Optical line protection switching method and device of BIDI system

Abstract

The invention relates to the field of data monitoring, in particular to an optical line protection switching method and device of a BIDI system, and the method comprises the steps: collecting real-time environment parameters through an environment sensor, and generating an environment feature vector; dynamically correcting a switching threshold value of an optical signal quality analysis model through an adaptive algorithm based on the environment feature vector; an optical module is packaged by adopting a double-layer electromagnetic shielding structure, and the structure comprises an inner flexible conductive adhesive layer and an outer metal coating shell; the method comprises the following steps: acquiring feature information of a first BIDI optical module through an optical fiber signal acquisition device, and training a first enhanced optical signal quality analysis model based on the feature information; a mixed signal analysis model is constructed, the model fuses a convolutional neural network (CNN) and a curvature analysis algorithm, and spatial-temporal features of the optical signals are extracted; and when the output of the mixed signal analysis model exceeds a dynamically corrected switching threshold value, millisecond-level main and standby line switching is triggered.

Description

Technical Field

[0001] The present invention relates to the field of data monitoring, and in particular to an optical line protection switching method and device of a BIDI system. Background Art

[0002] The BIDI system utilizes wavelength division multiplexing (WDM) technology to achieve bidirectional optical signal transmission over a single optical fiber, reducing fiber resource usage by 73%. The OLP protection device uses optical power monitoring and model analysis to determine the status of the primary line in real time. When optical signal quality (such as power, curvature variation, and crosstalk) exceeds preset thresholds, it automatically switches to the backup line, with switching speeds controlled within 15 milliseconds.

[0003] In contrast, Chinese invention publication CN114095076A discloses a BIDI-based OLP optical line protection switching monitoring method and device. Traditional BIDI system OLP (optical line protection) technology relies on fixed thresholds to determine line status, making it prone to false or delayed switching in complex environments (such as electromagnetic interference and temperature fluctuations). This existing technology suffers from the following issues: poor environmental adaptability: the fixed threshold cannot dynamically adapt to optical power fluctuations, resulting in a high false positive rate; weak anti-interference capability: electromagnetic interference causes deviations in signal feature extraction, affecting model analysis accuracy; and limited switching efficiency: traditional hardware design does not optimize signal isolation, resulting in switching delays that increase the risk of service interruption. Summary of the Invention

[0004] The purpose of the present invention is to provide an optical line protection switching method and device for a BIDI system in order to solve the above problems.

[0005] The present invention achieves the above-mentioned purpose through the following technical solutions:

[0006] The method is applied to an OLP optical line protection switching optimization device, which includes an optical fiber signal acquisition device, an environmental sensor, and an anti-interference hardware module. The method includes:

[0007] Collect real-time environmental parameters through environmental sensors and generate environmental feature vectors;

[0008] Based on the environmental feature vector, dynamically modifying the switching threshold of the optical signal quality analysis model through an adaptive algorithm;

[0009] The optical module is encapsulated using a double-layer electromagnetic shielding structure, which includes an inner flexible conductive adhesive layer and an outer metal-coated shell;

[0010] Acquire characteristic information of a first BIDI optical module through an optical fiber signal acquisition device, and train a first enhanced optical signal quality analysis model based on the characteristic information;

[0011] Constructing a hybrid signal analysis model that integrates a convolutional neural network (CNN) with a curvature analysis algorithm to extract spatiotemporal features of optical signals;

[0012] When the output of the mixed signal analysis model exceeds the dynamically modified switching threshold, a millisecond-level primary and backup line switchover is triggered;

[0013] The step of dynamically correcting the switching threshold includes:

[0014] Establish a mapping relationship between environmental parameters and optical power attenuation to generate dynamic attenuation compensation coefficients;

[0015] The preset threshold is adjusted by linear interpolation according to the compensation coefficient.

[0016] Preferably: the double-layer electromagnetic shielding structure includes:

[0017] The inner layer is a flexible conductive adhesive layer that covers the surface of the optical module PCB and extends to the edge of the differential signal line;

[0018] The outer layer is a metal-coated shell with a grounding impedance of ≤0.1Ω and a shielding effectiveness of ≥60dB;

[0019] The differential signal line is arranged on the TOP layer of the PCB, with a distance from the ground layer of ≥2mm.

[0020] Preferably, the construction of the mixed signal analysis model includes:

[0021] The frequency domain features of the optical signal are extracted through CNN to generate a spectrum energy distribution map;

[0022] Perform curvature analysis on the optical fiber signal change curve and calculate the signal distortion index;

[0023] The spectrum energy distribution map and distortion index are input into the fully connected layer, and the comprehensive signal quality score is output.

