Optical fiber data transmission method and system based on wavelength division multiplexing

Abstract

The invention discloses an optical fiber data transmission method and system based on wavelength division multiplexing, and relates to the technical field of optical fiber data transmission, and the method comprises the steps: constructing standard wavelength distribution and channel interval reference distribution of each wavelength channel in a wavelength division multiplexing optical fiber network; detecting the current wavelength offset and the current optical power parameter of each wavelength channel in real time to obtain a predicted wavelength offset distribution sequence; calculating an expected crosstalk intensity distribution sequence and an expected transmission quality influence distribution sequence between wavelength channels according to the predicted wavelength shift distribution sequence, the standard wavelength distribution and the channel interval reference distribution; and obtaining an optimal optical power parameter of each wavelength channel, and performing crosstalk suppression on the wavelength division multiplexing optical fiber network. The problems of serious crosstalk and unstable transmission quality of a wavelength division multiplexing network in the existing optical fiber data transmission technology are solved.

Description

Technical Field

[0001] This application relates to the field of optical fiber data transmission technology, specifically to an optical fiber data transmission method and system based on wavelength division multiplexing. Background Technology

[0002] With the development of optical fiber communication technology, wavelength division multiplexing (WDM) technology, with its advantage of simultaneously transmitting multiple wavelength signals in a single optical fiber, has been used to improve optical fiber transmission capacity and efficiency. However, current technologies lack effective prediction and real-time adjustment mechanisms for wavelength drift. Traditional methods mostly involve manual intervention and adjustment only after a significant degradation in transmission quality occurs. This reactive approach is not only inefficient but also fails to fundamentally solve the crosstalk problem, making it impossible to guarantee the long-term stable operation of WDM optical fiber networks.

[0003] Meanwhile, due to limitations in data acquisition and imperfections in the models themselves, existing prediction models have low accuracy in predicting wavelength drift, making it difficult to achieve ideal results when optimizing optical power adjustment and failing to effectively suppress crosstalk between channels. Summary of the Invention

[0004] This application provides a wavelength division multiplexing-based optical fiber data transmission method and system, which solves the technical problems of severe crosstalk and unstable transmission quality in existing optical fiber data transmission technologies using wavelength division multiplexing networks.

[0005] The technical solution to the above-mentioned technical problems in this application is as follows: In a first aspect, this application provides a fiber optic data transmission method based on wavelength division multiplexing, the method comprising: Construct the standard wavelength distribution and channel spacing reference distribution for each wavelength channel in a wavelength division multiplexing fiber optic network; Real-time detection of the current wavelength offset and current optical power parameters of each wavelength channel; based on the wavelength drift prediction model, obtain the predicted wavelength offset distribution sequence. Based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution, calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel; Based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, thereby suppressing crosstalk in the wavelength division multiplexing fiber network.

[0006] Secondly, this application provides a wavelength division multiplexing-based optical fiber data transmission system, comprising: The baseline distribution construction module is used to construct the standard wavelength distribution and channel spacing baseline distribution for each wavelength channel in a wavelength division multiplexing fiber optic network. The offset distribution prediction module is used to detect the current wavelength offset and current optical power parameters of each wavelength channel in real time, and obtain the predicted wavelength offset distribution sequence based on the wavelength drift prediction model. The distribution sequence calculation module is used to calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution. The optimal parameter acquisition module is used to optimize optical power adjustment based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, to obtain the optimal optical power parameters for each wavelength channel, and to suppress crosstalk in the wavelength division multiplexing optical fiber network.

[0007] This application provides one or more technical solutions, which have at least the following technical effects or advantages: This application provides a wavelength division multiplexing (WDM)-based optical fiber data transmission method and system. First, it constructs the standard and reference distributions of each wavelength channel in the WDM optical fiber network, providing accurate reference for subsequent analysis and adjustments. Second, by utilizing real-time detection and wavelength drift prediction models, the wavelength shift of each wavelength channel can be predicted in advance, achieving effective prediction of wavelength drift. Third, by calculating the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence, the degree of influence of wavelength shift on inter-channel crosstalk and transmission quality is understood. Finally, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, thereby suppressing crosstalk in the WDM optical fiber network.

[0008] The above technical solutions enable real-time monitoring and dynamic adjustment of wavelength division multiplexing (WDM) fiber optic networks, improve the accuracy of wavelength drift prediction, effectively suppress crosstalk between channels, ensure the long-term stable operation of WDM fiber optic networks, and enhance the quality and efficiency of fiber optic data transmission. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of an optical fiber data transmission method based on wavelength division multiplexing provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a fiber optic data transmission system based on wavelength division multiplexing provided in an embodiment of this application.

