An optical terminal transmission monitoring method and system thereof

Abstract

The present invention relates to the technical field of communication monitoring, and discloses an optical terminal transmission monitoring method, which includes the following steps: S1, data acquisition and preprocessing: real-time collect various operating data of the optical terminal, including temperature, voltage, current and signal strength, and preprocess the collected data, including data cleaning, noise filtering and normalization; S2, non-linear dynamics modeling and fault prediction. The present invention also provides an optical terminal transmission monitoring system, including: a data acquisition module, which is used to collect the parameters of the temperature, voltage, current and signal strength of the optical terminal in real time; a non-linear dynamics analysis module, which is used to perform system modeling based on non-linear dynamics theory and calculate the Lyapunov exponent. By introducing the LQG control algorithm, redundant coding technology and remote monitoring platform, the present invention realizes the optimization in dynamic threshold adjustment, fault prediction and data transmission security, and improves the intelligence, reliability and security of the system.

Description

Technical Field

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

[0002] In modern industries such as optical fiber communication, radio and television transmission, and security monitoring, optical terminals, as key devices, undertake important tasks such as data transmission, signal processing, and network connection. With the continuous expansion of application scenarios, the monitoring requirements for optical terminals 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, thus ensuring the stable operation of the system.

[0003] In the prior art, optical terminal monitoring systems mainly rely on simple built-in sensors and static preset alarm mechanisms. Existing solutions generally judge the working state of the device by monitoring basic parameters such as temperature, voltage, and signal strength, and issue alarms through preset thresholds. This method can, to a certain extent, complete the status monitoring of the device and reduce failures caused by exceeding the thresholds.

[0004] However, there are still some deficiencies in the prior art; firstly, the fixed threshold setting is difficult to cope with various complex and dynamic changes that occur during the operation of the optical terminal, easily leading to false alarms or missed alarms, affecting the stability and reliability of the system; secondly, most existing monitoring systems lack intelligent fault prediction and real-time diagnosis capabilities, resulting in failures not being able to be predicted in the initial stage, thus affecting the operation and maintenance efficiency; furthermore, traditional remote management platforms usually fail to provide data encryption and effective security guarantees, resulting in information being vulnerable to attacks during transmission, increasing the risks of data leakage and tampering. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides an optical terminal transmission monitoring method and system thereof, which solve 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 objectives, the present invention is realized through the following technical solutions: An optical terminal transmission monitoring method includes the following steps:

[0007] S1. Data acquisition and preprocessing: Real-time collect various operating data of the optical terminal, including temperature, voltage, current, and signal strength, and perform preprocessing on the collected data, including data cleaning, noise filtering, and normalization;

[0008] S2. Nonlinear dynamics modeling and fault prediction: Use nonlinear dynamics theory to establish a system model of the optical terminal, calculate the stability of the system through Lyapunov exponents, and further predict the critical points of the system through bifurcation analysis;

[0009] S3, Redundancy Encoding and Convolutional Encoding: Perform redundancy encoding and convolutional encoding on the collected monitoring data to ensure that the data is not affected by noise during transmission and improve the reliability of data transmission;

[0010] S4, Optimizing Threshold Adjustment with Optimal Control Algorithm: Based on the optimal control theory, use the LQG control algorithm to optimize the monitoring threshold of the optical terminal in real time and adjust the thresholds of various parameters according to the real-time data;

[0011] S5, Fault Warning and Remote Diagnosis: When the monitoring system detects a fault or signs approaching a fault, notify the operation and maintenance personnel through the warning mechanism and support remote diagnosis and maintenance.

[0012] Preferably, the data acquisition and preprocessing include:

[0013] Collect various key parameters of the optical terminal in real time through high-precision sensors, including but not limited to temperature, voltage, current, and signal strength;

[0014] Denoise the collected data, including removing sensor noise and outliers;

[0015] Normalize the collected data to ensure that the values of different parameters can be unified, facilitating subsequent analysis.

[0016] Preferably, the non-linear dynamics modeling and fault prediction include:

[0017] Establish a non-linear dynamics model of the optical terminal, where the system state is represented by the vector , including temperature and voltage parameters;

[0018] Calculate the Lyapunov exponent , and judge whether the system is approaching an unstable state according to the positive or negative value of ;

[0019] Identify possible fault modes of the optical terminal system through bifurcation analysis and determine the critical state of the system.

