Digital twin model parameter updating method applied to optical cable

Abstract

The invention relates to the technical field of optical fiber communication, in particular to a digital twin model parameter updating method applied to an optical cable. The method comprises the following steps: collecting optical fiber laying data; performing laying structure division based on the optical fiber laying data to obtain flat structure optical fiber data and layer-stranded optical fiber data; evaluating the tensile strength of the optical cable based on flat structure optical fiber data; detecting the optical signal transmission intensity of the flat-structure optical fiber data based on the tensile property of the optical cable; performing intelligent loss compensation on the flat structure optical fiber data according to the optical signal transmission intensity to obtain signal compensation data; calculating the distribution uniformity based on the layer-stranded optical fiber data; determining optical fiber dislocation based on the distribution uniformity to obtain optical fiber dislocation data; and performing optical fiber Brillouin scattering detection based on the optical fiber dislocation data to obtain Brillouin scattering data. The accuracy and real-time performance of optical cable performance evaluation are improved based on the optical fiber communication technology, and therefore the stability and reliability of optical cable signal transmission are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of optical fiber communication, and particularly relates to a method for updating parameters of a digital twin model applied to an optical cable. Background Art

[0002] As an important communication transmission medium, the performance of an optical cable directly affects the transmission quality and stability of information. An optical cable usually consists of multiple optical fiber units, which are arranged in different structural forms and have key technical characteristics such as tensile strength, signal transmission strength, and anti-interference ability. The digital twin model for an optical cable mainly creates a digital copy of the optical cable through virtual simulation technology to monitor and optimize the performance of the optical cable in real time. Usually, it relies on manual or basic sensors for data collection, resulting in limited accuracy and coverage of data collection, making it difficult to comprehensively and accurately obtain the key parameters of the optical cable, especially in complex or inaccessible areas. Model updates in traditional methods are often carried out periodically and cannot achieve real-time updates, which leads to a large deviation between the model and the actual performance of the optical cable, affecting the accuracy and timeliness of prediction. Minor damages or changes in the optical cable (such as fiber misalignment, cracks, etc.) cannot be captured by traditional methods, resulting in incomplete evaluation of the optical cable performance. Traditional methods often only analyze a single optical cable characteristic (such as tensile strength, signal loss), ignoring the comprehensive influence of environmental factors such as temperature, humidity, and external mechanical stress, and cannot accurately reflect the comprehensive performance of the optical cable. Traditional digital twin models usually adopt simplified mathematical and physical models, ignoring complex factors such as nonlinear effects and local stress distribution, resulting in insufficient accuracy of the model. The actual operating environment of the optical cable is complex, and traditional methods are difficult to cope with dynamic changes such as external physical interference and natural aging, resulting in the model not being reliable and effective in practical applications. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for updating parameters of a digital twin model applied to an optical cable to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for updating parameters of a digital twin model applied to an optical cable includes the following steps:

[0005] Step S1: Collect optical fiber laying data; based on the optical fiber laying data, perform laying structure division to obtain flat structure optical fiber data and stranded optical fiber data;

[0006] Step S2: Evaluate the tensile strength of the optical cable based on the flat structure optical fiber data; detect the optical signal transmission strength of the flat structure optical fiber data based on the tensile strength of the optical cable; perform intelligent loss compensation on the flat structure optical fiber data according to the optical signal transmission strength to obtain signal compensation data;

[0007] Step S3: Calculate the distribution uniformity based on the stranded optical fiber data; determine the optical fiber misalignment based on the distribution uniformity to obtain optical fiber misalignment data; perform optical fiber Brillouin scattering detection based on the optical fiber misalignment data to obtain Brillouin scattering data; improve the stranding parameters of the stranded optical fiber data based on the Brillouin scattering data;

[0008] Step S4: Transmit the signal compensation data and the stranding parameters to the optical cable digital twin model and predict the optical cable signal attenuation degree; update the parameters of the optical cable digital twin model according to the optical cable signal attenuation degree to generate digital twin model update parameters.

