagnosis.

8. The optical terminal transmission monitoring system according to claim 7, characterized in that: The data acquisition module includes multiple high-precision sensors and has data noise removal and normalization processing functions, wherein: The high-precision sensors include temperature sensors, voltage sensors, current sensors and signal strength sensors, which can monitor the operating environment and status of the optical terminal in real time; The data noise removal function uses the Kalman filter algorithm to estimate and correct noise, ensuring the accuracy and stability of the collected data; The data normalization processing function standardizes the data from different sensors and converts each monitoring data into the same measurement unit and range for subsequent analysis and modeling; The data acquisition module ensures the integrity and continuity of data through real-time sampling and caching functions, ensuring that the monitoring system can still operate stably under high-frequency acquisition.

9. The optical transmission monitoring system according to claim 7, characterized in that: The nonlinear dynamics analysis module determines the stability of the optical terminal system and predicts possible failure modes by calculating the Lyapunov exponent and performing bifurcation analysis, wherein: The Lyapunov index calculation module evaluates the stability of the optical terminal system through a model based on nonlinear dynamics and tracks the dynamic changes of the system in real time; When the Lyapunov exponent value is positive, it means that the system may exhibit chaotic characteristics, and the module will issue an early warning to warn of possible system instability; The bifurcation analysis module uses a numerical method to scan the control parameters of the optical terminal system to find out the bifurcation points where the system state may suddenly change; When the bifurcation analysis module detects that the system has entered a bifurcation point, it promptly adjusts the control parameters and issues a warning signal to avoid irreversible failures of the optical terminal.

10. The optical terminal transmission monitoring system according to claim 7, characterized in that: The encoding module adaptively adjusts the data encoding strategy according to the channel quality of the optical terminal to ensure the accuracy and integrity of the data in different transmission environments, wherein: The adaptive coding strategy module automatically selects a suitable coding method according to the channel quality parameters monitored in real time, including but not limited to LDPC coding and convolution coding; When the channel quality is poor, the adaptive coding strategy module will automatically enable stronger error correction coding to improve the robustness of data transmission; The adaptive coding strategy module dynamically adjusts the coding rate according to the state of the communication link and provides real-time feedback on the evaluation result of the current network quality; The encoding module ensures that errors and losses in data transmission can be minimized in different environments by updating the transmission mode in real time, thereby improving the data accuracy and real-time performance of the entire optical terminal monitoring system.

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