[0011] The components represented by each number in the attached diagram are explained below: The module includes a baseline distribution construction module 11, an offset distribution prediction module 12, a distribution sequence calculation module 13, and an optimal parameter acquisition module 14. Detailed Implementation

[0012] This application provides a wavelength division multiplexing-based optical fiber data transmission method and system to address the technical problems of severe crosstalk and unstable transmission quality in existing optical fiber data transmission technologies using wavelength division multiplexing networks.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0014] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0015] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0016] Example 1, as Figure 1 As shown, this application provides a method for optical fiber data transmission based on wavelength division multiplexing, including: S10: Construct the standard wavelength distribution and channel spacing reference distribution for each wavelength channel in the wavelength division multiplexing optical fiber network; In this embodiment, a standard wavelength distribution and a channel spacing reference distribution are constructed through theoretical analysis and actual measurement of the wavelength division multiplexing (WDM) optical fiber network. For the standard wavelength distribution, the center wavelength value of each wavelength channel is determined according to relevant standards and network design requirements, forming a wavelength distribution table. When constructing the channel spacing reference distribution, the physical characteristics of the optical fiber, signal transmission requirements, and possible interference factors are considered to set the spacing distance between adjacent wavelength channels.

[0017] In practice, high-precision spectrometers are used to monitor and analyze wavelengths in the fiber optic network in real time to obtain wavelength data. Based on the obtained data, combined with theoretical calculations and empirical values, the standard wavelength distribution and channel spacing reference distribution are continuously adjusted and optimized to ensure that they accurately reflect the actual operation of the wavelength division multiplexing fiber optic network.

[0018] By constructing standard wavelength distribution and channel spacing benchmark distribution, a reference basis is provided for subsequent wavelength offset detection and optical power adjustment optimization, enabling the system to more accurately determine the state of the wavelength channel, promptly detect wavelength drift problems, and take effective measures to adjust them, thereby improving the transmission quality and stability of wavelength division multiplexing fiber optic networks.

[0019] Specifically, step S10 in the method includes: Obtain channel configuration information for wavelength division multiplexing fiber optic networks, including the standard center wavelength and designed channel spacing for each wavelength channel; During the initialization phase, the wavelength stability range and optical power range of each wavelength channel under ideal operating conditions are measured; Based on the standard center wavelength and wavelength stability range, establish the standard wavelength distribution for each wavelength channel; Based on the designed channel spacing and optical power range, a baseline distribution of channel spacing is established.

[0020] In this embodiment, channel configuration information is first obtained from equipment documentation and network planning schemes of the wavelength division multiplexing fiber optic network to determine the standard center wavelength and designed channel spacing for each wavelength channel. During the initialization phase, optical measurement equipment, such as an optical time domain reflectometer and an optical power meter, is used to measure the wavelength stability range and optical power range of each wavelength channel under ideal operating conditions.

[0021] Secondly, based on the standard center wavelength, a standard wavelength distribution is established. Combined with the measured wavelength stability range, the allowable wavelength fluctuation range for each wavelength channel is determined, forming a standard wavelength distribution for each wavelength channel. This allows for the determination of whether wavelength shifts have occurred during subsequent monitoring.

[0022] Furthermore, when establishing the channel spacing baseline distribution, the design channel spacing is considered, while also taking into account the impact of optical power range on the channel spacing. Different optical powers may lead to varying degrees of mutual interference between signals, thus affecting the actual effectiveness of the channel spacing. Therefore, by comprehensively designing the channel spacing and optical power range, a reasonable spacing distance between adjacent wavelength channels is determined, and a channel spacing baseline distribution is established.

[0023] The standard wavelength distribution and channel spacing reference distribution established through the above steps provide a foundation for the stable operation of wavelength division multiplexing (WDM) fiber optic networks. During subsequent operation, the status of the wavelength channels can be monitored in real time based on the reference distribution, allowing for timely detection of issues such as wavelength drift and abnormal channel spacing, and enabling corresponding adjustment measures to ensure network transmission quality and stability.

[0024] S20: Real-time detection of the current wavelength offset and current optical power parameters of each wavelength channel, and obtaining the predicted wavelength offset distribution sequence based on the wavelength drift prediction model; In this embodiment, wavelength detection devices, such as wavelength meters and spectrum analyzers, are used to acquire the current wavelength values ​​of each wavelength channel in real time. The current wavelength values ​​are compared with the center wavelength in the standard wavelength distribution to calculate the current wavelength offset of each wavelength channel. Simultaneously, devices such as optical power meters are used to measure the current optical power parameters of each wavelength channel in real time.

[0025] Furthermore, the wavelength drift prediction model is built upon historical data and machine learning algorithms. After obtaining the current wavelength offset and current optical power parameters, these are input into the wavelength drift prediction model. The model then uses its internal algorithms and learned patterns to predict the wavelength offset of each wavelength channel over a future period, thereby obtaining a predicted wavelength offset distribution sequence.