[0009] By collecting the optical fiber laying data and dividing the laying structure, the present invention can effectively obtain the structural characteristics of the optical cable and realize the detailed analysis of the flat structure optical fiber and the stranded optical fiber, which provides accurate basic data for subsequent performance evaluation. Based on the structural division of the optical fiber laying data, it can ensure that different types of optical fiber data are processed independently and accurately, avoiding errors caused by data mixing, thereby providing reliable input for the optical cable performance evaluation. During the evaluation of the tensile property of the optical cable, the strength and durability of the optical cable are analyzed in detail, which not only helps to optimize the design of the optical cable but also provides a theoretical basis for the tensile property test in practical applications. The detected optical signal transmission intensity can monitor the actual transmission state of the optical cable in real time, thereby timely discovering the optical fiber loss situation and optimizing the optical signal intensity through intelligent loss compensation. This process ensures the stability of the signal quality and enhances the adaptability of the optical cable under different environmental conditions. Calculating the distribution uniformity of the stranded optical fiber can identify the misalignment problem in the optical fiber, and then obtain accurate optical fiber misalignment data through Brillouin scattering detection. Based on these data, the stranding structure of the optical fiber can be effectively optimized, avoiding signal loss or performance degradation caused by misalignment during long-term use of the optical fiber. This analysis can improve the stability and reliability of the optical cable during long-term operation. Transmitting the signal compensation data and the optimized stranding parameters to the digital twin model of the optical cable, combined with the real-time predicted signal attenuation degree, can dynamically monitor and adjust the state of the optical cable. This process can realize the real-time update of the optical cable digital twin model, making it always match the actual operation state of the optical cable, reducing the lag problem of traditional periodic updates. Through this method, the optical cable can be continuously optimized and adjusted during operation, ensuring that the optical cable can still maintain the best performance when facing complex environments or usage conditions, reducing the performance deviation caused by traditional update methods. Description of the Drawings

[0010] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0011] Figure 1Schematic diagram of the step process of a method for updating parameters of a digital twin model applied to an optical cable according to the present invention;

[0012] Figure 2 Schematic diagram of the detailed step process of step S1 in the present invention;

[0013] Figure 3 Schematic diagram of the detailed step process of step S2 in the present invention;

[0014] The implementation, functional features and advantages of the object of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0015] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative work based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0016] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0017] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0018] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a method for updating parameters of a digital twin model applied to an optical cable, and the method includes the following steps:

[0019] Step S1: Collect fiber laying data; perform laying structure division based on the fiber laying data to obtain flat structure fiber data and stranded fiber data;

[0020] In this embodiment, during the optical cable laying process, a high-precision optical fiber laying monitoring system is used to collect optical fiber laying data. The system includes an inertial measurement unit (IMU), a laser ranging sensor, and a high-frequency imaging device to obtain the three-dimensional spatial position information, laying path, and surrounding environment information of the optical cable. The optical fiber laying data mainly includes parameters such as the laying method, bending radius, mechanical tension, and laying angle of the optical fiber. Based on the collected data, a clustering analysis method is used to divide the laying structure. First, the K-means clustering algorithm is used to classify the morphology of the outer sheath of the optical cable and the distribution morphology of the optical fibers to distinguish between flat-structured optical fibers and stranded optical fibers. For flat-structured optical fiber data, parameters such as the arrangement spacing, parallelism, and tensile tension of the optical fibers are mainly extracted; for stranded optical fiber data, characteristics such as the winding angle, number of stranding turns, and winding tension of the optical fibers are extracted. By statistically analyzing the variation laws of various parameters during the laying process of different types of optical fibers, the laying structure type is determined and stored in the optical fiber database.

[0021] Step S2: Evaluate the tensile strength of the optical cable based on the flat-structured optical fiber data; detect the optical signal transmission intensity of the flat-structured optical fiber data based on the tensile strength of the optical cable; perform intelligent loss compensation on the flat-structured optical fiber data according to the optical signal transmission intensity to obtain signal compensation data;

[0022] In this embodiment, for the flat-structured optical fiber data, a tensile test bench is used to evaluate the tensile strength of the optical cable. Specifically, after fixing both ends of the optical cable, a tensile force is applied, and a precision stress sensor (resolution 0.01 N) is used to measure the force on the optical fiber under different tensile loads, and the optical signal transmission intensity is recorded in combination with an optical fiber Brillouin optical time domain analyzer (BOTDA). An optical signal attenuation curve is obtained through experiments to analyze the influence of optical fiber stretching on signal transmission and calculate the signal loss degree. According to the variation trend of the optical signal attenuation, a least squares curve fitting method is used to establish a loss compensation model, and optical signal compensation is performed based on an intelligent compensation algorithm. Specifically, on the basis of the known tensile strength and loss curve, high-frequency noise is removed using Fourier transform, and the optical signal is dynamically compensated in combination with an adaptive filtering algorithm (Kalman filter) to ensure that the intensity of the compensated optical signal meets the transmission standard. Finally, signal data after intelligent loss compensation is generated and stored in the database.