[0024] Preferably, the acquiring of the characteristic information of the first BIDI optical module by the optical fiber signal acquisition device includes:

[0025] Perform unsupervised learning classification on the optical signal dataset to generate a set of numerical continuous optical signals;

[0026] Construct the optical fiber signal variation curve based on the continuous set and extract the characteristic parameters through curvature analysis;

[0027] The characteristic parameters are input into the optical signal characteristic evaluation model, and the encrypted BIDI optical module characteristic information is output.

[0028] Preferably: the unsupervised learning classification includes:

[0029] Perform traversal access to the optical signal data set to generate a uniformly distributed data set;

[0030] Define P clusters and calculate the average distance within the cluster;

[0031] A light signal clustering tree is generated based on the minimum average distance, and clusters are recursively merged until convergence.

[0032] Preferably, the method further comprises:

[0033] Perform variance analysis on the discrete optical signal set. If the deviation exceeds the threshold, a secondary warning is triggered.

[0034] Encrypt BIDI optical module feature information and ensure data integrity through blockchain distributed storage.

[0035] Preferred: an environmental sensing unit for collecting temperature, humidity and electromagnetic interference intensity parameters;

[0036] A dynamic threshold calculation unit, configured to generate a dynamic switching threshold according to environmental parameters;

[0037] Anti-interference hardware module, including double-layer shielding structure and differential wiring PCB;

[0038] Hybrid model analysis unit, used to perform CNN feature extraction and curvature analysis;

[0039] The switching decision unit is used to trigger line switching within 15ms when the comprehensive score exceeds the threshold;

[0040] The signal encryption unit is used to perform AES-256 encryption and distributed storage on the optical module's characteristic information.

[0041] The invention comprises an electronic device, comprising a bus, a processor, a memory and a computer program stored in the memory, wherein the steps of the method are implemented when the computer program is executed by the processor.

[0042] The invention comprises a computer-readable storage medium having a computer program stored thereon, and the steps of the method are implemented when the program is executed by a processor.

[0043] Compared with the prior art, the beneficial effects are as follows:

[0044] Through the collaborative design of environmental adaptive thresholds, anti-interference hardware and hybrid analysis models, the reliability of the OLP system in complex environments is significantly improved. It is particularly suitable for high-demand scenarios such as 5G fronthaul networks and industrial Internet of Things. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 It is a flow chart of an optical line protection switching method of a BIDI system according to the present invention;

[0047] Figure 2 This is a schematic diagram of the process of obtaining characteristic information of the first BIDI optical module by using the optical fiber signal acquisition device according to the present invention;

[0048] Figure 3 It is a schematic diagram of the process of obtaining characteristic information of the second BIDI optical module through the optical fiber signal acquisition device according to the present invention. DETAILED DESCRIPTION

[0049] In describing the embodiments of the present invention, those skilled in the art should understand that the embodiments of the present invention can be implemented as methods, apparatuses, electronic devices, and computer-readable storage media. Therefore, the embodiments of the present invention can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software. In addition, in some embodiments, the embodiments of the present invention can also be implemented in the form of a computer program product in one or more computer-readable storage media, wherein the computer-readable storage medium contains computer program code.

[0050] The computer-readable storage medium may be any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor devices, apparatuses or components, or any combination thereof. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination thereof. In an embodiment of the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or component.

[0051] Application Overview

[0052] The embodiments of the present invention describe the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0053] It should be understood that each block in the flowchart and / or block diagram, as well as combinations of blocks in the flowchart and / or block diagram, can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine. These computer-readable program instructions are executed by the computer or other programmable data processing device to produce a device that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0054] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to operate in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product that implements the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0055] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process that implements the functions / operations specified by the blocks in the flowchart and / or block diagram.

[0056] The embodiments of the present invention are described below with reference to the accompanying drawings.

[0057] like Figure 1-Figure 3 As shown, S100: according to the first BIDI optical module parameter information, call the first optical signal quality analysis model from the optical signal evaluation model library

[0058] The parameter information of BIDI optical modules is rich and varied, covering the optical module model, package type, rate, wavelength, maximum transmission distance, interface, fiber type, transmitted optical power, transmission angle, and receiving sensitivity. These parameters are the key attributes of the optical module and directly determine its performance in the optical communication network. For example, different models of BIDI optical modules have different applicable transmission scenarios and performance characteristics. High-rate optical modules are suitable for scenarios with large amounts of data transmission, but have higher requirements for the transmission environment and equipment. The optical signal evaluation model library is a collection that stores multiple optical signal quality analysis models, among which the first optical signal quality analysis model accurately matches the parameter information of the first BIDI optical module. Based on the specific parameters of the first BIDI optical module, the system can quickly and accurately call the corresponding model from the model library, providing a basic framework for subsequent optical signal quality analysis.

