Optical signal wavelength optimization method and device, storage medium and processor

Abstract

The invention discloses an optical signal wavelength optimization method and device, a storage medium and a processor. In the scheme, wavelength distortion characteristics in original optical signal data are extracted; performing deep analysis on target distortion distribution data constructed according to the distortion category of the wavelength distortion characteristics to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determining mapping relation data according to the parameters and parameters of an optical communication system; generating a wavelength correction coefficient corresponding to the optical signal based on the mapping relation data, and adjusting the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristic of the optical fiber material to obtain a target wavelength parameter; simulating a distortion change degree of the optical signal in an optical communication system transmission environment based on the target wavelength parameter; and adjusting the target wavelength parameter according to the distortion change degree. The problem that wavelength distortion cannot be effectively suppressed due to the fact that the prior art is difficult to adapt to a dynamically changing transmission environment is solved, and the signal transmission quality of an optical communication system is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a method, device, storage medium, and processor for optimizing optical signal wavelength. Background Art

[0002] In the field of optical communications, wavelength distortion of optical signals is a complex and critical technical challenge that directly impacts the transmission quality and performance of optical communication systems. As optical signal transmission distances increase and data rates rise, the impact of dispersion effects (such as material dispersion and waveguide dispersion) and nonlinear effects (such as four-wave mixing and stimulated Raman scattering) in optical fibers on optical signals increases significantly, leading to increased wavelength distortion. This wavelength distortion not only degrades optical signal quality but also increases the complexity of optical communication systems, limiting increases in transmission capacity.

[0003] Existing technologies typically use static compensation methods, such as fixed dispersion compensation modules or pre-compensation techniques, to suppress wavelength distortion. However, these methods are difficult to adapt to dynamic transmission environments, such as temperature fluctuations and fiber link topology adjustments, and therefore cannot effectively suppress wavelength distortion.

[0004] Based on this, an adaptive wavelength optimization method is urgently needed to effectively suppress the increasingly severe wavelength distortion problem in high-speed, large-capacity optical communication systems and ensure the signal transmission quality and system performance of optical communication systems. Summary of the Invention

[0005] Based on the above problems, the present application provides a method, device, storage medium and processor for optimizing the wavelength of an optical signal, with the aim of solving the technical problem in the prior art that wavelength distortion cannot be effectively suppressed due to the difficulty in adapting to the dynamically changing transmission environment, thereby ensuring the signal transmission quality and system performance of the optical communication system.

[0006] The embodiments of this application disclose the following technical solutions: In a first aspect, the present application provides a method for optimizing optical signal wavelength, the method comprising: Collecting original optical signal data from an optical communication system; the original optical signal data includes initial wavelength parameters of the optical signal; Extracting wavelength distortion features from the original optical signal data, and constructing target distortion distribution data based on the distortion categories of the wavelength distortion features; the distortion categories of the wavelength distortion features include adjacent channel interference distortion and group velocity dispersion distortion, and the target distortion distribution data is used to characterize the distribution of wavelength distortion in a time dimension or a space dimension; A pre-trained convolutional neural network model is used to perform an in-depth analysis of the target distortion distribution data to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and mapping relationship data is determined based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and the parameters of the optical communication system; the mapping relationship data is used to characterize the mapping relationship between the nonlinear crosstalk characteristic parameters and the polarization mode dispersion characteristic parameters and the complexity of the optical communication system; The pre-trained generative adversarial network model generates a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter; Based on the target wavelength parameter, simulating the degree of distortion change of the optical signal in the transmission environment of the optical communication system; the degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission; The target wavelength parameter is adjusted according to the degree of distortion change to optimize the wavelength of the optical signal.

[0007] In an optional implementation, adjusting the target wavelength parameter according to the degree of distortion change to optimize the wavelength of the optical signal includes: If the degree of distortion change is greater than or equal to a preset threshold, acquiring wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal; adjusting the target wavelength parameter based on the wavelength drift interference data and the temperature-dependent dispersion data corresponding to the optical signal to obtain an adjusted target wavelength parameter; Performing a polarization mode crosstalk analysis on the adjusted target wavelength parameter to obtain a first signal compensation coefficient, and performing a high-order dispersion effect analysis on the adjusted target wavelength parameter to obtain a second signal compensation coefficient; The wavelength of the optical signal is optimized according to the first signal compensation coefficient and the second signal compensation coefficient.

[0008] In an optional implementation, extracting the wavelength distortion feature from the original optical signal data and constructing target distortion distribution data according to the distortion category of the wavelength distortion feature includes: extracting power spectral density data from the raw optical signal data; Based on the power spectrum density data, calculating the error between the actual power value and the ideal power value at each wavelength point of the optical signal to obtain the wavelength distortion characteristic; performing classification processing on the wavelength distortion feature to classify the wavelength distortion feature into adjacent channel interference distortion and group velocity dispersion distortion; Performing adjacent channel interference analysis on the wavelength distortion characteristics to obtain first distortion distribution data; performing group velocity dispersion analysis on the wavelength distortion characteristics to obtain second distortion distribution data; The target distortion distribution data is constructed based on the first distortion distribution data and the second distortion distribution data.

[0009] In an optional implementation, the pre-trained convolutional neural network model performs an in-depth analysis on the target distortion distribution data to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determines the mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and the parameters of the optical communication system, including: Inputting the target distortion distribution data into the convolutional neural network model, and performing classification processing on the target distortion distribution data by the convolutional neural network model to classify the target distortion distribution data into nonlinear crosstalk distortion and polarization mode dispersion distortion; Performing nonlinear crosstalk analysis on the target distortion distribution data using the convolutional neural network model to obtain the nonlinear crosstalk characteristic parameters; Performing polarization mode dispersion analysis on the target distortion distribution data using the convolutional neural network model to obtain the polarization mode dispersion characteristic parameters; generating a complexity index of the optical communication system based on the nonlinear crosstalk characteristic parameter, the polarization mode dispersion characteristic parameter, and the parameters of the optical communication system through a regression analysis model; The mapping relationship data is generated based on the nonlinear crosstalk characteristic parameter, the polarization mode dispersion characteristic parameter and the corresponding complexity index.

[0010] In an optional implementation, the pre-trained generative adversarial network model generates a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameter, including: Inputting the mapping relationship data into the generative adversarial network model, and having the generative adversarial network model match the corresponding wavelength correction coefficient for the optical signal based on the mapping relationship data; Correcting the initial wavelength parameter based on the wavelength correction coefficient to obtain a corrected wavelength parameter; Performing a dispersion analysis on the corrected wavelength parameter based on the dispersion characteristics of the optical fiber material to obtain a dispersion result; the dispersion result includes a dispersion value and a dispersion slope; Calculating a wavelength shift based on the dispersion value and the dispersion slope; The corrected wavelength parameter is adjusted based on the wavelength offset to obtain the target wavelength parameter.