[0023] Step S3: Calculate the distribution uniformity based on the stranded optical fiber data; determine the fiber misalignment based on the distribution uniformity to obtain fiber misalignment data; perform optical fiber Brillouin scattering detection based on the fiber misalignment data to obtain Brillouin scattering data; improve the stranding parameters of the stranded optical fiber data based on the Brillouin scattering data;

[0024] In this embodiment, for the stranded optical fiber data, an optical fiber distributed deformation detection system is used to calculate the uniformity of the optical fiber distribution. Using optical interference measurement technology, the winding pitch of the optical fiber is extracted through the change of the interference light intensity, and the optical fiber distribution signal is decomposed based on wavelet transform to obtain the winding uniformity distribution data. Based on the winding uniformity data, a template matching algorithm is used to detect the misalignment of the optical fiber. Specifically, a standard optical fiber winding distribution model is constructed, and the cross-correlation matching algorithm is used to calculate the similarity between the current optical fiber winding structure and the standard model. If the similarity is lower than the set threshold (for example, 0.85), it is determined that there is an optical fiber misalignment, and the spatial position information of the misaligned section is extracted to generate optical fiber misalignment data. The Brillouin optical time domain analysis technology (BOTDA) is used to perform Brillouin scattering detection on the optical fiber misalignment data. This technology injects pulsed laser into the optical fiber and records the scattered light signal to analyze the internal stress change of the optical fiber. By calculating the Brillouin frequency shift parameter, the internal stress distribution of the optical fiber is obtained, and the stranding parameters of the stranded optical fiber are optimized based on the stress distribution, including the adjustment range of the winding angle (for example, ±2°) and the adjustment amplitude of the winding tension (for example, ±5%), to ensure the structural stability of the optical cable.

[0025] Step S4: Transmit the signal compensation data and the stranding parameters to the optical cable digital twin model, and predict the signal attenuation degree of the optical cable; update the parameters of the optical cable digital twin model according to the signal attenuation degree of the optical cable to generate the updated parameters of the digital twin model.

[0026] In this embodiment, the signal compensation data and the stranding parameters are input into the optical cable digital twin model, and the finite element analysis method is used to simulate the stress state of the optical cable structure. The optical signal transmission simulation technology (based on the ray tracing algorithm) is used to simulate the propagation path of the optical signal under different laying environments and calculate the signal attenuation degree of the optical cable. The calculation formula of the optical signal attenuation degree is as follows:

[0027]

[0028] where P input is the signal power at the input end of the optical cable, and P output is the signal power at the output end of the optical cable. The simulation parameters are adjusted according to different environmental conditions (such as temperature change, mechanical stress) to improve the prediction accuracy.

[0029] Preferably, step S1 is specifically:

[0030] Step S11: Collect the optical fiber laying data;

[0031] In this embodiment, during the optical cable laying process, a fiber optic distributed sensing system (DAS, Distributed Acoustic Sensing) and a lidar scanning device are used to synchronously collect optical fiber laying data. The fiber optic distributed sensing system modulates an optical signal and propagates it along the optical fiber, and in the laying process, it can obtain information such as the spatial placement form, vibration frequency, ambient temperature, and strain distribution of the optical cable in real time. The lidar scanning device uses point cloud data to record the three-dimensional coordinates of the optical fiber and identifies the arrangement pattern of the optical fiber along the path through computer vision algorithms. During the data collection process, the sampling frequency of the DAS system is set to 10 kHz to ensure sufficient spatial resolution (usually 1 m). The resolution of the lidar point cloud data is set to 5 mm to ensure the accuracy of the optical fiber path information. All the collected data is stored in a database, and a time stamp and a spatial index are attached for subsequent data processing.