[0026] By detecting and predicting wavelength shifts in real time, the changing trends of the wavelength channel can be forecasted, providing a basis for subsequent optical power adjustment and optimization. Before wavelength drift seriously affects transmission quality, appropriate measures can be taken to adjust it, avoiding significant degradation in transmission quality and improving the stability and reliability of the wavelength division multiplexing fiber optic network.

[0027] Specifically, step S20 in the method includes: The current center wavelength of each wavelength channel is monitored in real time by a spectral analyzer, and the offset from the standard center wavelength of each wavelength channel is calculated to obtain the current wavelength offset of each wavelength channel. The current optical power parameters of each wavelength channel are monitored in real time using an optical power meter. Call the pre-trained wavelength drift prediction model; The current wavelength offset and current optical power parameters of each wavelength channel are used as inputs to the wavelength drift prediction model, and the predicted wavelength offset value within a future preset time window is output. The predicted wavelength offset values ​​are organized according to a time series to generate a predicted wavelength offset distribution sequence.

[0028] In this embodiment, firstly, a spectral analyzer is used to monitor the current center wavelength of each wavelength channel in real time. The monitored current center wavelength is compared with the previously established standard center wavelength to calculate the current wavelength offset of each wavelength channel. For example, if the standard center wavelength is λ0 and the currently monitored center wavelength is λ1, then the current wavelength offset Δλ = λ1 - λ0. Simultaneously, an optical power meter is used to monitor the current optical power parameters of each wavelength channel in real time, as optical power is one of the factors affecting signal transmission quality.

[0029] Secondly, a pre-trained wavelength drift prediction model is invoked. This model is built upon historical data and machine learning algorithms. The historical data includes wavelength shifts in various wavelength channels under different environmental conditions, such as temperature, humidity, and pressure, along with corresponding optical power parameters. By analyzing and mining the optical power parameters, the intrinsic relationship between wavelength drift and environmental factors, as well as the optical power parameters, is identified.

[0030] Then, the current wavelength offset and current optical power parameters of each wavelength channel are input into the wavelength drift prediction model. Based on its internal algorithm and learned patterns, the model accurately predicts the wavelength offset of each wavelength channel within a preset time window and outputs the predicted wavelength offset value.

[0031] Finally, the predicted wavelength offset values ​​are organized according to time series to generate a predicted wavelength offset distribution sequence. The predicted wavelength offset distribution sequence shows the wavelength offset trend of each wavelength channel over a period of time, providing a basis for subsequent optical power adjustment and optimization.

[0032] The steps for constructing the wavelength drift prediction model include: Collect historical operational data, including historical wavelength offset data for each wavelength channel, historical optical power parameter data at the corresponding time, and labels indicating wavelength offset at future time. Based on the historical operating data, construct a sample wavelength offset set, a sample optical power parameter set, and a sample predicted wavelength offset value set; Using the sample wavelength offset set and the sample optical power parameter set as input features, and the sample predicted wavelength offset value set as supervision labels, the wavelength drift prediction model is constructed and generated.

[0033] In this embodiment, firstly, historical operation data is collected from the long-term operation records of the wavelength division multiplexing optical fiber network, including historical wavelength offset data of each wavelength channel at different time points, reflecting the change of wavelength over time; historical optical power parameter data at the corresponding time, as fluctuations in optical power may affect the stability of wavelength; and labels that identify the wavelength offset at future time points, which are used for supervised learning during subsequent model training.

[0034] Secondly, a sample set is constructed based on the collected historical operational data. The historical wavelength offset data is organized into a sample wavelength offset set, representing the wavelength offset status of each wavelength channel at different times; the historical optical power parameter data is constructed into a sample optical power parameter set, reflecting the characteristics of optical power variation at different times; and the labels that identify the wavelength offset at future times are used to form a sample predicted wavelength offset value set, which serves as the target output for model training.

[0035] Then, using the sample wavelength offset set and sample optical power parameter set as input features, and the sample predicted wavelength offset value set as supervision labels, a wavelength drift prediction model is constructed and generated using appropriate machine learning algorithms, such as neural network algorithms. During training, the model continuously adjusts its internal parameters to learn the mapping relationship between input features and supervision labels, enabling the model to accurately predict the wavelength offset of each wavelength channel within a preset time window based on the current wavelength offset and optical power parameters.

[0036] For example, a wavelength drift prediction model is built and trained based on a neural network. The specific steps are as follows: First, data preparation involves collecting sample wavelength offset sets, sample optical power parameter sets, and sample predicted wavelength offset value sets based on historical operational data.