[0011] In an optional implementation, simulating the degree of distortion change of the optical signal in the transmission environment of the optical communication system based on the target wavelength parameter includes: Based on the target wavelength parameter, performing an optical power leakage simulation analysis on the optical signal in the optical communication system transmission environment to obtain an optical power leakage value corresponding to the optical signal; If the optical power leakage value is greater than a preset leakage value, calculating a first compensation gain value based on the target wavelength parameter using a power compensation model; The distortion change degree is determined according to the first compensation gain value.

[0012] In an optional implementation, determining the degree of distortion change according to the first compensation gain value includes: Adjusting the target wavelength parameter based on the first compensation gain value to obtain a compensated target wavelength parameter; performing dispersion compensation error analysis on the optical signal in the optical communication system transmission environment based on the compensated target wavelength parameter to obtain a compensation error corresponding to the optical signal; generating a parameter correction coefficient based on the compensation error, and correcting the compensated target wavelength parameter based on the parameter correction coefficient to obtain a corrected target wavelength parameter; The distortion change degree is determined based on the corrected target wavelength parameter and the initial wavelength parameter.

[0013] In a second aspect of the present application, a device for optimizing optical signal wavelength is provided, the device comprising: A data acquisition module is used to collect original optical signal data from the optical communication system; the original optical signal data includes initial wavelength parameters of the optical signal; a feature extraction module for extracting wavelength distortion features from the original optical signal data and constructing target distortion distribution data based on the distortion categories of the wavelength distortion features; the distortion categories of the wavelength distortion features include adjacent channel interference distortion and group velocity dispersion distortion, and the target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or the space dimension; An analysis module is configured to perform an in-depth analysis of the target distortion distribution data using a pre-trained convolutional neural network model to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determine mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and parameters of the optical communication system; the mapping relationship data is used to characterize a mapping relationship between the nonlinear crosstalk characteristic parameters and the polarization mode dispersion characteristic parameters and the complexity of the optical communication system; An adjustment module is configured to generate a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data by a pre-trained generative adversarial network model, and adjust the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter; a simulation module, configured to simulate, based on the target wavelength parameter, a degree of distortion change of the optical signal in the transmission environment of the optical communication system; the degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission; An optimization module is used to adjust the target wavelength parameter according to the degree of distortion change to optimize the wavelength of the optical signal.

[0014] In a third aspect of the present application, a computer-readable storage medium is provided, in which a computer program is stored. When the computer program is executed by a processor, the above-mentioned optical signal wavelength optimization method is implemented.

[0015] In a fourth aspect of the present application, a processor is provided for running a computer program, and when the computer program is running, the above-mentioned optical signal wavelength optimization method is executed.

[0016] Compared with the existing technology, this application has the following beneficial effects: In the technical solution of the present application, by collecting raw optical signal data from the optical communication system in real time, the initial wavelength parameters of the optical signal can be fully obtained, providing accurate basic data support for subsequent wavelength analysis and compensation, and ensuring the optical communication system's dynamic perception of the optical signal state; then, the wavelength distortion characteristics in the raw optical signal data are extracted, and target distortion distribution data is constructed based on the distortion categories of the wavelength distortion characteristics. Since the distortion categories of the wavelength distortion characteristics include adjacent channel interference distortion and group velocity dispersion distortion, the target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or space dimension. Therefore, by classifying and extracting adjacent channel interference distortion and group velocity dispersion distortion, different types of distortion sources can be accurately identified. Combined with the distribution characteristics of the time or space dimension, the dynamic evolution law of wavelength distortion can be more comprehensively characterized, providing a basis for subsequent targeted compensation; Then, the pre-trained convolutional neural network model performs an in-depth analysis of the target distortion distribution data, which can quickly identify complex interference in optical signal transmission (such as mutual interference between signals and irregular changes during light wave propagation) to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters. Based on the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters combined with the parameters of the optical communication system, the mapping relationship data used to characterize the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters and the complexity of the optical communication system is determined. It can accurately identify the correlation between the above-mentioned interference problems and system settings, and provide a data basis for the subsequent precise correction of the above-mentioned interference; then, the pre-trained generative adversarial network model generates the wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adaptively adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameter, realizing dynamic response to changes in the transmission environment, significantly improving the flexibility and real-time performance of the compensation strategy, and effectively dealing with signal distortion problems in complex scenarios; Then, based on the target wavelength parameters, the degree of distortion change of the optical signal in the transmission environment of the optical communication system is simulated. Since the degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission, the target wavelength parameters are adjusted according to the degree of distortion change. This can quantitatively evaluate the compensation effect and further optimize the wavelength parameters to continuously improve the transmission quality of the optical signal, thereby solving the technical problem in the existing technology that is difficult to adapt to the dynamically changing transmission environment, resulting in the inability to effectively suppress wavelength distortion, and ensuring the signal transmission quality and system performance of the optical communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 A flowchart of a method for optimizing optical signal wavelength provided in an embodiment of the present application; Figure 2 A flowchart of a process for constructing target distortion distribution data provided in an embodiment of the present application; Figure 3 A flowchart of a process for constructing mapping relationship data provided in an embodiment of the present application; Figure 4 A flowchart of a wavelength parameter adjustment process provided in an embodiment of the present application; Figure 5 A schematic diagram of the structure of an optical signal wavelength optimization device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] As previously described, existing technologies typically employ static compensation methods (such as fixed dispersion compensation modules or pre-compensation techniques) to mitigate wavelength distortion. However, these methods struggle to adapt to dynamically changing transmission environments (such as temperature fluctuations and fiber link topology adjustments), rendering them ineffective in suppressing wavelength distortion. Consequently, an adaptive wavelength optimization method is urgently needed to effectively mitigate the increasingly severe wavelength distortion problem in high-speed, high-capacity optical communication systems, thereby ensuring signal transmission quality and system performance.