[0059] S200: Obtaining characteristic information of the first BIDI optical module through the optical fiber signal acquisition device

[0060] S210: Collect signals from the first BIDI optical module using an optical fiber signal collection device to obtain a first optical signal data set

[0061] The fiber-optic signal acquisition device plays a key role in the entire optical signal monitoring process. It utilizes specialized optical and electrical sensing technologies to comprehensively and in real time collect the optical signals transmitted by the first BIDI optical module. During this process, the acquisition device captures various changes in the optical signal during transmission, including fluctuations in optical power and frequency drift. This information is aggregated to form the first optical signal dataset. This dataset serves as the raw data for subsequent analysis of the optical module's characteristics, and its integrity and accuracy directly impact the reliability of the analysis results.

[0062] S220: Perform unsupervised learning classification on the first optical signal data set to generate a first numerical continuous optical signal set

[0063] Unsupervised learning classification is an intelligent data processing technique suitable for situations lacking prior knowledge and data annotation. When processing a first optical signal dataset, all data in the dataset are first traversed and reorganized to generate a first uniform optical signal dataset, achieving a more balanced data distribution and facilitating subsequent analysis. Next, the data in the first uniform optical signal dataset are defined as P clusters. Clustering is based on data similarity, with similar data points grouped into the same cluster. The average distance between each pair of data points in each cluster is calculated to generate an average distance dataset. Based on the average distance dataset, the cluster set with the smallest average distance is selected to form a similar optical signal dataset. Based on the similar optical signal dataset, the average distance dataset is recursively clustered layer by layer, continuously merging similar clusters to ultimately generate an optical signal cluster tree. Based on the structure and characteristics of the optical signal cluster tree, the first optical signal dataset is classified into two categories: continuous and discrete. The continuous portion forms the first numerical continuous optical signal set. This unsupervised learning classification method automatically discovers potential patterns and features in the optical signal data, providing a more valuable data foundation for subsequent analysis.

[0064] S230: Constructing a first optical fiber signal change curve based on the first numerical continuous optical signal set

[0065] The data in the first continuous optical signal set reflects the continuous variation trend of the optical signal over a period of time. By connecting the data points in the set sequentially, with time as the horizontal axis and the optical signal intensity or other relevant parameters as the vertical axis, a first optical fiber signal variation curve can be constructed. This curve intuitively illustrates the dynamic variation of the optical signal over time. By observing the curve's shape, slope, and other characteristics, a preliminary assessment of the optical signal's stability and quality can be made. Large fluctuations in the curve indicate possible external interference or performance issues with the optical module itself.

[0066] S240: Perform curvature analysis on the first optical fiber signal change curve to obtain the first optical fiber signal curvature change result

[0067] Curvature analysis is a mathematical method used to measure the curvature of a curve. For the first optical fiber signal change curve, the curvature of each point on the curve is calculated to obtain the curvature change results of the first optical fiber signal. The larger the curvature, the greater the curve's curvature at that point, indicating a more dramatic change in the optical signal at that moment. For example, when the optical signal experiences sudden interference, the curve's curvature will suddenly increase. By analyzing the curvature change results, abnormal changes in the optical signal can be more accurately detected, providing important evidence for determining the operating status of the optical module.

[0068] S250: Inputting the curvature change result of the first optical fiber signal into the optical signal characteristic evaluation model to obtain characteristic information of the first BIDI optical module

[0069] The optical signal characteristic evaluation model is an intelligent model built on a neural network or other advanced algorithms. After inputting the curvature change results of the first optical fiber signal into the model, it conducts in-depth analysis and processing of this data. The model's internal neurons and complex algorithmic structure automatically extract key features from the data, match them with pre-trained knowledge and patterns, and ultimately output characteristic information about the first BIDI optical module. This characteristic information, including key indicators such as optical power, optical signal transmission speed level, and signal stability, comprehensively reflects the optical signal transmission characteristics of the first BIDI optical module, providing core data support for subsequent optical signal quality analysis and line protection switching decisions.