[0020] Preferably, the redundancy encoding and convolutional encoding include:

[0021] Perform LDPC redundancy encoding on the collected data, add redundant bits, and ensure that the data can still be correctly recovered even if affected by noise during data transmission;

[0022] Use convolutional encoding to encode the data, generate redundant data, and use the Viterbi decoding algorithm to recover the received encoded data;

[0023] Based on the channel quality of the current optical terminal system, adaptively adjust the encoding strategy.

[0024] Preferably, the optimal control algorithm optimization threshold adjustment step includes:

[0025] Adopt the LQG control algorithm, and calculate the control input at each moment according to the real-time state of the optical terminal system , calculate the control input at each moment ;

[0026] Adjust the threshold of the system according to the real-time feedback, and adjust the monitoring parameters through the control matrix to minimize the system error;

[0027] Update the threshold in real time to ensure that the monitoring system can adapt to the environmental changes of the optical terminal.

[0028] Preferably, the fault warning and remote diagnosis step includes:

[0029] When a fault occurs or is approaching, the system issues a warning through LED display, sound and light alarm, and email;

[0030] Remote maintenance personnel view the real-time state and historical data through the system management platform, perform remote fault diagnosis and debug the equipment;

[0031] The system realizes the remote diagnosis function through an encrypted network communication channel to ensure the secure transmission of data.

[0032] The present invention also provides an optical terminal transmission monitoring system, including:

[0033] A data acquisition module for real-time acquisition of parameters such as the temperature, voltage, current, and signal strength of the optical terminal;

[0034] A non-linear dynamics analysis module for system modeling based on non-linear dynamics theory and calculation of Lyapunov exponents to predict possible fault modes of the system;

[0035] An encoding module for performing redundant encoding and convolutional encoding on the acquired data to ensure the reliability of data transmission;

[0036] A threshold adjustment module for real-time optimization and adjustment of the monitoring threshold of the optical terminal through the LQG control algorithm;

[0037] A warning and remote diagnosis module responsible for issuing a warning when a fault sign is found and supporting remote maintenance and diagnosis.

[0038] Preferably, the data acquisition module includes multiple high-precision sensors and has the functions of data noise removal and normalization processing, wherein:

[0039] The high-precision sensor includes a temperature sensor, a voltage sensor, a current sensor, and a signal strength sensor, and can monitor the operating environment and status of the optical terminal in real time;

[0040] The data noise removal function performs noise estimation and correction through the Kalman filtering algorithm to ensure the accuracy and stability of the collected data;

[0041] The data normalization processing function standardizes the data from different sensors, converting various monitoring data to the same measurement unit and range for subsequent analysis and modeling;

[0042] 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 sampling.

[0043] Preferably, the nonlinear dynamics analysis module judges the stability of the optical terminal system and predicts possible fault modes by calculating the Lyapunov exponent and performing bifurcation analysis, where:

[0044] The Lyapunov exponent 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;

[0045] When the Lyapunov exponent value is positive, it indicates that the system may exhibit chaotic characteristics, and the module will issue a warning to remind of possible system instability;

[0046] The bifurcation analysis module uses numerical methods to scan the control parameters of the optical terminal system to find the bifurcation points where the system state may undergo sudden changes;

[0047] When the bifurcation analysis module detects that the system enters a bifurcation point, it timely adjusts the control parameters and issues a warning signal to avoid irreparable faults in the optical terminal.

[0048] Preferably, the coding module adaptively adjusts the data coding strategy according to the channel quality of the optical terminal to ensure the accuracy and integrity of data in different transmission environments, where:

[0049] The adaptive coding strategy module automatically selects a suitable coding method according to the real-time monitored channel quality parameters, including but not limited to LDPC coding and convolutional coding;

[0050] 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;

[0051] 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 results of the current network quality;

[0052] The encoding module ensures minimizing errors and losses in data transmission to the greatest extent in different environments by updating the transmission mode in real time, improving the data accuracy and real-time performance of the entire optical terminal monitoring system.

[0053] The present invention provides an optical terminal transmission monitoring method and system, which have the following beneficial effects:

[0054] 1. The present invention introduces the LQG control algorithm to intelligently adjust the monitoring threshold of the optical terminal and optimize system parameters according to the real-time working state. This dynamic adjustment mechanism enables the system to automatically adapt to environmental changes, avoiding the limitations brought by fixed thresholds. Compared with traditional methods, it solves the dilemma that the optical terminal cannot flexibly respond to system state fluctuations, improving the response speed and stability of the system.