[0020] After research, the inventors proposed a method for optimizing the wavelength of optical signals. In this scheme, the original optical signal data is first collected in real time from the optical communication system, which can fully obtain the initial wavelength parameters of the optical signal, provide accurate basic data support for subsequent wavelength analysis and compensation, and ensure the optical communication system's dynamic perception of the optical signal status; then, the wavelength distortion characteristics in the original optical signal data are extracted, and target distortion distribution data is constructed based on the distortion categories of the wavelength distortion characteristics. Since the distortion categories of the wavelength distortion characteristics include adjacent channel interference distortion and group velocity dispersion distortion, the target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or space dimension. Therefore, by classifying and extracting adjacent channel interference distortion and group velocity dispersion distortion, different types of distortion sources can be accurately identified. Combined with the distribution characteristics of the time or space dimension, the dynamic evolution law of wavelength distortion can be more comprehensively characterized, providing a basis for subsequent targeted compensation; Then, the pre-trained convolutional neural network model performs an in-depth analysis of the target distortion distribution data, which can quickly identify complex interference in optical signal transmission (such as mutual interference between signals and irregular changes during light wave propagation) to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters. Based on the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters combined with the parameters of the optical communication system, the mapping relationship data used to characterize the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters and the complexity of the optical communication system is determined. It can accurately identify the correlation between the above-mentioned interference problems and system settings, and provide a data basis for the subsequent precise correction of the above-mentioned interference; then, the pre-trained generative adversarial network model generates the wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adaptively adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameter, realizing dynamic response to changes in the transmission environment, significantly improving the flexibility and real-time performance of the compensation strategy, and effectively dealing with signal distortion problems in complex scenarios; Then, based on the target wavelength parameters, the degree of distortion change of the optical signal in the transmission environment of the optical communication system is simulated. Since the degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission, the target wavelength parameters are adjusted according to the degree of distortion change. This can quantitatively evaluate the compensation effect and further optimize the wavelength parameters to continuously improve the transmission quality of the optical signal, thereby solving the technical problem in the existing technology that is difficult to adapt to the dynamically changing transmission environment, resulting in the inability to effectively suppress wavelength distortion, and ensuring the signal transmission quality and system performance of the optical communication system.

[0021] In order to help those skilled in the art better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0022] Method Example An embodiment of the present application provides an embodiment of a method for optimizing the wavelength of an optical signal. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0023] See also Figure 1 , which is a flow chart of a method for optimizing optical signal wavelength provided by an embodiment of the present application, as shown in FIG. Figure 1 As shown, the method includes the following steps: Step S101: collecting original optical signal data from an optical communication system.

[0024] In step S101 , the original optical signal data includes initial wavelength parameters of the optical signal (eg wavelength range, transmission rate, etc.).

[0025] In an optional embodiment, an optical communication system may be used as the execution subject of the optical signal wavelength optimization method of the embodiment of the present application. For the convenience of description, the optical communication system is referred to as the system below.

[0026] In the embodiment of the present application, the system can comprehensively obtain the initial wavelength parameters of the optical signal by collecting the original optical signal data from the optical communication system in real time, providing accurate basic data support for subsequent wavelength analysis and compensation, and ensuring the optical communication system's dynamic perception capability of the optical signal status.

[0027] Step S102 : extracting wavelength distortion features from the original optical signal data, and constructing target distortion distribution data according to the distortion categories of the wavelength distortion features.

[0028] In step S102, the distortion categories of the wavelength distortion feature include adjacent channel interference distortion and group velocity dispersion distortion, and the target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or the space dimension.

[0029] In an embodiment of the present application, the system can extract the wavelength distortion features from the original optical signal data and classify the wavelength distortion features into adjacent channel interference distortion and group velocity dispersion distortion. Then, based on the distortion category of the wavelength distortion features, target distortion distribution data is constructed to characterize the distribution of wavelength distortion in the time dimension or space dimension. In this way, adjacent channel interference distortion and group velocity dispersion distortion can be extracted through classification, and different types of distortion sources can be accurately identified. In combination with the distribution characteristics of the time or space dimension, the dynamic evolution law of wavelength distortion can be more comprehensively characterized, thereby providing a basis for subsequent targeted compensation.

[0030] See also Figure 2 , which is a flowchart of a process for constructing target distortion distribution data provided by an embodiment of the present application, and the process includes the following steps: Step S1021, extracting power spectrum density data from the original optical signal data; Step S1022, calculating the error between the actual power value and the ideal power value at each wavelength point of the optical signal based on the power spectrum density data to obtain a wavelength distortion characteristic; Step S1023: classify the wavelength distortion feature to classify the wavelength distortion feature into adjacent channel interference distortion and group velocity dispersion distortion; Step S1024, performing adjacent channel interference analysis on the wavelength distortion characteristics to obtain first distortion distribution data; Step S1025, performing group velocity dispersion analysis on the wavelength distortion characteristics to obtain second distortion distribution data; Step S1026 : constructing target distortion distribution data based on the first distortion distribution data and the second distortion distribution data.

[0031] Specifically, extracting wavelength distortion features in an optical communication system and performing classification processing on adjacent channel interference and group velocity dispersion to obtain target distortion distribution data can be achieved through the following example: First, the system collects wavelength data (i.e., original optical signal data) of a set of optical signals from the optical communication system, with a wavelength range of 1550nm to 1560nm and a sampling interval of 0.1nm. Power spectral density data is extracted from the original optical signal data using an optical spectrum analyzer, and the deviation (i.e., error) between the actual power value and the ideal power value at each wavelength point is calculated to obtain the wavelength distortion feature; for example, if the actual power value measured at a wavelength of 1550.5nm is -2.3dB, while the ideal power value is 0dB, then the recorded wavelength distortion feature is determined to be 2.3.

[0032] The system then performs classification processing for adjacent channel interference, using a spectrum separation algorithm to characterize power leakage between adjacent channels, with a threshold of 1.5 dB. If the leakage power at 1551.0 nm is 1.8 dB, the system classifies it as affected by adjacent channel interference. It then uses a fast Fourier transform (FFT) algorithm to decompose the corresponding power spectrum, extract the interference frequency components, and quantify the interference intensity, generating the first distortion distribution data. This data shows that the interference is primarily concentrated within a 0.2 nm bandwidth. The system then performs classification processing for group velocity dispersion, using a dispersion compensation algorithm to quantify the dispersion impact by measuring pulse broadening. If the pulse broadening at 1552.0 nm is 10 ps, exceeding the threshold of 5 ps, it is classified as affected by group velocity dispersion. The system then uses the finite difference method to calculate the dispersion coefficient, resulting in a value of 20 ps / nm / km, which is used as the second distortion distribution data. Finally, the first distortion distribution data and the second distortion distribution data are integrated into the target distortion distribution data to generate a two-dimensional matrix. The rows of the two-dimensional matrix represent wavelength points, and the columns represent the distortion type and value. For example, the distortion value corresponding to 1550.5nm in the two-dimensional matrix is 2.3, and the type is adjacent channel interference. A heat map is generated using a data visualization tool to intuitively display the distortion distribution. Analysis shows that the distortion is mainly concentrated in the range of 1550nm to 1552nm, providing a basis for subsequent optimization.

[0033] In step S103, a pre-trained convolutional neural network model performs an in-depth analysis on the target distortion distribution data to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determines mapping relationship data based on the nonlinear crosstalk characteristic parameters, polarization mode dispersion characteristic parameters and parameters of the optical communication system.

[0034] In step S103, the mapping relationship data is used to characterize the mapping relationship between the nonlinear crosstalk characteristic parameter and the polarization mode dispersion characteristic parameter and the complexity of the optical communication system.

[0035] In an embodiment of the present application, the system can perform in-depth analysis of the target distortion distribution data using a pre-trained convolutional neural network model, and can quickly identify complex interference in optical signal transmission (such as mutual interference between signals and irregular changes during light wave propagation) to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters. Based on the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters combined with the parameters of the optical communication system, the system can determine the mapping relationship data used to characterize the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters and the complexity of the optical communication system, and can accurately identify the correlation between the above-mentioned interference problems and system settings, providing a data basis for the subsequent precise correction of the above-mentioned interference.