[0037] Secondly, the model is constructed using the sample wavelength offset set and the sample optical power parameter set as inputs, including historical wavelength offset data and historical optical power parameter data at corresponding times. The predicted wavelength offset value within a preset future time window is the output. The number of nodes in the input layer equals the dimension of the input features. For example, if there are 6 features in total, such as historical wavelength offset data and historical optical power parameter data at corresponding times, then the input layer contains 6 nodes. One to 3 hidden layers are set, with the number of nodes in each layer adjusted experimentally, such as 64 or 32. The ReLU activation function is used. The number of nodes in the output layer equals the number of predicted wavelength offset values. For example, if the prediction time requires 1 node, the output layer generally does not use an activation function and directly outputs continuous values.

[0038] Then, the model is trained. In each training iteration, the sample predicted wavelength shift value set is used as the supervision data. The Adam optimizer and mean squared error (MSE) loss function are used to build the training framework. The batch size is set to 32 and the total number of training rounds is 50. An early stopping mechanism with patience=5 is introduced. When the validation set loss does not decrease for 5 consecutive rounds, the training process is automatically terminated, and the trained wavelength drift prediction model is obtained.

[0039] By constructing a wavelength drift prediction model, a reliable prediction basis can be provided for the optical power adjustment optimization of wavelength division multiplexing fiber optic networks, thereby further improving the transmission quality and stability of the network.

[0040] S30: Based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution, calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel; In this embodiment, the interaction between wavelength channels is analyzed based on the predicted wavelength offset distribution sequence, combined with the previously established standard wavelength distribution and channel spacing reference distribution. When a wavelength offset occurs, the signals between adjacent wavelength channels may overlap, thus causing crosstalk. By simulating and calculating the offset of each wavelength channel in the predicted wavelength offset distribution sequence, and considering factors such as signal amplitude and phase, the expected crosstalk intensity distribution sequence between each wavelength channel is obtained.

[0041] Meanwhile, crosstalk can affect signal transmission quality, such as causing signal distortion and increased bit error rate. Based on the expected crosstalk intensity distribution sequence, considering the transmission characteristics and signal processing requirements of wavelength division multiplexing fiber optic networks, the degree of crosstalk's impact on transmission quality is assessed, thus obtaining the expected transmission quality impact distribution sequence.

[0042] Specifically, when calculating the expected crosstalk intensity distribution sequence, electromagnetic theory and signal propagation models are used to model the coupling effect between different wavelength channels. Based on the offset of each wavelength channel in the predicted wavelength offset distribution sequence, the degree and range of signal overlap are determined. Combined with parameters such as the signal's power spectral density, the crosstalk intensity between adjacent wavelength channels is calculated. For the case of multiple wavelength channels, the overall expected crosstalk intensity distribution sequence is obtained by superimposing and analyzing the crosstalk contributions between different channels.

[0043] When calculating the expected transmission quality impact distribution sequence, crosstalk introduces noise and interference, affecting the demodulation and decoding processes. Performance evaluation metrics for communication systems, such as bit error rate and signal-to-noise ratio, are used in conjunction with the expected crosstalk intensity distribution sequence to establish a mapping relationship between crosstalk and transmission quality. By simulating and analyzing transmission performance under different crosstalk intensities, the expected transmission quality impact degree for each wavelength channel is determined, thereby generating the expected transmission quality impact distribution sequence.

[0044] Specifically, step S30 in the method includes: Activate the offset impact analyzer, which includes a crosstalk intensity prediction branch and a transmission quality prediction branch; The predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution are input into the offset impact analyzer. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the channel spacing reference distribution into the crosstalk intensity prediction branch, and outputs the expected crosstalk intensity distribution sequence between each wavelength channel. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the standard wavelength distribution into the transmission quality prediction branch and outputs the expected transmission quality impact distribution sequence.

[0045] In this embodiment of the application, firstly, the offset impact analyzer is activated. The offset impact analyzer consists of a crosstalk intensity prediction branch and a transmission quality prediction branch, with each branch responsible for different aspects of the analysis.

[0046] Secondly, the predicted wavelength offset distribution sequence, standard wavelength distribution, and channel spacing reference distribution are input into the offset impact analyzer. The above data includes the current state, ideal state, and channel spacing information of the wavelength channel.

[0047] For the crosstalk intensity prediction branch, the predicted wavelength offset distribution sequence and the channel spacing baseline distribution are received as inputs. Based on the input data, the crosstalk intensity prediction branch simulates the coupling effect between different wavelength channels using electromagnetic theory and signal propagation models. By analyzing the offset of each wavelength channel in the predicted wavelength offset distribution sequence, the degree and range of signal overlap are determined. Simultaneously, considering parameters such as the signal's power spectral density, the crosstalk intensity between adjacent wavelength channels is calculated. For multiple wavelength channels, this branch superimposes and analyzes the crosstalk contributions between different channels, ultimately outputting the expected crosstalk intensity distribution sequence between each wavelength channel.