[0032] Step S12: Extract the optical fiber arrangement pattern based on the optical fiber laying data; extract the optical fiber routing space based on the optical fiber laying data;

[0033] In this embodiment, based on the optical fiber laying data, Fourier transform and wavelet analysis methods are used to extract the optical fiber arrangement pattern. First, the Fourier transform is used to extract the periodic characteristics of the optical fiber arrangement and analyze the distribution form of the optical fiber along the length direction. Subsequently, the wavelet analysis method is applied to decompose the spatial signal of the optical fiber to extract the local arrangement pattern of the optical fiber and determine whether there are characteristics of parallel arrangement or spiral winding arrangement. For the optical fiber routing space, a point cloud data clustering algorithm (DBSCAN, Density-Based Spatial Clustering of Applications with Noise) is used to analyze the spatial distribution characteristics of the optical fiber laying area. The density threshold of the DBSCAN algorithm is set to at least 10 point cloud data per cubic centimeter to ensure accurate identification of the density difference of the routing space. By calculating the spacing and arrangement pattern between the optical fibers, the spatial attributes of the optical fiber routing are marked.

[0034] Step S13: Construct a planar arrangement structure according to the optical fiber arrangement pattern; construct a spiral arrangement structure according to the optical fiber arrangement pattern;

[0035] In this embodiment, after the optical fiber arrangement pattern is extracted, structural modeling is performed for the identified different arrangement patterns. For the planar arrangement structure, the Delaunay triangulation method is used to grid the optical fibers on a two-dimensional plane, and the arrangement spacing and the optical fiber arrangement angle between the optical fibers are calculated (the set spacing standard is ±0.1 mm, and the arrangement angle error does not exceed ±2°). For the spiral arrangement structure, a spiral parameter fitting method is used to calculate the spiral winding angle, the number of winding turns, and the pitch according to the three-dimensional coordinate data of the optical fiber. The calculation formula for the winding angle is as follows:

[0036]

[0037] Among them, h is the helical rising height and r is the winding radius. The pitch is set within the range of 5 mm to 10 mm, and the winding angle error is controlled within ±3°. Finally, a planar arrangement structure and a helical arrangement structure are constructed and stored in the database.

[0038] Step S14: Identify the spacious cabling space in the optical fiber cabling space; identify the high-density cabling space in the optical fiber cabling space;

[0039] In this embodiment, based on the optical fiber cabling space data, the voxel-based segmentation method is used to classify the cabling space. First, the optical fiber cabling space is divided into three-dimensional voxel grids with equal spacing (the grid size is set to 1 cm 3 ), and then the number of optical fibers in each voxel unit is counted. For the spacious cabling space, the determination threshold is set to less than 3 optical fibers per cubic centimeter, and the average spacing between the optical fibers is calculated to ensure that it is more than 5 mm. For the high-density cabling space, the determination threshold is more than 5 optical fibers per cubic centimeter, and at the same time, the minimum spacing between the optical fibers is calculated to ensure that it is less than 2 mm. Based on these data, the cabling space is classified into a spacious cabling space and a high-density cabling space, and their space coordinates and cabling density parameters are recorded.

[0040] Step S15: Determine the flat structure optical fiber type according to the planar arrangement structure and the spacious cabling space to obtain flat structure optical fiber data;

[0041] In this embodiment, based on the identified optical fiber arrangement structure and cabling space, the data that meets the characteristics of the flat structure optical fiber is screened. First, the optical fibers that meet the planar arrangement structure are extracted, and the cabling space where they are located is retrieved. If the cabling space is marked as a spacious cabling space, then the optical fiber is determined to be a flat structure optical fiber. Calculate the maximum spacing between the optical fibers and ensure that the spacing is more than 5 mm; check the optical fiber arrangement direction to ensure that its relative angle error is less than ±2°; count the number of layers of the optical fibers and ensure that the number of optical fibers placed in a single layer is not less than 5. The optical fiber data that meets the above conditions is marked as flat structure optical fiber data and stored in the database.

[0042] Step S16: Determine the stranded optical fiber type according to the helical arrangement structure and the high-density cabling space to obtain stranded optical fiber data.

[0043] In this embodiment, for the fiber optic data with a spiral arrangement structure, the stranded fiber optic type is determined in combination with the fiber optic cabling space classification result. First, the fiber optic data with spiral arrangement characteristics is extracted, and the cabling space where it is located is matched. If the cabling space is marked as a high-density cabling space, the fiber optic data is classified as stranded fiber optic data. The determination criteria include the following points: the spiral winding angle needs to be maintained between 30° and 45°; the pitch needs to be between 5 mm and 10 mm; the number of winding layers needs to be greater than 2 layers; the number of single-layer optical fibers is not less than 3; the maximum distance between optical fibers needs to be less than 2 mm. The fiber optic data that meets all the above conditions is marked as stranded fiber optic data and stored in the database for subsequent analysis of the optical cable digital twin model.