[0036] See also Figure 3 , which is a flowchart of a process for constructing mapping relationship data provided by an embodiment of the present application, and the process includes the following steps: Step S1031: input the target distortion distribution data into a convolutional neural network model, and the convolutional neural network model classifies the target distortion distribution data to classify the target distortion distribution data into nonlinear crosstalk distortion and polarization mode dispersion distortion; Step S1032: performing nonlinear crosstalk analysis on the target distortion distribution data using a convolutional neural network model to obtain nonlinear crosstalk characteristic parameters; Step S1033: performing polarization mode dispersion analysis on the target distortion distribution data using a convolutional neural network model to obtain polarization mode dispersion characteristic parameters; Step S1034, generating a complexity index of the optical communication system based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and the parameters of the optical communication system through a regression analysis model; Step S1035 : generating mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and the corresponding complexity index.

[0037] Specifically, in-depth analysis of target distortion distribution data and construction of mapping relationship data in optical communication systems can be achieved through the following example: the system uses a pre-trained convolutional neural network model to deeply mine the wavelength distortion features for a set of target distortion distribution data with a wavelength range of 1545nm to 1555nm, wherein the model includes a network model with multiple layers of convolution and pooling layers. The input data of the model is the target distortion distribution data, the matrix dimension is 50×10, each row of the matrix represents a wavelength point, and each column represents a different distortion parameter; the hidden layer feature vector is extracted from the target distortion distribution data through the convolutional neural network model. For example, the eigenvalue output at 1546.4nm is 0.85, indicating the presence of significant nonlinear characteristics. The convolutional neural network model then performs polarization mode dispersion analysis on the target distortion distribution data. A signal demodulation algorithm is used to match the hidden layer eigenvectors with a pre-set nonlinear model. If the correlation coefficient between the eigenvalue at 1547.2 nm and the model reaches 0.9, nonlinear crosstalk is determined to be dominant. The affected bandwidth is calculated to be 0.3 nm, which is used as the polarization mode dispersion characteristic parameter. The convolutional neural network model then performs polarization mode dispersion analysis on the target distortion distribution data, using a polarization state decomposition algorithm to quantify the polarization differential group delay. For example, the delay value measured at 1548.0 nm is 8 ps, exceeding the threshold of 6 ps, and is classified as a feature affected by polarization mode dispersion. The polarization mode distribution is further calculated using a matrix decomposition method, and the principal axis offset angle is calculated to be 15 degrees, which is used as the polarization mode dispersion characteristic parameter. Finally, the system can generate the complexity index of the optical communication system through a regression analysis model based on the nonlinear crosstalk characteristic parameters, polarization mode dispersion characteristic parameters and the parameters of the optical communication system (such as the number of channels is 40). For example, the analysis shows that the complexity index at 1546.4nm is 3.2, which is positively correlated with the nonlinear crosstalk characteristic value of 0.85. Based on the correlation between the nonlinear crosstalk characteristic parameters, polarization mode dispersion characteristic parameters and the corresponding complexity index, mapping relationship data is generated.

[0038] In step S104, the pre-trained generative adversarial network model generates a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameter.

[0039] In an embodiment of the present application, the system can generate a wavelength correction coefficient corresponding to the optical signal based on mapping relationship data using a pre-trained generative adversarial network model, and adaptively adjust the initial wavelength parameters according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameters, thereby achieving dynamic response to changes in the transmission environment, significantly improving the flexibility and real-time performance of the compensation strategy, and effectively addressing signal distortion problems in complex scenarios.

[0040] See also Figure 4, which is a flow chart of a wavelength parameter adjustment process provided in an embodiment of the application, and the process includes the following steps: Step S1041, inputting the mapping relationship data into the generative adversarial network model, and the generative adversarial network model matches the corresponding wavelength correction coefficient for the optical signal based on the mapping relationship data; Step S1042, correcting the initial wavelength parameter based on the wavelength correction coefficient to obtain a corrected wavelength parameter; Step S1043, performing dispersion analysis on the corrected wavelength parameter based on the dispersion characteristics of the optical fiber material to obtain a dispersion result; the dispersion result includes a dispersion value and a dispersion slope; Step S1044, calculating the wavelength offset based on the dispersion value and the dispersion slope; Step S1045 : adjusting the corrected wavelength parameter based on the wavelength offset to obtain the target wavelength parameter.

[0041] Specifically, in an optical communication system, the wavelength correction coefficient corresponding to the optical signal is generated based on the mapping relationship data, and the initial wavelength parameter is adjusted according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material. This can be achieved by the following example: First, the mapping relationship data with a wavelength range of 1560nm to 1570nm is input into a pre-trained generative adversarial network model, which generates the wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data. For example, a wavelength correction coefficient of 0.75 is output at 1562.5nm, which is used to subsequently correct the initial wavelength parameter to obtain the corrected wavelength parameter. The discriminator network of the generative adversarial network model is used to evaluate the feasibility of the generated correction strategy. The number of training iterations is set to 800, and the learning rate is 0.002. Next, the system can analyze the dispersion value and dispersion slope of the corrected wavelength parameters based on the dispersion characteristics of the optical fiber material. For example, the dispersion value measured at 1563.0nm is 18ps / nm / km, and the dispersion slope is 0.08ps / nm² / km. The adjusted wavelength offset is calculated to be 0.15nm through a numerical simulation algorithm. The corrected wavelength parameters are then adjusted based on the wavelength offset to obtain the target wavelength parameters.

[0042] Step S105 : simulating the degree of distortion change of the optical signal in the transmission environment of the optical communication system based on the target wavelength parameter.

[0043] In step S105 , the distortion variation degree is used to measure the distortion performance of the optical communication system during signal transmission.

[0044] In an embodiment of the present application, the system can perform an optical power leakage simulation analysis on the optical signal in the transmission environment of the optical communication system based on the target wavelength parameters to obtain an optical power leakage value corresponding to the optical signal; if the optical power leakage value is greater than the preset leakage value, the system calculates a first compensation gain value based on the target wavelength parameters through a power compensation model; and then determines the degree of distortion change based on the first compensation gain value.

[0045] Optionally, the system determines the degree of distortion change based on the first compensation gain value, including the following process: adjusting the target wavelength parameter based on the first compensation gain value to obtain the compensated target wavelength parameter; then, based on the compensated target wavelength parameter, performing dispersion compensation error analysis on the optical signal in the transmission environment of the optical communication system to obtain the compensation error corresponding to the optical signal; then, generating a parameter correction coefficient based on the compensation error, and correcting the above-mentioned compensated target wavelength parameter based on the parameter correction coefficient to obtain the corrected target wavelength parameter; finally, determining the degree of distortion change based on the corrected target wavelength parameter and the initial wavelength parameter.