[0055] 2. The remote management platform enables operators to monitor and manage the optical terminal system in real time regardless of their location. Through the platform, maintenance personnel can remotely adjust parameters, view operation data, and receive warning information. Compared with traditional methods, this remote monitoring and operation ability greatly improves the operation and maintenance efficiency, reduces the dependence on on-site operations, and improves the maintainability of the system.

[0056] 3. Through redundant encoding and the SSL / TLS encryption protocol, the present invention greatly improves the reliability and security of data transmission. Regardless of signal interference or loss, redundant encoding ensures that the data is not affected, while the encryption protocol protects the information during transmission from being stolen. Compared with traditional solutions, this security guarantee 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

[0057] Figure 1 is a flowchart of the method of the present invention;

[0058] Figure 2 is a structural diagram of the system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0060] Please refer to the attached Figure 1 , the embodiments of the present invention provide an optical terminal transmission monitoring method, including the following steps:

[0061] S1. Data Collection and Preprocessing: Real-time collect various operating data of the optical terminal, including temperature, voltage, current, and signal strength, and preprocess the collected data, including data cleaning, noise filtering, and normalization;

[0062] To ensure that the operating status of the optical terminal can be accurately monitored, it is necessary to obtain data on various key parameters through high-precision sensors and preprocess this data. The core purpose of data preprocessing is to clean the data, remove noise, and perform standardization processing, ultimately providing a reliable basis for subsequent fault prediction and analysis.

[0063] In this embodiment, the data collection process is completed by multiple high-precision sensors. The sensors include but are not limited to:

[0064] Temperature sensor: Used to monitor the temperature inside and outside the optical terminal in real time;

[0065] Voltage sensor: Monitor the input and output voltages of the optical terminal;

[0066] Current sensor: Detect current changes;

[0067] Signal strength sensor: Real-time feedback of the optical fiber signal strength to ensure the quality of the optical signal.

[0068] 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 piece of collected data, it is first preliminarily verified and format-converted by the microprocessor to ensure that the data is not affected by errors during subsequent processing.

[0069] Due to reasons such as environmental interference and errors in the sensors themselves, the collected data often contains noise and outliers, which may affect the monitoring accuracy of the optical terminal. Therefore, it is necessary to remove the noise from the collected data.

[0070] In this embodiment, the noise removal uses the Kalman filtering technique. Kalman filtering is a recursive algorithm that can eliminate noise in time series data and obtain a more accurate state estimate. The Kalman filtering algorithm includes two steps: prediction and update.

[0071] In the Kalman filtering algorithm, the prediction formula for state estimation is as follows:

[0072] ;

[0073] ;

[0074] Where: is the predicted system state estimate; is the state transition matrix, indicating that the system goes from time Up to the moment Change; Is the control input matrix, indicating how the control input affects the system state; Is the control input (such as the influence of external factors like temperature change, current adjustment, etc.); Is the predicted covariance matrix, indicating the accuracy of the system estimation; Is the process noise covariance matrix, indicating the error when the system model is incomplete; Is the state estimate at the previous moment; Is the error covariance matrix at the previous moment; Is the transpose of the state transition matrix.

[0075] The update formula is as follows:

[0076] ;

[0077] ;

[0078] Where: Is the Kalman gain, indicating the degree of correction of the new observation data to the current state estimate; Is the actual measurement value; Is the observation matrix, indicating how to relate the state to the observation data; Is the observation noise covariance matrix, indicating the noise introduced in the measurement process.

[0079] Through Kalman filtering, the data affected by noise is corrected to obtain an accurate state estimate of the optical transceiver. This is crucial for subsequent data analysis and fault prediction.

[0080] In this embodiment, after data acquisition and denoising processing, the collected data will be normalized. Different types of sensor data usually have different dimensions and data ranges. For example, the temperature may vary between -50°C and 100°C, while the voltage may vary between 0V and 5V. If these data are directly input into the subsequent analysis model, it may cause some parameters to have a disproportionate impact on the overall analysis result.

[0081] To avoid this problem, we use data normalization to map all parameter data to a unified standard range. For example, data such as temperature data, signal strength, and current will be scaled between 0 and 1 to ensure that each data has the same influence on subsequent analysis such as nonlinear dynamics modeling.

[0082] Specifically, data normalization is transformed using the following formula:

[0083] ;

[0084] Wherein: is the original data value, representing the original parameter value collected by the sensor; and respectively represent the possible minimum and maximum values of the parameter in the dataset; is the normalized data, with a range of [0, 1].