[0048] The transmission quality prediction branch receives the predicted wavelength offset distribution sequence and the standard wavelength distribution as input. It considers that crosstalk introduces noise and interference, affecting the demodulation and decoding processes. Using performance evaluation metrics of the communication system, such as bit error rate and signal-to-noise ratio, combined with the expected crosstalk intensity distribution sequence, a mapping relationship between crosstalk and transmission quality is established. By simulating and analyzing transmission performance under different crosstalk intensities, this branch can determine the expected transmission quality impact on each wavelength channel, and thus output the expected transmission quality impact distribution sequence.

[0049] In summary, the offset impact analyzer can analyze and predict the expected crosstalk intensity and expected transmission quality impact between different wavelength channels, providing a reference for subsequent adjustments and optimizations of wavelength division multiplexing fiber optic networks.

[0050] Furthermore, the steps for constructing the offset impact analyzer include: Collect historical wavelength offset impact data, including historical wavelength offset data, corresponding crosstalk intensity measurements, and transmission quality degradation values; Based on the historical wavelength offset data, the corresponding crosstalk intensity measurement value, and the transmission quality degradation value in the historical wavelength offset impact data, a sample wavelength offset set, a sample crosstalk intensity set, and a sample transmission quality set are constructed. Using the sample wavelength offset set as input features and the sample crosstalk intensity set as supervision labels, the crosstalk intensity prediction branch is constructed and generated. Using the sample wavelength offset set as input features and the sample transmission quality set as supervision labels, the transmission quality prediction branch is constructed and generated. The crosstalk intensity prediction branch and the transmission quality prediction branch are integrated into the offset impact analyzer.

[0051] In this embodiment, a method similar to that used for training wavelength drift prediction models is employed to build and train a drift impact analyzer.

[0052] First, historical wavelength offset impact data are collected from the historical operation records of the wavelength division multiplexing fiber optic network, including historical wavelength offset data for each wavelength channel; corresponding crosstalk intensity measurements, reflecting the actual intensity of crosstalk between adjacent channels when the wavelength offset occurs; and transmission quality degradation values, indicating the degree of impact of crosstalk on transmission quality.

[0053] Secondly, a sample set is constructed based on the collected historical wavelength offset impact data. The historical wavelength offset data is organized into a sample wavelength offset set, representing the wavelength offset status of each wavelength channel at different times; the corresponding crosstalk intensity measurements are constructed into a sample crosstalk intensity set, reflecting the variation characteristics of crosstalk intensity with wavelength offset; and the transmission quality degradation values ​​are used to form a sample transmission quality set, which serves as the target output for subsequent model training.

[0054] Subsequently, using the sample wavelength offset set as input features and the sample crosstalk intensity set and sample transmission quality set as supervision labels, respectively, crosstalk intensity prediction branches and transmission quality prediction branches are constructed. For the crosstalk intensity prediction branch, a machine learning algorithm, such as a neural network algorithm, is used for training, with the sample wavelength offset set as input and the sample crosstalk intensity set as supervision labels. During training, the internal parameters are continuously adjusted to learn the mapping relationship between the input features and the supervision labels, enabling the model to accurately predict crosstalk intensity based on wavelength offset. For the transmission quality prediction branch, a neural network algorithm is also used for training, with the sample wavelength offset set as input and the sample transmission quality set as supervision labels, allowing the model to predict the degree of impact on transmission quality based on wavelength offset.

[0055] Finally, the trained crosstalk intensity prediction branch and transmission quality prediction branch are integrated into an offset impact analyzer. The integrated offset impact analyzer can take into account the impact of wavelength offset on crosstalk intensity and transmission quality, providing a more comprehensive reference for the adjustment and optimization of wavelength division multiplexing fiber optic networks.

[0056] Specifically, based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, including: Based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, thereby suppressing crosstalk in the wavelength division multiplexing fiber network.

[0057] Set crosstalk suppression control targets, including the maximum allowable crosstalk intensity threshold between each wavelength channel and the minimum transmission quality requirements for each wavelength channel; Determine whether the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence meet the crosstalk suppression control objective; If the crosstalk suppression control objective is not met, multiple sets of optical power adjustment candidate schemes are generated in the optical power parameter adjustment space. The multiple sets of optical power adjustment candidate schemes were simulated and verified, and the optimization direction was determined based on the simulation and verification results. Based on the optimization direction adjustment, multiple sets of updated optical power adjustment candidate schemes are generated. The simulation verification and optimization direction determination process are repeated and iterated until the convergence condition is met, and the optimal optical power parameters of each wavelength channel are output. If the crosstalk suppression control objective is met, the current optical power parameters of each wavelength channel are maintained.