[0070] S300: Training a first optical signal quality analysis model according to characteristic information of the first BIDI optical module to obtain a first enhanced optical signal quality analysis model

[0071] In its initial state, the first optical signal quality analysis model is trained based on a large amount of basic optical signal data and possesses certain optical signal quality analysis capabilities. However, to more accurately adapt to the actual operating conditions of a specific first BIDI optical module, it needs to be further trained using the acquired characteristic information of the first BIDI optical module. During the training process, incremental learning is used to incorporate new characteristic information into the model. The model adjusts its internal parameters and weights based on this new data, continuously optimizing its performance. Through training with loss data, the first enhanced optical signal quality analysis model can more accurately analyze the optical signal quality of the current first BIDI optical module while retaining its original basic functions. This improves the accuracy and personalization of the analysis results, providing strong support for subsequent more precise optical signal monitoring and protection switching.

[0072] S400: Obtain the second BIDI optical module characteristic information through the optical fiber signal acquisition device, and update and train the second optical signal quality analysis model according to the second BIDI optical module characteristic information to obtain a second enhanced optical signal quality analysis model

[0073] S410: Match input data in the optical signal evaluation model library according to the characteristic information of the second BIDI optical module to obtain a first matching result

[0074] Because BIDI optical modules are typically used in pairs in practical applications, the characteristic information of the second BIDI optical module represents the optical signal transmission characteristics of the paired optical module. This characteristic information is input into the optical signal evaluation model library and matched against the second optical signal quality analysis model in the library. The model library then calculates the degree of match between the characteristic information of the second BIDI optical module and each model based on a preset matching algorithm, ultimately outputting a first matching result reflecting the degree of fit between the characteristic information of the second BIDI optical module and the second optical signal quality analysis model.

[0075] S420: Obtain a predetermined matching threshold, where the predetermined matching threshold corresponds to the second optical signal quality analysis model.

[0076] The predetermined matching threshold is a numerical range pre-set based on the characteristics of the second optical signal quality analysis model and application requirements. It measures the degree of fit between the first matching result and the model and serves as an important criterion for determining whether the second BIDI optical module's characteristic information can be effectively used to update the training model. Different second optical signal quality analysis models may have different predetermined matching thresholds. The threshold is set based on factors such as the model's accuracy requirements, the data's fluctuation range, and the error tolerance of the actual application scenario.

[0077] S430: Determine whether the first matching result is within a predetermined matching threshold

[0078] The first matching result is compared with a predetermined matching threshold. If the first matching result is within the predetermined matching threshold, it indicates that the characteristic information of the second BIDI optical module is well compatible with the second optical signal quality analysis model, and this characteristic information can be used to update and train the model. If it is not within the threshold, further data accuracy needs to be checked or the model needs to be adjusted.

[0079] S440: If the first matching result is within the predetermined matching threshold, obtain a second optical signal quality analysis model and train it to obtain a second enhanced optical signal quality analysis model

[0080] If the first matching result meets the predetermined matching threshold, the second optical signal quality analysis model is confirmed as applicable and trained using the characteristic information of the second BIDI optical module. During the training process, the model adjusts its parameters and structure based on the new characteristic information, optimizing its ability to analyze the optical signal quality of the second BIDI optical module. This training results in a second enhanced optical signal quality analysis model that can more accurately assess the optical signal quality of the paired second BIDI optical module, laying the foundation for the subsequent construction of a more accurate comprehensive optical signal quality analysis model.

[0081] S500: Extracting first parameter information of the first enhanced optical signal quality analysis model and second parameter information of the second enhanced optical signal quality analysis model to construct a third optical signal quality analysis model

[0082] Key parameter information is extracted from the first and second enhanced optical signal quality analysis models, including optical signal transmission parameters, optical signal reception and transmission parameters, and model internal weights and biases. The extracted first and second parameter information is then integrated and used to reconstruct a third optical signal quality analysis model. By integrating the advantageous parameters of the two models, the third optical signal quality analysis model can more comprehensively and accurately reflect the quality of the optical signal throughout the entire transmission link, improving the accuracy and reliability of optical signal monitoring.

[0083] S600: Perform optical signal monitoring on the first main optical line according to the third optical signal quality analysis model to obtain a first optical signal analysis result

[0084] The optical signal transmitted through the first main optical line is fed into the third optical signal quality analysis model, which performs in-depth analysis and calculations on various characteristics of the optical signal. Using its internal algorithms and parameter settings, the model evaluates multiple aspects of the optical signal, including intensity, frequency, and stability, ultimately outputting the first optical signal analysis results. This result comprehensively reflects the current optical signal quality of the first main optical line, providing a direct basis for subsequent protection switching decisions.

[0085] S700: If the analysis result of the first optical signal exceeds a predetermined switching threshold, a first warning instruction is obtained, and the first warning instruction is used to perform a warning switching on the first main optical line.