[0085] The normalization process ensures that different sensor data can be compared on the same scale and avoids biases caused by different data ranges. In addition, the normalized data can be better input into subsequent non-linear dynamics modeling and fault prediction algorithms, ensuring the accuracy of the results.

[0086] In some embodiments, during the data preprocessing process, data outliers or losses may also occur. These outliers may be caused by reasons such as sensor failures and external environmental interferences. If these abnormal data are not processed in a timely manner, it will affect subsequent analysis and prediction.

[0087] Therefore, in this embodiment, an outlier detection and processing step will be added in 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 these data as abnormal and perform correction or discard.

[0088] For example, if the temperature acquisition value at a certain moment far exceeds the normal range (such as 1000 °C), then this data is considered abnormal and will trigger the alarm system for correction. The correction methods include data interpolation, missing value filling, etc., to ensure the coherence of the data stream.

[0089] 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 scheme provided in this embodiment can ensure the accuracy of subsequent analysis and modeling.

[0090] S2, Non-linear Dynamics Modeling and Fault Prediction: Use non-linear dynamics theory to establish a system model of the optical terminal, calculate the stability of the system through Lyapunov exponents, and further predict the critical points of the system through bifurcation analysis;

[0091] Non-linear Dynamics Modeling and Fault Prediction. The core of this part is to establish an accurate mathematical model to simulate the dynamic behavior of the optical terminal and use non-linear dynamics methods to perform stability analysis and fault prediction on it. Through accurate system modeling in this step, potential faults can be identified in real time, thus effectively avoiding the impact of optical terminal faults on the overall performance of the system.

[0092] In this embodiment, the operating state of the optical terminal is regarded as a complex system jointly affected by multiple non-linear factors. To accurately simulate the working state of the optical terminal, a mathematical model based on non-linear dynamics theory is established. This model uses the state vector to represent the current state of the system. The state vector includes various important parameters of the optical terminal, such as temperature, voltage, current, signal strength, etc. The evolution process of the system can be described by the following non-linear function:

[0093] ;

[0094] where: is the state vector at time , which includes all the parameters to be monitored (such as temperature, voltage, current, etc.); is the non-linear dynamics mapping function, representing the mutual relationship between system states and their evolution over time; is the external control parameter, representing the changes in the system affected by factors such as the environment and operating conditions.

[0095] This non-linear dynamics model takes into account the complex non-linear relationships between variables such as temperature and voltage, and can adapt to various different working environments. When the system is in a constantly changing operating state, the interaction between its parameters may exhibit non-linear characteristics. Therefore, this model can well reflect the dynamic evolution of the optical terminal.

[0096] As an option, in this embodiment, the specific implementation of the model can be based on known physical laws and device characteristics to model the influence of each parameter. For example, temperature may be affected by factors such as the magnitude of current and voltage fluctuations; the relationship between voltage and current may have non-linear characteristics. Through this modeling method, a model reflecting all operating states of the optical terminal system can be obtained.

[0097] In this embodiment, 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 initial conditions. Its calculation formula is:

[0098] ;

[0099] where: is the Lyapunov Exponent, representing the stability of the system; is the state perturbation vector at time ; is the state perturbation vector at the initial time; is the time variable, representing the time step of the optical terminal operation; denotes the natural logarithm function, used to calculate the logarithm value of the perturbation ratio; denotes at time the magnitude or size of the state perturbation, reflecting the distance between the current system state and the initial state; denotes the magnitude of the state perturbation at the initial time 0, measuring the size of the perturbation at the initial time. Similarly, the Euclidean distance is used for calculation.

[0100] Specifically, when the Lyapunov exponent > 0, it indicates that the system is unstable and may enter a chaotic state; when < 0, it indicates that the system tends to a stable state; and when = 0, it indicates that the stability of the system is at the boundary and may fluctuate with time. By calculating the Lyapunov exponent in real time, the system can timely determine whether the optical transceiver has entered an unstable state. Once it detects > 0, the system will trigger a fault warning to remind the operation and maintenance personnel that the system is about to fail.

[0101] In this embodiment, we further enhance the fault prediction ability of the system through bifurcation analysis. Bifurcation analysis is a non - linear dynamics analysis method that can help us identify the critical points and potential fault modes of the system. In the dynamic model of the optical transceiver, some parameters of the system may change under specific conditions, causing the system to change from a stable state to an unstable state.