[0044] Preferably, step S2 is specifically as follows:

[0045] Step S21: Identify the cross-sectional shape of the optical fiber based on the flat-structured optical fiber data;

[0046] In this embodiment, it is necessary to use a high-resolution industrial CT scanning device to scan the cross-section of the optical fiber. The resolution of the industrial CT is set to not less than 1 μm to ensure obtaining the complete microscopic structure of the optical fiber. The data scanned by the CT is stored in the DICOM format and three-dimensional volume data reconstruction is performed for subsequent morphological analysis. For the obtained CT images, first, grayscale processing is performed, and the Gaussian filtering algorithm is used to remove noise. The filtering window is set to 3×3 pixels to ensure edge clarity. Then, the Canny edge detection algorithm is used to extract the outer contour of the optical fiber cross-section, and the Otsu threshold segmentation method is combined to ensure clear boundaries. After the contour is extracted, the Hough transform is used to detect its shape type. If it is an ellipse, the major axis length, minor axis length, and elliptic eccentricity are calculated; if it is a rectangle, the side length ratio and area ratio are calculated; if it is an irregular polygon, the Marching Squares algorithm is used for isocontour fitting, and the least squares method is used to calculate the fitting error to ensure accurate contour data. All the calculated shape parameters are stored in the database and indexed according to the optical fiber number for subsequent material analysis and modeling.

[0047] Step S22: Determine the cross-sectional material of the optical fiber according to the cross-sectional shape of the optical fiber; draw the cross-sectional material distribution map of the optical fiber according to the cross-sectional material; calculate the material stress based on the cross-sectional material of the optical fiber;

[0048] In this embodiment, after the cross-sectional shape of the optical fiber is determined, it is necessary to analyze the composition of the optical fiber material using an energy-dispersive X-ray spectrometer (EDS). The energy resolution of the EDS is set to 129 eV, and the scanning range is set to 0-20 keV to detect the main elements in the optical fiber material, including silicon (Si), fluorine (F), phosphorus (P), germanium (Ge), oxygen (O), etc. Peak fitting is performed on the scanned spectral curve, and it is compared with the material standard database to determine the material category of the optical fiber cross-section, such as silica-based optical fiber, fluoride optical fiber, or plastic coating. After the material is determined, the nearest neighbor interpolation method is used to partition and calibrate different material regions of the optical fiber cross-section based on the spectral data, and different material distributions are encoded using pseudo-color. For example, red represents the silica-based core material, blue represents the fluoride coating, and green represents the polymer coating. Finally, a material distribution map of the optical fiber cross-section is generated and stored in PNG format. Based on the material data of the optical fiber cross-section, the finite element analysis (FEA) method is used to calculate the material stress, a finite element model of the optical fiber is constructed, and material parameters are set. For example, the Young's modulus of the silica-based optical fiber is 73 GPa, the Poisson's ratio is 0.17, the Young's modulus of the fluoride optical fiber is 60 GPa, the Poisson's ratio is 0.21, and the Young's modulus of the polymer coating is 2 GPa, the Poisson's ratio is 0.4. An axial tensile force is applied to the optical fiber cross-section, set to 100 N, and the finite element solver is used to calculate the stress distribution in each material region and store the stress data for subsequent analysis.

[0049] Step S23: Map the material stress to the material distribution map of the optical fiber cross-section to generate a cross-sectional material stress distribution map;

[0050] In this embodiment, first, normalization is performed to map the stress value to the range of [0,1] for subsequent visual analysis. The bilinear interpolation method is used to map the stress data space to the material distribution map of the optical fiber cross-section so that the stress value accurately matches the material distribution region. Pseudo-color coding is performed using Matplotlib or MATLAB to visually generate the material stress distribution map of the optical fiber cross-section, and the color mapping scheme is set. For example, red represents the high-stress region, and blue represents the low-stress region. The generated stress gradient map can intuitively reflect the stress concentration trend. Finally, the stress distribution map is stored in PNG format and indexed to the optical fiber database for subsequent analysis.