[0086] The predetermined switching threshold is a predefined optical signal quality standard based on the performance requirements of the optical communication system and actual application experience. When the analysis result of the first optical signal exceeds the predetermined switching threshold, it indicates that the optical signal quality of the first primary optical line has degraded, potentially affecting normal communication. At this point, the system generates a first warning instruction, which triggers the relevant control mechanism to perform a warning switching operation on the first primary optical line, switching optical signal transmission to the backup line. This ensures the continuity and stability of optical communication and avoids communication interruptions caused by failures on the primary line.

[0087] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for optical line protection switching in a BIDI system, characterized by: The method is applied to an OLP optical line protection switching optimization device, which includes an optical fiber signal acquisition device, an environmental sensor, and an anti-interference hardware module. The method includes: Collect real-time environmental parameters through environmental sensors and generate environmental feature vectors; Based on the environmental feature vector, dynamically modifying the switching threshold of the optical signal quality analysis model through an adaptive algorithm; The optical module is encapsulated using a double-layer electromagnetic shielding structure, which includes an inner flexible conductive adhesive layer and an outer metal-coated shell; Acquire characteristic information of a first BIDI optical module through an optical fiber signal acquisition device, and train a first enhanced optical signal quality analysis model based on the characteristic information; Constructing a hybrid signal analysis model that integrates a convolutional neural network (CNN) with a curvature analysis algorithm to extract spatiotemporal features of optical signals; When the output of the mixed signal analysis model exceeds the dynamically modified switching threshold, a millisecond-level primary and backup line switchover is triggered; The step of dynamically correcting the switching threshold includes: Establish a mapping relationship between environmental parameters and optical power attenuation to generate dynamic attenuation compensation coefficients; The preset threshold is adjusted by linear interpolation according to the compensation coefficient.

2. The optical line protection switching method of a BIDI system according to claim 1, characterized in that: The double-layer electromagnetic shielding structure comprises: The inner layer is a flexible conductive adhesive layer that covers the surface of the optical module PCB and extends to the edge of the differential signal line; The outer layer is a metal-coated shell with a grounding impedance of ≤0.1Ω and a shielding effectiveness of ≥60dB; The differential signal line is arranged on the TOP layer of the PCB, with a distance from the ground layer of ≥2mm.

3. The optical line protection switching method of a BIDI system according to claim 1, characterized in that: The construction of the mixed signal analysis model includes: The frequency domain features of the optical signal are extracted through CNN to generate a spectrum energy distribution map; Perform curvature analysis on the optical fiber signal change curve and calculate the signal distortion index; The spectrum energy distribution map and distortion index are input into the fully connected layer, and the comprehensive signal quality score is output.

4. The optical line protection switching method of a BIDI system according to claim 1, characterized in that: Acquiring the characteristic information of the first BIDI optical module by using the optical fiber signal acquisition device includes: Perform unsupervised learning classification on the optical signal dataset to generate a set of numerical continuous optical signals; Construct the optical fiber signal variation curve based on the continuous set and extract the characteristic parameters through curvature analysis; The characteristic parameters are input into the optical signal characteristic evaluation model, and the encrypted BIDI optical module characteristic information is output.

5. The optical line protection switching method of a BIDI system according to claim 1, characterized in that: The unsupervised learning classification includes: Perform traversal access to the optical signal data set to generate a uniformly distributed data set; Define P clusters and calculate the average distance within the cluster; A light signal clustering tree is generated based on the minimum average distance, and clusters are recursively merged until convergence.

6. The optical line protection switching method of a BIDI system according to claim 1, characterized in that: The method further comprises: Perform variance analysis on the discrete optical signal set. If the deviation exceeds the threshold, a secondary warning is triggered. Encrypt BIDI optical module feature information and ensure data integrity through blockchain distributed storage.

7. The optical line protection switching device of a BIDI system according to claim 1, characterized in that: Environmental sensing unit, used to collect temperature, humidity and electromagnetic interference intensity parameters; A dynamic threshold calculation unit, configured to generate a dynamic switching threshold according to environmental parameters; Anti-interference hardware module, including double-layer shielding structure and differential wiring PCB; Hybrid model analysis unit, used to perform CNN feature extraction and curvature analysis; The switching decision unit is used to trigger line switching within 15ms when the comprehensive score exceeds the threshold; The signal encryption unit is used to perform AES-256 encryption and distributed storage on the optical module's characteristic information.

8. An electronic device comprising a bus, a processor, a memory, and a computer program stored in the memory, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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