[0102] Specifically, assuming that the control parameter of the optical transceiver is , by scanning different control parameter values, we can determine the bifurcation points of the system. The basic formula for bifurcation analysis is:

[0103] ;

[0104] where: is the state vector at time ; is the non - linear mapping describing the dynamic evolution of the system; is the control parameter, representing the changes of the optical transceiver system under different external environments or operating conditions.

[0105] By scanning the system states under different parameter values, bifurcation analysis can identify the bifurcation points in the system, that is, near these points the system may change from a stable state to an unstable state. This process can help the optical transceiver predict possible faults in advance and timely adjust the system control strategy.

[0106] As an option, this embodiment adopts a numerical method for bifurcation analysis. Common numerical methods such as the Newton method can quickly and accurately find the bifurcation points 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.

[0107] By combining Lyapunov exponents and bifurcation analysis, the system can monitor the state of the optical terminal in real time and predict potential failure modes. When the system detects that the Lyapunov exponent is greater than 0 or the bifurcation analysis result shows that the system is approaching the bifurcation point, the fault warning mechanism will be automatically activated.

[0108] In some embodiments, the warning mechanism includes various notification methods such as audible and visual alarms, LED displays, and emails. These methods can quickly transmit the fault information to the operation and maintenance personnel to ensure that the system can be processed in a timely manner. Through real-time fault prediction and warning, the operating safety of the optical terminal has been greatly improved, reducing the downtime caused by equipment failures.

[0109] Through techniques such as nonlinear dynamics modeling, Lyapunov exponent calculation, and bifurcation analysis, the system can accurately evaluate the stability of the optical terminal and issue early warnings when the system is approaching a fault or instability. The combination of these technical means not only improves the accuracy of fault prediction but also ensures that the optical terminal can operate stably under various environmental conditions.

[0110] S3. Redundancy coding and convolutional coding: Perform redundancy coding and convolutional coding on the collected monitoring data to ensure that the data is not interfered by noise during transmission and improve the reliability of data transmission;

[0111] Enhance the reliability of data during transmission through redundancy coding and convolutional coding. Since the signals transmitted by the optical terminal may be affected by noise interference or channel quality fluctuations, redundancy coding and convolutional coding play a key role in ensuring the correctness and integrity of data transmission.

[0112] In this embodiment, the redundancy coding and convolutional coding technologies increase the anti-interference ability of the data by adding redundant information, enabling the receiving end to detect and correct errors during transmission. The combined application of the two ensures that the data in the optical terminal monitoring system can maintain a high transmission reliability in an unstable network environment.

[0113] Generally, to ensure the reliability of data transmission, the system performs low-density parity-check (LDPC) redundancy coding on the original monitoring data. LDPC codes are error-correcting codes widely used in modern communication systems. By adding redundant information, the receiving end can detect and correct a certain number of errors when receiving the coded data with noise.

[0114] In this embodiment, the system processes the collected original data vector through an LDPC encoder, where represents the length of the original data. The LDPC encoding maps the data to a longer encoded vector using the generator matrix . The generated encoded data contains redundant information that can help the receiving end detect and correct transmission errors. The specific formula is as follows:

[0115] ;

[0116] where: 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 the data to the encoded space; represents the matrix transpose.

[0117] Specifically, LDPC codes can maintain good error correction capabilities at relatively low signal-to-noise ratios (SNR). Through LDPC redundant encoding, the receiving end can perform error detection and correction after receiving the transmitted data, greatly reducing system operation anomalies caused by data transmission errors.

[0118] As an option, in addition to LDPC redundant encoding, the system also uses convolutional encoding to process the data. Convolutional encoding generates a set of redundant information by performing convolutional operations on the data stream, thereby enhancing the anti-interference ability of the signal. When the channel quality is poor, convolutional encoding can significantly improve the data integrity during transmission.

[0119] In this embodiment, the input data stream is processed by the convolutional encoder to generate the output data stream , and the process is implemented through the convolutional transfer matrix . The formula is as follows:

[0120] ;

[0121] where: is the input data stream, containing real-time monitoring data from the optical terminal; is the output encoded data stream, containing redundant bits; is the transfer matrix of the convolutional encoder, representing the state transfer rule of the data stream.

[0122] In general, convolutional coding adds redundant bits so that even if the received signal is affected by noise or interference, the receiving end can still recover the original information through decoding. The receiving end decodes the encoded signal through the Viterbi decoding algorithm to recover the original monitoring data.