[0051] Step S24: Identify the stress concentration region in the cross-sectional material stress distribution map; evaluate the tensile strength of the optical cable based on the stress concentration region;

[0052] In this embodiment, the threshold segmentation method is used to identify the stress concentration area. The stress concentration determination threshold is set to 80% of the material yield strength. For example, if the yield strength of the silicon-based optical fiber is 5 GPa, the threshold is set to 4 GPa. The region growing algorithm is used to detect the area exceeding this threshold, and the coordinates, area, and shape of the stress concentration area are marked. Based on the distribution of the stress concentration area, the tensile limit of the optical cable is calculated. The tensile experiment is used to simulate the deformation of the optical cable under different stress states, and the finite element analysis is combined to calculate the maximum load when the optical cable breaks. The experimental data is stored in the database and indexed by the optical fiber number for subsequent optical fiber loss analysis.

[0053] Step S25: Based on the tensile property detection of the optical cable, the optical signal transmission intensity of the flat structure optical fiber data is detected;

[0054] In this embodiment, the optical time-domain reflectometer (OTDR) is used to detect the optical signal transmission intensity of the flat structure optical fiber. The test wavelength of the OTDR is set to 1550 nm, and the sampling interval is set to 10 cm to ensure the accuracy of the optical fiber signal attenuation data. During the detection process, a laser signal with a standard power (set to 0 dBm) is input into the optical fiber, the backscattered signal during the transmission along the optical fiber is recorded, and the optical power loss at different positions is calculated. If the optical signal attenuation at a certain measurement point exceeds the set threshold (for example, greater than 0.5 dB / km), it is marked as an optical signal weakening point, and the measurement point coordinates and loss value are stored. The detection data is stored in the database and compared with the optical cable tensile property evaluation result to analyze the correlation between the stress concentration area and the signal loss.

[0055] Step S26: The optical signal transmission intensity is used to perform intelligent loss compensation on the flat structure optical fiber data to obtain signal compensation data.

[0056] In this embodiment, based on the historical optical fiber transmission data, an optical signal loss prediction model is constructed. The input features include optical fiber material, stress concentration area coordinates, optical signal attenuation rate, etc., and the output result is the optical signal loss compensation strategy. The loss compensation uses the dynamic gain adjustment method. Near the optical signal weakening point, the gain of the optical amplifier (EDFA) is adjusted, and the compensation amplitude is calculated according to the loss measurement value. For example, if the optical signal attenuation at a certain point is 0.6 dB / km, the compensation gain is set to +0.6 dB. In addition, for the severely lossy area, the optical fiber mode field diameter (MFD) optimization strategy is adopted to reduce the mode mismatch loss by adjusting the mode field distribution of the incident laser. Finally, all loss compensation data is stored in the database and synchronously updated with the optical cable digital twin model to ensure the accuracy of the subsequent optical cable state evaluation.

[0057] Preferably, step S25 is specifically as follows:

[0058] Step S251: Determine the low-tensile-strength optical fiber segments of the flat-structured optical fiber data based on the tensile strength of the optical cable;

[0059] In this embodiment, based on the tensile performance parameters of the optical cable, first collect the optical cable structure data, including the number of optical fiber cores, sheath material, type and arrangement of strengthening members, and conduct a tensile test on the optical cable. Use an electronic universal testing machine to clamp the optical cable sample, apply an axial tensile force at both ends of the optical cable, set the tensile rate to 1 mm / min, record the load change and strain curve during the tensile process of the optical cable, and calculate the overall tensile limit of the optical cable. Subsequently, use an optical fiber Brillouin time-domain analyzer (BOTDA) to conduct distributed strain measurement on the optical fibers inside the optical cable, configure the light source wavelength to 1550 nm, and set the sweep frequency range to 10 MHz to 13 GHz to obtain the local strain values of the optical fibers. For flat-structured optical fibers, collect the strain data along the axial direction of the optical fiber, compare with the yield strength of the optical fiber material, calculate the strain gradient through difference calculation to identify the local strain mutation regions. Set a low-tensile threshold, calculate the low-tensile limit value based on the Young's modulus and yield strength of the silicon-based optical fiber, and screen out the optical fiber segments with local strain exceeding this threshold, record the start and end coordinates, strain values and corresponding stress states of the optical fiber segments, mark these optical fiber segments as low-tensile-strength optical fiber segments, and store them in the database for subsequent tensile load simulation analysis.