[0046] Specifically, in an optical communication system, based on the target wavelength parameters, the degree of distortion change of an optical signal in the transmission environment of the optical communication system can be simulated as follows: First, an optical power leakage simulation analysis is performed on the optical signal in the transmission environment of the optical communication system. Using a power distribution analysis algorithm, a power leakage value of -2.3dBm is detected at 1564.8nm, exceeding a threshold of -3.0dBm. The system automatically invokes a power compensation model, calculates a compensation gain (i.e., a first compensation gain value) of 1.2dB, and adjusts the target wavelength parameters based on the first compensation gain value to obtain the compensated target wavelength parameters. Subsequently, a dispersion compensation error analysis is performed on the optical signal in the transmission environment of the optical communication system. Using an error quantization algorithm, a compensation error of 0.5ps / nm is measured at 1565.2nm. The system generates a parameter correction coefficient of 0.92 based on the compensation error using an adaptive filtering algorithm. The compensation module parameters are automatically adjusted to correct the compensated target wavelength parameters based on the parameter correction coefficient, obtaining the corrected target wavelength parameters. Finally, the degree of distortion change is determined based on the corrected target wavelength parameters and the initial wavelength parameters to ensure distortion minimization.

[0047] Step S106 , adjusting the target wavelength parameter according to the degree of distortion change to optimize the wavelength of the optical signal.

[0048] In an embodiment of the present application, the system can obtain wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal when the degree of distortion change is greater than or equal to a preset threshold; then, based on the wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal, the target wavelength parameter is adjusted to obtain the adjusted target wavelength parameter; then, the system can perform polarization mode crosstalk analysis on the adjusted target wavelength parameter to obtain a first signal compensation coefficient, and perform high-order dispersion effect analysis on the adjusted target wavelength parameter to obtain a second signal compensation coefficient; finally, the system can further optimize the wavelength of the optical signal based on the first signal compensation coefficient and the second signal compensation coefficient to continuously improve the transmission quality of the optical signal, thereby solving the technical problem in the prior art that it is difficult to adapt to the dynamically changing transmission environment, resulting in the inability to effectively suppress wavelength distortion, thereby ensuring the signal transmission quality and system performance of the optical communication system.

[0049] Specifically, in optical communication systems, if the degree of distortion change does not reach a preset threshold, the target wavelength parameters can be automatically adjusted using real-time feedback of wavelength drift interference data and temperature-dependent dispersion data. First, the system collects real-time optical signals in the wavelength range of 1555nm to 1565nm with a sampling interval of 0.05nm to generate a wavelength drift interference matrix (200×6 dimensions) (i.e., wavelength drift interference data). This matrix is then input into a deep learning prediction model based on a convolutional neural network. The model is trained for 1000 iterations with a learning rate of 0.001, and predicts a wavelength drift of 0.12nm at 1557.8nm. The system then combines the temperature-dependent dispersion data to determine the dispersion at 1558.2nm is 16.5ps / nm / km at 25°C. When the temperature rises to 30°C, the dispersion increases to 17.0ps / nm / km, with a dispersion slope of 0.07ps / nm² / km. Using a polynomial fitting algorithm, the system calculates the dispersion shift caused by the temperature change to be 0.03nm. Based on the wavelength drift of 0.12nm and the dispersion shift of 0.03nm, the system automatically updates the wavelength parameters to obtain the adjusted target wavelength parameters. The system then performs polarization mode crosstalk analysis on the adjusted target wavelength parameters. Using the optical power monitoring algorithm, the system detects a power attenuation of -1.8dBm at 1559.5nm, which does not exceed the -2.5dBm threshold. The system then invokes the gain adjustment model, calculates a gain compensation value of 0.9dB (i.e., the first signal compensation coefficient), and generates a power distribution curve, which is stored in the system log. If missing dispersion data due to temperature fluctuations is detected, the system analyzes the higher-order dispersion effects of the adjusted target wavelength parameters. It then automatically invokes a time-series interpolation algorithm, combining historical data to complete the dispersion value at 1560.1nm to 16.8ps / nm / km (the second signal compensation coefficient). Finally, to address the combined effects of wavelength drift and dispersion, the system employs an adaptive equalization algorithm, calculating an equalization coefficient of 0.88 at 1561.3nm. The system then automatically adjusts transmission module parameters to further optimize the optical signal wavelength and ensure signal integrity.

[0050] In an optional embodiment, the system can generate a compensation mechanism design based on the optimized wavelength parameters, perform signal compensation processing for polarization mode crosstalk and high-order dispersion effects, and update the wavelength parameters of the optical signal through multiple rounds of iterative calculations.

[0051] Specifically, in optical communication systems, to compensate for polarization-mode crosstalk (PMC) and higher-order dispersion effects, the system first samples signals in the wavelength range of 1545nm to 1555nm with a sampling interval of 0.04nm. This generates a PMC matrix (250×8 dimensions). Principal component analysis (PCA) is used to extract the main interference components, calculating the PMC value at 1547.6nm to be 0.15dB. Next, the system uses a random forest regression model with 800 training iterations and a learning rate of 0.002 to predict that the signal phase offset caused by PMC at 1548.3nm is 0.08rad. Subsequently, to address higher-order dispersion effects, the system measured second-order dispersion at 1549.1 nm and found it to be 0.045 ps / nm² / km at 25°C. This increased to 0.048 ps / nm² / km at 28°C. Using a Lagrangian interpolation algorithm, the system calculated the dispersion increment due to the temperature change to be 0.002 ps / nm² / km, storing the result in the parameter database. To address signal distortion caused by polarization-mode crosstalk (PMC), the system invoked an adaptive filtering algorithm, calculating a filter coefficient of 0.92 at 1550.7 nm. This generated a polarization compensation curve and stored it in the log. If higher-order dispersion data were missing, the system used a Kalman filter time series prediction to complete the dispersion value at 1551.4 nm to 0.046 ps / nm² / km. Finally, the system integrated PMC and higher-order dispersion effects and, using a gradient descent optimization algorithm, iteratively calculated a compensation coefficient of 0.85 at 1552.9 nm, thus updating the wavelength parameters of the optical signal.

[0052] Furthermore, the system can analyze the transmission quality indicator variation trends of the modulation format and the optical signal bandwidth in the optimization parameter set to obtain the optimization parameter set.