[0123] Specifically, the Viterbi decoding algorithm uses dynamic programming techniques to select the most likely path to recover the data by calculating the cumulative distance of each possible path. The process of Viterbi decoding can be expressed by the following formula:

[0124] ;

[0125] where: is the path distance in the decoding process, representing the cumulative error of each path; is the received encoded signal; is the distance metric between the received signal and the encoded signal, usually using Hamming distance or Euclidean distance; is the hypothesized input data, and the goal of Viterbi decoding is to find the shortest path to recover the original data; is the input data vector, representing the original data sequence to be decoded; G is the state transition matrix of the convolutional encoder, which defines how the input data is converted into encoded data ; is the time step, representing the sequence position of the data during transmission; represents the minimization operation on the input data

[0126] Through Viterbi decoding, the system can recover the original data from the received signal according to the principle of minimizing the path distance. Even in the case of signal loss or damage, the integrity of the data can still be guaranteed.

[0127] As an option, to further enhance the reliability of data transmission, the system adopts an adaptive coding strategy. This strategy dynamically adjusts the coding scheme according to the real-time evaluated channel quality. During the transmission of the optical terminal, the channel quality may change, such as factors like signal strength fluctuations and noise interference. Therefore, adopting the adaptive coding strategy can automatically increase the coding intensity when the channel quality is poor, ensuring high reliability of data transmission.

[0128] ​Specifically, when the channel quality is poor (e.g., low signal-to-noise ratio or high bit error rate), the system automatically switches to a stronger coding scheme. At this time, the system will increase the redundant bits of the LDPC code or adopt a stronger convolutional coding method to enhance the anti-interference ability of the data. Through this adaptive mechanism, the system can maintain a high data transmission quality in different communication environments.

[0129] Through the application of redundant coding and convolutional coding, the reliability of the data transmission process has been significantly enhanced. LDPC coding provides strong error correction ability for the data, ensuring that errors in data transmission can be effectively corrected even under harsh channel conditions. Convolutional coding, on the other hand, improves the anti-noise ability of the signal by adding redundant information and reduces data loss during transmission. The adaptive coding strategy automatically adjusts the coding scheme according to the real-time evaluated channel quality, further enhancing the robustness and reliability of data transmission.

[0130] S4. Optimization of Threshold Adjustment by Optimal Control Algorithm: Based on the optimal control theory, the monitoring thresholds of the optical terminal are optimized in real time through the LQG control algorithm, and the thresholds of various parameters are adjusted according to the real-time data.

[0131] The optimization of threshold adjustment by the optimal control algorithm dynamically adjusts the monitoring thresholds of the system through the optimal control algorithm (LQG control algorithm), thereby improving the responsiveness and stability of the system.

[0132] 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 thresholds. The LQG control algorithm minimizes the cost function through a feedback mechanism on the premise of ensuring the stability of the system, thereby optimizing the monitoring thresholds of the optical terminal. The key to this process is to optimize and adjust the monitoring thresholds in real time so that the system can maintain efficient and stable operation in a changing working environment.

[0133] Generally, the LQG control algorithm is a feedback-based optimal control method. 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 thresholds of the optical terminal by minimizing the cost function, so that the system can adapt to different working states and maintain the best performance.

[0134] Specifically, the state vector of the system includes all the key parameters to be monitored, such as the temperature, voltage, current, etc. of the optical terminal. The LQG control algorithm calculates the optimal control input at each moment to minimize the cost in the objective function. The relationship between the state and control input of the system is expressed by the state equation as:

[0135] ;

[0136] wherein: is the state vector at time and contains various parameters monitored by the optical terminal (such as temperature, voltage, current, etc.); is the state transition matrix, indicating the state change of the system from time to ; is the control input matrix, indicating the influence of the control input on the system state; is the control input, that is, the adjusted monitoring threshold.

[0137] Through these equations, the LQG control algorithm can predict the 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.

[0138] In LQG control, the 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 definition of the cost function is as follows:

[0139] ;

[0140] wherein: is the overall cost function, representing the cost during the entire control process; is the state vector at time ; is the control input at time (i.e., the optimized threshold adjustment amount); is the state weighting matrix, indicating the degree of emphasis on the system state error; is the control input weighting matrix, representing the cost of the threshold adjustment amount; is the length of the control period; : State error cost, used to quantify the deviation between the current state of the system and the desired target state, ensuring that the system operates in the optimal working area; : Control input cost, measuring the magnitude of the control signal and preventing increased energy consumption or system instability caused by excessive control.