[0058] In this embodiment, firstly, optical power adjustment and optimization are carried out based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels. The core objective is to obtain the optimal optical power parameters for each wavelength channel, thereby effectively suppressing crosstalk in the wavelength division multiplexing fiber optic network. Setting crosstalk suppression control objectives is the foundation of the optimization process. The maximum allowable crosstalk intensity threshold between each wavelength channel clarifies the upper limit of crosstalk intensity, while the minimum transmission quality requirement for each wavelength channel ensures the basic quality of signal transmission.

[0059] Secondly, after setting the control objectives, the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence are evaluated to see if they meet the crosstalk suppression control objectives. If not, multiple candidate optical power adjustment schemes are generated in the optical power parameter adjustment space. The optical power parameter adjustment space is determined based on the actual situation of the wavelength division multiplexing fiber optic network and the adjustable range of optical power parameters. When generating candidate schemes, the characteristics of different wavelength channels and the adjustment range of optical power are considered.

[0060] Next, after generating multiple candidate optical power adjustment schemes, simulation verification was performed. By establishing a model of the wavelength division multiplexing fiber optic network, the network operation under different optical power adjustment schemes was simulated, thereby obtaining the corresponding crosstalk intensity and transmission quality data. Based on the simulation verification results, the optimization direction was determined, that is, in which direction the optical power parameters should be adjusted to better meet the crosstalk suppression control objective.

[0061] Furthermore, based on the determined optimization direction, multiple sets of candidate schemes for updating optical power adjustment are generated, and then the simulation verification and optimization direction determination process is repeated. The iterative execution process continues until a convergence condition is met. The convergence condition can be that the crosstalk strength and transmission quality have reached preset accuracy requirements, or that the number of iterations has reached an upper limit, for example, 100 iterations. When the convergence condition is met, the optimal optical power parameters for each wavelength channel are output.

[0062] If the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence meet the crosstalk suppression control objective, then the current optical power parameters of each wavelength channel are maintained. Since the current optical power parameters already meet the network's crosstalk suppression and transmission quality requirements, no additional adjustments are needed. Through the optical power adjustment optimization process, the transmission quality and stability of wavelength division multiplexing fiber optic networks can be effectively improved, and the impact of crosstalk on signal transmission can be reduced.

[0063] In summary, compared to existing technologies, this application adopts the technical concept of predictive control, and by constructing a complete "monitoring-prediction-analysis-control" technology chain, it achieves proactive prevention and control of crosstalk problems caused by wavelength drift in wavelength division multiplexing systems. Unlike traditional passive response control, this scheme can preventively intervene before crosstalk problems worsen, fundamentally ensuring the transmission quality of the system.

[0064] In summary, the embodiments of this application have at least the following technical effects: This application provides a wavelength division multiplexing (WDM)-based optical fiber data transmission method. First, it constructs standard and baseline distributions for each wavelength channel in the WDM optical fiber network, providing accurate reference for subsequent analysis and adjustments. Second, by utilizing real-time detection and wavelength drift prediction models, the wavelength shift of each wavelength channel can be predicted in advance, achieving effective prediction of wavelength drift. Third, by calculating the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence, the degree of influence of wavelength shift on inter-channel crosstalk and transmission quality is understood. Finally, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, thereby suppressing crosstalk in the WDM optical fiber network. Through the above technical solution, real-time monitoring and dynamic adjustment of the WDM optical fiber network are achieved, improving the accuracy of wavelength drift prediction, effectively suppressing inter-channel crosstalk, ensuring the long-term stable operation of the WDM optical fiber network, and improving the quality and efficiency of optical fiber data transmission.

[0065] Example 2, as Figure 2 As shown, based on the same inventive concept as the wavelength division multiplexing-based optical fiber data transmission method provided in Embodiment 1, this application also provides a wavelength division multiplexing-based optical fiber data transmission system, including: The reference distribution construction module 11 is used to construct the standard wavelength distribution and channel spacing reference distribution of each wavelength channel in the wavelength division multiplexing optical fiber network. The offset distribution prediction module 12 is used to detect the current wavelength offset and current optical power parameters of each wavelength channel in real time, and obtain the predicted wavelength offset distribution sequence based on the wavelength drift prediction model. The distribution sequence calculation module 13 is used to calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution. The optimal parameter acquisition module 14 is used to perform optical power adjustment optimization based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, to obtain the optimal optical power parameters for each wavelength channel, and to suppress crosstalk in the wavelength division multiplexing optical fiber network.

[0066] In one embodiment, the benchmark distribution construction module 11 is specifically used for: Obtain channel configuration information for wavelength division multiplexing fiber optic networks, including the standard center wavelength and designed channel spacing for each wavelength channel; During the initialization phase, the wavelength stability range and optical power range of each wavelength channel under ideal operating conditions are measured; Based on the standard center wavelength and wavelength stability range, establish the standard wavelength distribution for each wavelength channel; Based on the designed channel spacing and optical power range, a baseline distribution of channel spacing is established.