[0053] Specifically, in optical communication systems, the system automatically generates an optimized parameter set for optimized wavelength parameters by analyzing the impact of modulation format and optical signal bandwidth on transmission quality indicators. First, the system collects signal data within the wavelength range of 1560nm to 1570nm with a sampling interval of 0.05nm. The system calculates the bit error rate (BER) trends for different modulation formats, such as QPSK and 16-QAM. The system finds that at 1562.5nm, the BER is 1.2e-4 for QPSK modulation, while it increases to 3.5e-4 for 16-QAM. This analysis indicates that higher-order modulation formats are more sensitive to noise. Subsequently, the system uses a support vector machine (SVM) algorithm with a radial basis function (RBF) kernel and 500 training samples. The system predicts that at 1564.8nm, when the optical signal bandwidth is extended from 10GHz to 20GHz, the signal eye opening decreases by 0.18, indicating that bandwidth expansion leads to a decrease in transmission quality. The system then analyzed the spectrum characteristics using Fourier transforms. At 1566.3nm, the measured spectral width for a 15GHz bandwidth was 0.12nm. A linear regression model was used to predict the impact of bandwidth on signal jitter, calculating the jitter increment to be 0.03ps. Furthermore, the system combined the effects of modulation format and bandwidth, invoking a genetic algorithm at 1568.9nm with a population size of 100, iterations of 200, and an optimized modulation depth parameter of 0.75 to generate an optimized parameter set.

[0054] Furthermore, the system can adjust the wavelength configuration strategy of the optical module by optimizing the parameter set, monitor the changes in transmission quality indicators such as optical amplifier noise and dispersion compensation error in real time, and determine whether the system complexity meets the preset path reduction requirements.

[0055] Specifically, in optical communication systems, the wavelength configuration strategy of the optical module is adjusted based on the optimized parameter set. The system first automatically collects signal data in the wavelength range of 1550nm to 1560nm with a sampling interval of 0.1nm. In view of the influence of optical amplifier noise, the changing trend of the signal-to-noise ratio (SNR) is calculated. Through analysis, it is found that the SNR is 25.6dB at 1553.2nm, while the SNR drops to 23.8dB at 1555.7nm, indicating that the noise accumulation increases with wavelength offset. The system then calls the random forest algorithm, sets the number of trees to 150 and the maximum depth to 20, and predicts that when the noise power at 1558.4nm increases from 0.02mW to 0.05mW, the SNR will further decrease by 1.5dB. The analysis shows that the interference of noise on transmission quality increases nonlinearly. Next, the system used a time-domain analysis algorithm to calculate the residual dispersion at 1551.9nm, finding it to be 12.3ps / nm. Using a polynomial regression model to predict the impact of the error on signal distortion, the system determined a distortion increment of 0.07. Combined with historical data analysis, this confirmed a positive correlation between error accumulation and wavelength offset. To enable real-time monitoring, the system deployed a neural network-based prediction model at 1554.6nm, setting the number of hidden layers to 3 and the number of nodes per layer to 64. The system calculated transmission quality indicators in real time and found that the signal delay increment caused by the dispersion compensation error was 0.04ps. Adjustment recommendations were automatically generated and the parameter library updated. The system also assessed complexity. By calculating resource utilization and algorithm execution time, it determined that the average processing time of the current monitoring logic was 0.32 seconds, below the preset threshold of 0.5 seconds. Based on business needs analysis, the system adjusted the monitoring frequency from 1 to 0.8 times per second to further reduce resource load and ensure compliance with path complexity reduction requirements. The relevant data is synchronized to the system log, forming a complete closed-loop process from parameter adjustment to complexity optimization.

[0056] Furthermore, if the system complexity reduction does not meet the preset path requirements, the system can re-analyze the interaction between fiber dispersion interaction and optical signal bandwidth through the long short-term memory network, obtain a new correction strategy solution, and determine the final wavelength optimization parameters.

[0057] Specifically, in an optical communication system, when system complexity reduction fails to meet pre-set path requirements, the system automatically activates a long short-term memory (LSTM) network for in-depth analysis, evaluating the interaction between fiber dispersion and optical signal bandwidth. First, the system collects optical signal data at 0.2nm intervals within the wavelength range of 1548nm to 1552nm and calculates the correlation between dispersion and bandwidth utilization. The analysis shows that at 1549.5nm, the dispersion is 15.7ps / nm and the bandwidth utilization is 82.3%. However, at 1551.0nm, the dispersion increases to 16.4ps / nm and the bandwidth utilization decreases to 79.8%, indicating that increased dispersion is constraining bandwidth resources. Next, the system deploys an LSTM model with a time step of 10 and 128 hidden units. Using historical data, the model is trained to predict that at 1550.3nm, if the bandwidth utilization is adjusted to 80.5%, the dispersion value could decrease to 15.9ps / nm. This analysis demonstrates a nonlinear correlation between bandwidth and dispersion. The system then generated a correction strategy based on the prediction results and optimized the wavelength parameters using a gradient descent algorithm. Setting a learning rate of 0.01 and 200 iterations, the system calculated that adjusting the signal bandwidth to 81.2% at 1549.8nm would keep the dispersion value within 15.5ps / nm. Finally, the system combined the optimized wavelength parameters with service requirements and automatically verified their impact on transmission stability. Simulation analysis confirmed that the adjusted transmission bit error rate dropped from 0.00012 to 0.00009, meeting service reliability requirements.

[0058] Through the method provided in the embodiment of the present application, the initial wavelength parameters of the optical signal are fully acquired, and accurate basic data support is provided for subsequent wavelength analysis and compensation, thereby ensuring the dynamic perception capability of the optical communication system to the optical signal status; the adjacent channel interference distortion and group velocity dispersion distortion are extracted by classification, and different types of distortion sources can be accurately identified. Combined with the distribution characteristics of the time or space dimensions, the dynamic evolution law of the wavelength distortion can be more comprehensively characterized, providing a basis for subsequent targeted compensation; the complex interference in the optical signal transmission (such as mutual interference between signals, irregular changes during light wave propagation) is quickly identified to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and based on the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters combined with the parameters of the optical communication system, the mapping between the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters and the complexity of the optical communication system is determined. The mapping relationship data can accurately identify the correlation between the above-mentioned interference problems and system settings, providing a data basis for the subsequent precise correction of the above-mentioned interference; then the pre-trained generative adversarial network model generates the wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adaptively adjusts the initial wavelength parameters according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain the target wavelength parameters, realizing dynamic response to changes in the transmission environment, significantly improving the flexibility and real-time performance of the compensation strategy, and effectively dealing with signal distortion problems in complex scenarios; then the target wavelength parameters are adjusted according to the degree of distortion change, which can quantitatively evaluate the compensation effect and further optimize the wavelength parameters to continuously improve the transmission quality of the optical signal, thereby solving the technical problem in the existing technology that it is difficult to adapt to the dynamically changing transmission environment, resulting in the inability to effectively suppress wavelength distortion, and ensuring the signal transmission quality and system performance of the optical communication system.

[0059] Device embodiment The embodiment of the present application provides an optical signal wavelength optimization device, wherein: Figure 5 A schematic diagram of the structure of an optical signal wavelength optimization device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the device includes: a data acquisition module 11, a feature extraction module 12, an analysis module 13, an adjustment module 14, a simulation module 15 and an optimization module 16. Figure 5 You can see the connection relationship between several modules.