[0141] As an option, in practical applications, the matrices and can be adjusted according to the working environment and requirements of the optical terminal. A larger weight will prompt the system to reduce the state error, while a larger weight will cause the system to reduce excessive threshold adjustment and avoid system instability that may be brought about by excessive adjustment.

[0142] 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 unit. The state feedback formula is:

[0143] ;

[0144] Where: is the control input at time , that is, the optimized monitoring threshold; is the state feedback gain matrix, indicating the influence of each monitoring parameter (such as temperature, voltage, etc.) on the control input; is the system state vector at time .

[0145] By adjusting the state feedback gain matrix , the system can adjust the monitoring threshold in real time according to the current operating state of the optical terminal unit, ensuring that the optical terminal unit can maintain the best performance under different operating environments. This process enables the monitoring system of the optical terminal unit to have self - adaptability and can automatically adapt to changes under different working environments.

[0146] Generally, as the system state changes continuously, the monitoring threshold of the optical terminal unit 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 according to the system state to avoid unnecessary fault responses or over - regulation.

[0147] As an option, the LQG control algorithm in this embodiment also considers the influence of historical data. On the basis of real - time feedback, the system can make predictions according to past trends and further optimize the threshold adjustment. By introducing the trend of historical data, the system can take measures in advance to avoid failures or instability caused by sudden fluctuations.

[0148] In a possible implementation, the LQG control algorithm enhances the robustness of the system by adaptively adjusting the gain matrix . As the working environment changes (such as changes in factors like temperature and humidity), the system can calculate the feedback control input in real time according to the new state information, enabling the optical terminal unit to always maintain high efficiency and stability under different working environments.

[0149] By introducing dynamic adjustment and self - adaptation mechanisms, the LQG control algorithm effectively solves the deficiencies of traditional fixed - threshold control strategies and can significantly improve the response speed and stability of the optical terminal unit monitoring system in complex environments.

[0150] By minimizing the cost function, LQG control not only balances the cost of system stability and threshold adjustment, but also can adjust the monitoring parameters during real-time operation through the state feedback mechanism. The adaptive adjustment of the gain matrix enhances the robustness of the system, enabling the optical terminal to maintain optimal performance in a complex and changing environment.

[0151] S5, Fault Warning and Remote Diagnosis: When the monitoring system detects a fault or signs approaching a fault, it notifies the operation and maintenance personnel through the warning mechanism and supports remote diagnosis and maintenance;

[0152] In steps S1 to S4, the monitoring data of the optical terminal has been effectively collected, processed, modeled, and optimally controlled. 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 mechanisms, communication encryption, remote management platforms, and data security to ensure the efficiency, reliability, and security of the transmission process.

[0153] 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 the monitoring data of the optical terminal. Through this mechanism, remote operators can view and adjust system parameters at any time and place, respond to any anomalies in a timely manner, and ensure the stable operation of the optical terminal monitoring system.

[0154] Generally, the stability of data transmission depends on efficient protocols and communication interfaces. To ensure the reliability of data transmission, the system in this embodiment adopts a variety of network protocols and communication interfaces, capable of achieving stable data transmission in a variety of communication environments.

[0155] Specifically, the optical terminal sends data to the remote management platform through a communication module that supports interfaces such as Ethernet, optical fiber, and Wi-Fi. During the transmission process, the data adopts redundant coding and convolutional coding technologies to ensure that even in the case of network jitter, packet loss, or signal attenuation during data transmission, the data can still be recovered completely.

[0156] In a possible implementation, the communication protocol is the TCP / IP protocol, which provides reliability guarantees for network transmission. In the case of packet loss, the system can perform data recovery through the automatic repeat request (ARQ) mechanism to ensure the integrity of the data transmitted by the optical terminal.

[0157] To ensure the security of data during transmission, the system adopts the SSL / TLS encryption protocol during data transmission to ensure that all transmitted data is protected, preventing data from being maliciously intercepted, tampered with, or leaked.

[0158] The data encryption process is as follows:

[0159] ;

[0160] Wherein: is the encrypted data packet, which has been processed by SSL / TLS encryption; represents the process of encryption using the SSL / TLS protocol; is the original data packet, which contains the optical terminal monitoring data.