[0067] In one embodiment, the offset distribution prediction module 12 is specifically used for: The current center wavelength of each wavelength channel is monitored in real time by a spectral analyzer, and the offset from the standard center wavelength of each wavelength channel is calculated to obtain the current wavelength offset of each wavelength channel. The current optical power parameters of each wavelength channel are monitored in real time using an optical power meter. Call the pre-trained wavelength drift prediction model; The current wavelength offset and current optical power parameters of each wavelength channel are used as inputs to the wavelength drift prediction model, and the predicted wavelength offset value within a future preset time window is output. The predicted wavelength offset values ​​are organized according to a time series to generate a predicted wavelength offset distribution sequence.

[0068] Furthermore, in one embodiment of the application, the steps for constructing the wavelength drift prediction model include: Collect historical operational data, including historical wavelength offset data for each wavelength channel, historical optical power parameter data at the corresponding time, and labels indicating wavelength offset at future time. Based on the historical operating data, construct a sample wavelength offset set, a sample optical power parameter set, and a sample predicted wavelength offset value set; Using the sample wavelength offset set and the sample optical power parameter set as input features, and the sample predicted wavelength offset value set as supervision labels, the wavelength drift prediction model is constructed and generated.

[0069] In one embodiment, the distribution sequence calculation module 13 is specifically used for: Activate the offset impact analyzer, which includes a crosstalk intensity prediction branch and a transmission quality prediction branch; The predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution are input into the offset impact analyzer. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the channel spacing reference distribution into the crosstalk intensity prediction branch, and outputs the expected crosstalk intensity distribution sequence between each wavelength channel. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the standard wavelength distribution into the transmission quality prediction branch and outputs the expected transmission quality impact distribution sequence.

[0070] Furthermore, in one embodiment of the application, the construction steps of the offset influence analyzer include: Collect historical wavelength offset impact data, including historical wavelength offset data, corresponding crosstalk intensity measurements, and transmission quality degradation values; Based on the historical wavelength offset data, the corresponding crosstalk intensity measurement value, and the transmission quality degradation value in the historical wavelength offset impact data, a sample wavelength offset set, a sample crosstalk intensity set, and a sample transmission quality set are constructed. Using the sample wavelength offset set as input features and the sample crosstalk intensity set as supervision labels, the crosstalk intensity prediction branch is constructed and generated. Using the sample wavelength offset set as input features and the sample transmission quality set as supervision labels, the transmission quality prediction branch is constructed and generated. The crosstalk intensity prediction branch and the transmission quality prediction branch are integrated into the offset impact analyzer.

[0071] Further, in one embodiment, optical power adjustment optimization is performed based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels to obtain the optimal optical power parameters for each wavelength channel, including: Set crosstalk suppression control targets, including the maximum allowable crosstalk intensity threshold between each wavelength channel and the minimum transmission quality requirements for each wavelength channel; Determine whether the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence meet the crosstalk suppression control objective; If the crosstalk suppression control objective is not met, multiple sets of optical power adjustment candidate schemes are generated in the optical power parameter adjustment space. The multiple sets of optical power adjustment candidate schemes were simulated and verified, and the optimization direction was determined based on the simulation and verification results. Based on the optimization direction adjustment, multiple sets of updated optical power adjustment candidate schemes are generated. The simulation verification and optimization direction determination process are repeated and iterated until the convergence condition is met, and the optimal optical power parameters of each wavelength channel are output. If the crosstalk suppression control objective is met, the current optical power parameters of each wavelength channel are maintained.

[0072] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0073] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0074] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A fiber optic data transmission method based on wavelength division multiplexing, characterized in that, The method includes: Construct the standard wavelength distribution and channel spacing reference distribution for each wavelength channel in a wavelength division multiplexing fiber optic network; Real-time detection of the current wavelength offset and current optical power parameters of each wavelength channel; based on the wavelength drift prediction model, obtain the predicted wavelength offset distribution sequence. Based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution, calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel; Based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, thereby suppressing crosstalk in the wavelength division multiplexing fiber network.

2. The method according to claim 1, characterized in that, Constructing the standard wavelength distribution and channel spacing benchmark distribution for each wavelength channel in a wavelength division multiplexing fiber optic network includes: Obtain channel configuration information for wavelength division multiplexing fiber optic networks, including the standard center wavelength and designed channel spacing for each wavelength channel; During the initialization phase, the wavelength stability range and optical power range of each wavelength channel under ideal operating conditions are measured; Based on the standard center wavelength and wavelength stability range, establish the standard wavelength distribution for each wavelength channel; Based on the designed channel spacing and optical power range, a baseline distribution of channel spacing is established.