[0060] The data acquisition module 11 is used to collect original optical signal data from the optical communication system; the original optical signal data includes the initial wavelength parameters of the optical signal; A feature extraction module 12 is configured to extract wavelength distortion features from the original optical signal data and construct target distortion distribution data based on the distortion categories of the wavelength distortion features. The distortion categories of the wavelength distortion features include adjacent channel interference distortion and group velocity dispersion distortion. The target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or the space dimension. An analysis module 13 is configured to perform an in-depth analysis of the target distortion distribution data using a pre-trained convolutional neural network model to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determine mapping relationship data based on the nonlinear crosstalk characteristic parameters, polarization mode dispersion characteristic parameters, and parameters of the optical communication system; the mapping relationship data is used to characterize the mapping relationship between the nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters and the complexity of the optical communication system; An adjustment module 14 is configured to generate a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data using a pre-trained generative adversarial network model, and adjust the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter; The simulation module 15 is used to simulate the degree of distortion change of the optical signal in the transmission environment of the optical communication system based on the target wavelength parameter; the degree of distortion change is used to measure the distortion performance of the optical communication system during the signal transmission process; The optimization module 16 is configured to adjust the target wavelength parameter according to the degree of distortion change, so as to optimize the wavelength of the optical signal.

[0061] Optionally, the optimization module includes: an acquisition unit, configured to acquire wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal if the degree of distortion change is greater than or equal to a preset threshold; a parameter adjustment unit, configured to adjust a target wavelength parameter based on wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal to obtain an adjusted target wavelength parameter; a compensation analysis unit, configured to perform a polarization mode crosstalk analysis on the adjusted target wavelength parameter to obtain a first signal compensation coefficient, and perform a high-order dispersion effect analysis on the adjusted target wavelength parameter to obtain a second signal compensation coefficient; The optimization unit is configured to optimize the wavelength of the optical signal according to the first signal compensation coefficient and the second signal compensation coefficient.

[0062] Optionally, the feature extraction module includes: An extraction unit, configured to extract power spectrum density data from the original optical signal data; A first calculation unit is configured to calculate the error between the actual power value and the ideal power value at each wavelength point of the optical signal based on the power spectrum density data to obtain a wavelength distortion characteristic; a first classification processing unit, configured to perform classification processing on the wavelength distortion feature to classify the wavelength distortion feature into adjacent channel interference distortion and group velocity dispersion distortion; an interference analysis unit, configured to perform adjacent channel interference analysis on the wavelength distortion characteristics to obtain first distortion distribution data; a first dispersion analysis unit, configured to perform group velocity dispersion analysis on the wavelength distortion characteristics to obtain second distortion distribution data; The constructing unit is configured to construct target distortion distribution data based on the first distortion distribution data and the second distortion distribution data.

[0063] Optionally, the analysis module includes: a second classification processing unit, configured to input the target distortion distribution data into a convolutional neural network model, and perform classification processing on the target distortion distribution data by the convolutional neural network model to classify the target distortion distribution data into nonlinear crosstalk distortion and polarization mode dispersion distortion; A nonlinear crosstalk analysis unit, configured to perform nonlinear crosstalk analysis on target distortion distribution data using a convolutional neural network model to obtain nonlinear crosstalk characteristic parameters; a polarization mode dispersion analysis unit, configured to perform polarization mode dispersion analysis on target distortion distribution data using a convolutional neural network model to obtain polarization mode dispersion characteristic parameters; A first generating unit is configured to generate a complexity index of the optical communication system based on a nonlinear crosstalk characteristic parameter, a polarization mode dispersion characteristic parameter, and a parameter of the optical communication system through a regression analysis model; The second generating unit is configured to generate mapping relationship data based on the nonlinear crosstalk characteristic parameter, the polarization mode dispersion characteristic parameter and the corresponding complexity index.

[0064] Optionally, the adjustment module includes: A matching unit, configured to input the mapping relationship data into a generative adversarial network model, and the generative adversarial network model matches a corresponding wavelength correction coefficient for the optical signal based on the mapping relationship data; A correction unit, configured to correct the initial wavelength parameter based on the wavelength correction coefficient to obtain a corrected wavelength parameter; A second dispersion analysis unit is used to perform dispersion analysis on the corrected wavelength parameter based on the dispersion characteristics of the optical fiber material to obtain a dispersion result; the dispersion result includes a dispersion value and a dispersion slope; a second calculation unit, configured to calculate a wavelength offset based on the dispersion value and the dispersion slope; The first adjustment unit is configured to adjust the corrected wavelength parameter based on the wavelength offset to obtain a target wavelength parameter.

[0065] Optionally, the simulation module includes: A simulation analysis unit, configured to perform an optical power leakage simulation analysis on an optical signal in an optical communication system transmission environment based on a target wavelength parameter, and obtain an optical power leakage value corresponding to the optical signal; a third calculation unit, configured to calculate a first compensation gain value based on a target wavelength parameter by using a power compensation model if the optical power leakage value is greater than a preset leakage value; The determining unit is configured to determine a distortion variation degree according to the first compensation gain value.

[0066] Optionally, the determining unit includes: a second adjusting unit, configured to adjust the target wavelength parameter based on the first compensation gain value to obtain a compensated target wavelength parameter; a dispersion compensation error analysis unit, configured to perform dispersion compensation error analysis on an optical signal in an optical communication system transmission environment based on a compensated target wavelength parameter, and obtain a compensation error corresponding to the optical signal; a correction unit, configured to generate a parameter correction coefficient based on the compensation error, and correct the compensated target wavelength parameter based on the parameter correction coefficient to obtain a corrected target wavelength parameter; The degree determining unit is used to determine the degree of distortion change based on the corrected target wavelength parameter and the initial wavelength parameter.

[0067] Storage medium embodiment Embodiments of the present application provide a computer-readable storage medium storing a program. When executed by a processor, the program implements some or all of the steps of the optical signal wavelength optimization method described in the aforementioned method embodiments of the present application. The storage medium can be any medium capable of storing program code, such as a USB flash drive, a removable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0068] Processor Embodiments An embodiment of the present application provides a processor for running a program, wherein when the program is running, some or all of the steps in the optical signal wavelength optimization method described in the aforementioned method embodiment are executed.

[0069] It should be noted that the various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. The device embodiment described above is merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0070] The above is only one specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for optimizing optical signal wavelength, characterized in that: include: Collecting original optical signal data from an optical communication system; the original optical signal data includes initial wavelength parameters of the optical signal; Extracting wavelength distortion features from the original optical signal data, and constructing target distortion distribution data based on the distortion categories of the wavelength distortion features; the distortion categories of the wavelength distortion features include adjacent channel interference distortion and group velocity dispersion distortion, and the target distortion distribution data is used to characterize the distribution of wavelength distortion in a time dimension or a space dimension; Performing an in-depth analysis of the target distortion distribution data using a pre-trained convolutional neural network model to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determining mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and parameters of the optical communication system; The mapping relationship data is used to characterize the mapping relationship between the nonlinear crosstalk characteristic parameter and the polarization mode dispersion characteristic parameter, and the complexity of the optical communication system; The pre-trained generative adversarial network model generates a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter; Based on the target wavelength parameter, simulating the degree of distortion change of the optical signal in the transmission environment of the optical communication system; the degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission; The target wavelength parameter is adjusted according to the degree of distortion change to optimize the wavelength of the optical signal.