[0161] As an option, at the receiving end, the decryption process uses the corresponding key to decrypt and recover the original data. This ensures that even if the data packet is attacked during transmission, the system can guarantee the integrity and confidentiality of the data.

[0162] Specifically, the remote management platform is an important part of this system, which enables the operator to monitor the operating parameters of the optical terminal in real time. The platform receives the data transmitted by the optical terminal through the API interface, and technicians can view the health status, real-time data and device fault warning information of the optical terminal through the platform.

[0163] In a possible implementation, the platform provides multiple interaction methods. The operator can choose to view the real-time data, receive alarm information or perform remote control through the graphical interface. The platform supports the following functions:

[0164] Real-time monitoring: The operator can view the current working status of the optical terminal (such as temperature, voltage, current, etc.) and display the historical data through the data chart;

[0165] Fault warning: The platform analyzes the received transmission data and gives a real-time alarm for abnormal situations. The alarm information can be transmitted to the technician in multiple ways such as email, text message, APP notification, etc.;

[0166] Remote adjustment: The operator can directly adjust the working parameters of the optical terminal through the platform, such as threshold setting, working mode switching, etc.;

[0167] Maintenance suggestion: According to the historical data and trend analysis, the platform will also automatically generate regular equipment maintenance reports and fault prediction suggestions.

[0168] Generally, by analyzing the real-time data of the optical terminal equipment through the platform, the health status of the equipment can be effectively identified. In this embodiment, the system not only relies on the real-time data, but also combines the historical data, equipment operation trend and fault mode for fault prediction and diagnosis.

[0169] Specifically, the system uses machine learning algorithms (such as support vector machines, decision trees, etc.) to evaluate the health status of the optical terminal unit. Based on the historical operation data of the device, these algorithms can identify potential failure modes and issue early warnings.

[0170] 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 device also shows abnormal changes, which may be a precursor to device failure. Through failure mode recognition, the system can take measures in advance, such as issuing warnings, suggesting technicians to conduct inspections, or even automatically adjusting working parameters.

[0171] As an option, to ensure high system availability, a dual - machine hot standby and load - balancing mechanism is 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 system availability. The load - balancing mechanism distributes the load to multiple servers through intelligent algorithms, thus avoiding overloading of any single device and ensuring the performance and stability of the system.

[0172] Specifically, the load balancer dynamically selects the most suitable server to process requests according to the current load situation and response time of the servers, avoiding any single 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.

[0173] Redundant coding and convolutional coding techniques ensure the reliability of data transmission, while the SSL / TLS encryption protocol provides 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.

[0174] A kind of optical terminal unit transmission monitoring system described below can be correspondingly referred to the kind of optical terminal unit transmission monitoring method described above.

[0175] Please refer to the attached Figure 2 , the present invention also provides a kind of optical terminal unit transmission monitoring system, including:

[0176] A data acquisition module for real - time collecting parameters such as the temperature, voltage, current, and signal strength of the optical terminal unit;

[0177] A non - linear dynamics analysis module for system modeling based on non - linear dynamics theory and calculating the Lyapunov exponent to predict possible failure modes of the system;

[0178] An encoding module for performing redundant coding and convolutional coding on the collected data to ensure the reliability of data transmission;

[0179] The threshold adjustment module optimizes and adjusts the monitoring threshold of the optical terminal in real time through the LQG control algorithm;

[0180] The early warning and remote diagnosis module is responsible for issuing early warnings when fault signs are detected and supports remote maintenance and diagnosis.

[0181] The system of this embodiment can be used to execute the above method embodiment, and its principle and technical effect are similar, so details are not described here.

[0182] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. 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 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; 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; Identify possible failure modes of the optical transceiver system through bifurcation analysis and determine the critical state of the system; 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; When the channel quality is poor, the system automatically increases the redundant bits of the LDPC code or adopts a stronger convolutional coding method to enhance the data's anti-interference ability; 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.

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, 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.

4. 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 3, 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.

5. The optical transmission monitoring system according to claim 4, 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.

6. The optical transmission monitoring system according to claim 4, 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.

7. The optical transmission monitoring system according to claim 4, 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 encoding module automatically selects a suitable encoding method according to the channel quality parameters monitored in real time, including but not limited to LDPC encoding and convolutional encoding; When the channel quality is poor, the coding module will automatically enable stronger error correction coding to improve the robustness of data transmission; The encoding module dynamically adjusts the encoding 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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