3. The method according to claim 1, characterized in that, Real-time detection of the current wavelength offset and optical power parameters of each wavelength channel; based on the wavelength drift prediction model, obtaining the predicted wavelength offset distribution sequence, including: The current center wavelength of each wavelength channel is monitored in real time by a spectral analyzer, and the offset from the standard center wavelength of each wavelength channel is calculated to obtain the current wavelength offset of each wavelength channel. The current optical power parameters of each wavelength channel are monitored in real time using an optical power meter. Call the pre-trained wavelength drift prediction model; The current wavelength offset and current optical power parameters of each wavelength channel are used as inputs to the wavelength drift prediction model, and the predicted wavelength offset value within a future preset time window is output. The predicted wavelength offset values ​​are organized according to a time series to generate a predicted wavelength offset distribution sequence.

4. The method according to claim 3, characterized in that, The steps for constructing the wavelength drift prediction model include: Collect historical operational data, including historical wavelength offset data for each wavelength channel, historical optical power parameter data at the corresponding time, and labels indicating wavelength offset at future time. Based on the historical operating data, construct a sample wavelength offset set, a sample optical power parameter set, and a sample predicted wavelength offset value set; Using the sample wavelength offset set and the sample optical power parameter set as input features, and the sample predicted wavelength offset value set as supervision labels, the wavelength drift prediction model is constructed and generated.

5. The method according to claim 1, characterized in that, Based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution, calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel, including: Activate the offset impact analyzer, which includes a crosstalk intensity prediction branch and a transmission quality prediction branch; The predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution are input into the offset impact analyzer. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the channel spacing reference distribution into the crosstalk intensity prediction branch, and outputs the expected crosstalk intensity distribution sequence between each wavelength channel. The offset impact analyzer inputs the predicted wavelength offset distribution sequence and the standard wavelength distribution into the transmission quality prediction branch and outputs the expected transmission quality impact distribution sequence.

6. The method according to claim 5, characterized in that, The steps for constructing the offset influence analyzer include: Collect historical wavelength offset impact data, including historical wavelength offset data, corresponding crosstalk intensity measurements, and transmission quality degradation values; Based on the historical wavelength offset data, the corresponding crosstalk intensity measurement value, and the transmission quality degradation value in the historical wavelength offset impact data, a sample wavelength offset set, a sample crosstalk intensity set, and a sample transmission quality set are constructed. Using the sample wavelength offset set as input features and the sample crosstalk intensity set as supervision labels, the crosstalk intensity prediction branch is constructed and generated. Using the sample wavelength offset set as input features and the sample transmission quality set as supervision labels, the transmission quality prediction branch is constructed and generated. The crosstalk intensity prediction branch and the transmission quality prediction branch are integrated into the offset impact analyzer.

7. The method according to claim 1, characterized in that, Based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, optical power adjustment and optimization are performed to obtain the optimal optical power parameters for each wavelength channel, including: Set crosstalk suppression control targets, including the maximum allowable crosstalk intensity threshold between each wavelength channel and the minimum transmission quality requirements for each wavelength channel; Determine whether the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence meet the crosstalk suppression control objective; If the crosstalk suppression control objective is not met, multiple sets of optical power adjustment candidate schemes are generated in the optical power parameter adjustment space. The multiple sets of optical power adjustment candidate schemes were simulated and verified, and the optimization direction was determined based on the simulation and verification results. Based on the optimization direction adjustment, multiple sets of updated optical power adjustment candidate schemes are generated. The simulation verification and optimization direction determination process are repeated and iterated until the convergence condition is met, and the optimal optical power parameters of each wavelength channel are output. If the crosstalk suppression control objective is met, the current optical power parameters of each wavelength channel are maintained.

8. A fiber optic data transmission system based on wavelength division multiplexing, characterized in that, For performing the method according to any one of claims 1-7, comprising: The baseline distribution construction module is used to construct the standard wavelength distribution and channel spacing baseline distribution for each wavelength channel in a wavelength division multiplexing fiber optic network. The offset distribution prediction module is used to detect the current wavelength offset and current optical power parameters of each wavelength channel in real time, and obtain the predicted wavelength offset distribution sequence based on the wavelength drift prediction model. The distribution sequence calculation module is used to calculate the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between each wavelength channel based on the predicted wavelength offset distribution sequence, the standard wavelength distribution, and the channel spacing reference distribution. The optimal parameter acquisition module is used to optimize optical power adjustment based on the expected crosstalk intensity distribution sequence and the expected transmission quality impact distribution sequence between channels, to obtain the optimal optical power parameters for each wavelength channel, and to suppress crosstalk in the wavelength division multiplexing optical fiber network.

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