2. The method according to claim 1, characterized in that The adjusting the target wavelength parameter according to the degree of distortion change to optimize the wavelength of the optical signal includes: If the degree of distortion change is greater than or equal to a preset threshold, acquiring wavelength drift interference data and temperature-dependent dispersion data corresponding to the optical signal; adjusting the target wavelength parameter based on the wavelength drift interference data and the temperature-dependent dispersion data corresponding to the optical signal to obtain an adjusted target wavelength parameter; Performing a polarization mode crosstalk analysis on the adjusted target wavelength parameter to obtain a first signal compensation coefficient, and performing a high-order dispersion effect analysis on the adjusted target wavelength parameter to obtain a second signal compensation coefficient; The wavelength of the optical signal is optimized according to the first signal compensation coefficient and the second signal compensation coefficient.

3. The method according to claim 1, characterized in that The extracting of the wavelength distortion feature from the original optical signal data and constructing target distortion distribution data according to the distortion category of the wavelength distortion feature includes: extracting power spectral density data from the raw optical signal data; Based on the power spectrum density data, calculating the error between the actual power value and the ideal power value at each wavelength point of the optical signal to obtain the wavelength distortion characteristic; performing classification processing on the wavelength distortion feature to classify the wavelength distortion feature into adjacent channel interference distortion and group velocity dispersion distortion; Performing adjacent channel interference analysis on the wavelength distortion characteristics to obtain first distortion distribution data; performing group velocity dispersion analysis on the wavelength distortion characteristics to obtain second distortion distribution data; The target distortion distribution data is constructed based on the first distortion distribution data and the second distortion distribution data.

4. The method according to claim 1, wherein The pre-trained convolutional neural network model performs an in-depth analysis on the target distortion distribution data to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determines mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and the parameters of the optical communication system, including: Inputting the target distortion distribution data into the convolutional neural network model, and performing classification processing on the target distortion distribution data by the convolutional neural network model to classify the target distortion distribution data into nonlinear crosstalk distortion and polarization mode dispersion distortion; Performing nonlinear crosstalk analysis on the target distortion distribution data using the convolutional neural network model to obtain the nonlinear crosstalk characteristic parameters; Performing polarization mode dispersion analysis on the target distortion distribution data using the convolutional neural network model to obtain the polarization mode dispersion characteristic parameters; generating a complexity index of the optical communication system based on the nonlinear crosstalk characteristic parameter, the polarization mode dispersion characteristic parameter, and the parameters of the optical communication system through a regression analysis model; The mapping relationship data is generated based on the nonlinear crosstalk characteristic parameter, the polarization mode dispersion characteristic parameter and the corresponding complexity index.

5. The method according to claim 1, wherein The pre-trained generative adversarial network model generates a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data, and adjusts the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter, including: Inputting the mapping relationship data into the generative adversarial network model, and having the generative adversarial network model match the corresponding wavelength correction coefficient for the optical signal based on the mapping relationship data; Correcting the initial wavelength parameter based on the wavelength correction coefficient to obtain a corrected wavelength parameter; Performing a dispersion analysis on the corrected wavelength parameter based on the dispersion characteristics of the optical fiber material to obtain a dispersion result; the dispersion result includes a dispersion value and a dispersion slope; Calculating a wavelength shift based on the dispersion value and the dispersion slope; The corrected wavelength parameter is adjusted based on the wavelength offset to obtain the target wavelength parameter.

6. The method according to claim 5, characterized in that The simulating, based on the target wavelength parameter, the degree of distortion change of the optical signal in the transmission environment of the optical communication system includes: Based on the target wavelength parameter, performing an optical power leakage simulation analysis on the optical signal in the optical communication system transmission environment to obtain an optical power leakage value corresponding to the optical signal; If the optical power leakage value is greater than a preset leakage value, calculating a first compensation gain value based on the target wavelength parameter using a power compensation model; The distortion change degree is determined according to the first compensation gain value.

7. The method according to claim 6, characterized in that The determining the distortion change degree according to the first compensation gain value includes: Adjusting the target wavelength parameter based on the first compensation gain value to obtain a compensated target wavelength parameter; performing dispersion compensation error analysis on the optical signal in the optical communication system transmission environment based on the compensated target wavelength parameter to obtain a compensation error corresponding to the optical signal; generating a parameter correction coefficient based on the compensation error, and correcting the compensated target wavelength parameter based on the parameter correction coefficient to obtain a corrected target wavelength parameter; The distortion change degree is determined based on the corrected target wavelength parameter and the initial wavelength parameter.

8. An optical signal wavelength optimization device, characterized in that: include: A data acquisition module, used for acquiring raw optical signal data from the optical communication system; The original optical signal data includes the initial wavelength parameters of the optical signal; a feature extraction module for extracting wavelength distortion features from the original optical signal data and constructing target distortion distribution data based on the distortion categories of the wavelength distortion features; the distortion categories of the wavelength distortion features include adjacent channel interference distortion and group velocity dispersion distortion, and the target distortion distribution data is used to characterize the distribution of wavelength distortion in the time dimension or the space dimension; an analysis module, configured to perform an in-depth analysis of the target distortion distribution data using a pre-trained convolutional neural network model to obtain nonlinear crosstalk characteristic parameters and polarization mode dispersion characteristic parameters, and determine mapping relationship data based on the nonlinear crosstalk characteristic parameters, the polarization mode dispersion characteristic parameters, and parameters of the optical communication system; The mapping relationship data is used to characterize the mapping relationship between the nonlinear crosstalk characteristic parameter and the polarization mode dispersion characteristic parameter, and the complexity of the optical communication system; An adjustment module is configured to generate a wavelength correction coefficient corresponding to the optical signal based on the mapping relationship data by a pre-trained generative adversarial network model, and adjust the initial wavelength parameter according to the wavelength correction coefficient and the dispersion characteristics of the optical fiber material to obtain a target wavelength parameter; A simulation module, configured to simulate a degree of distortion change of the optical signal in a transmission environment of the optical communication system based on the target wavelength parameter; The degree of distortion change is used to measure the distortion performance of the optical communication system during signal transmission; An optimization module is used to adjust the target wavelength parameter according to the degree of distortion change to optimize the wavelength of the optical signal.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the optical signal wavelength optimization method according to any one of claims 1 to 7 is implemented.

10. A processor, characterized in that: It is used to run a computer program, and when the computer program is run, it executes the optical signal wavelength optimization method according to any one of claims 1 to 7.

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