[0060] Step S252: Conduct a tensile load simulation on the low-tensile-strength optical fiber segments to obtain tensile load simulation data;

[0061] In this embodiment, based on the low-tensile-strength optical fiber segments, construct a three-dimensional finite element analysis model to simulate and calculate the tensile load of the optical fiber. Use finite element analysis software to conduct mesh division on the optical fiber structure, adopt an eight-node solid element (C3D8R), and set the unit mesh size to 0.1 mm to ensure the accuracy of the simulation calculation. Set the boundary conditions, fix both ends of the optical fiber, apply a tensile load along the axial direction, and set the load range to 100 N to 500 N, increasing by 10 N each step to simulate the stress-strain changes of the optical fiber under different load conditions. Adopt an explicit dynamics solution method to calculate the stress distribution inside the optical fiber, and extract the maximum principal stress, principal strain distribution, and the axial deformation amount of the optical fiber under the maximum tensile load. During the simulation process, focus on the stress concentration regions of the low-tensile-strength optical fiber segments, and store the stress-strain data under different load conditions for subsequent anti-fatigue state analysis.

[0062] Step S253: Identify the anti-fatigue state of the optical fiber based on the tensile load simulation data;

[0063] In this embodiment, it is necessary to extract the maximum principal stress values under each load from the stress-strain curve of the optical fiber. These data are the stress distribution of the optical fiber under different tensile conditions obtained through finite element analysis, which can accurately reflect the local and overall stress states of the optical fiber during the stress application process. The simulation data for each tensile step includes the stress distribution of the optical fiber, especially the stress concentration region in the low-tensile-strength optical fiber section. Through these data, the maximum principal stress value of the optical fiber and its location under different tensile loads can be obtained. Combining the fatigue limit of the material used for the optical fiber (for example, the fatigue strength threshold of silica optical fiber), a fatigue life calculation method based on the S-N curve is used to evaluate the anti-fatigue state of the optical fiber. The S-N curve is obtained through experiments and is used to describe the fatigue behavior of the optical fiber under different stress levels per unit cycle. By analyzing the maximum principal stress value of each optical fiber section and comparing the stress value with the corresponding fatigue life based on the data in the S-N curve, the fatigue life of each optical fiber section can be obtained. This calculation process takes into account factors such as the material properties of the optical fiber, stress level, and changes in cyclic load. For the obtained fatigue life, if the calculated life of a certain optical fiber section is less than 10 5 cycles, it can be determined that this optical fiber section is a high-fatigue-risk area. Specifically, when the fatigue life of the optical fiber section is lower than this threshold, it means that this optical fiber section is very likely to experience performance degradation due to fatigue failure during actual use. Therefore, these optical fiber sections need to be marked, and their coordinate positions, fatigue life, and maximum principal stress values in the entire optical cable are recorded. To accurately evaluate the anti-fatigue state of the optical fiber, especially at the microscopic level, the initial state of surface microcracks also needs to be analyzed. The surface of the optical fiber is observed through instruments such as a scanning electron microscope (SEM) to detect whether there are initial cracks or microcracks, which are usually early signs of fatigue damage. Combining the stress analysis results, if there are microcracks on the surface of the optical fiber and the crack position is exactly in the maximum principal stress concentration region, it means that the fatigue life of this optical fiber section will be shorter, increasing the risk of crack propagation and rupture. All the information of the optical fiber sections obtained from the analysis, including coordinates, fatigue life, maximum principal stress value, and microcrack state, will be recorded and stored for predicting the crack initiation point and simulating crack propagation in subsequent steps. Through this comprehensive analysis, high-fatigue-risk areas can be accurately identified, providing a basis for subsequent crack propagation analysis and optical fiber performance evaluation.

[0064] Step S254: Predict the crack initiation point according to the anti-fatigue state of the optical fiber;

[0065] In this embodiment, the stress intensity factor of the optical fiber surface is calculated, and combined with the size of the microcracks on the optical fiber surface, the possibility of crack initiation is analyzed. A scanning electron microscope (SEM) is used to analyze the microscopic morphology of the optical fiber surface to observe whether there are microcracks and measure the initial size of the microcracks. Combining the stress intensity calculation results, the fracture toughness threshold of the optical fiber material is compared. If the stress intensity in a certain area exceeds the material fracture toughness, then this area is determined as the crack initiation point, and the initial crack size and position coordinates are recorded for crack propagation analysis.

[0066] Step S255: Based on the crack initiation point, crack propagation is carried out to obtain crack propagation data;

[0067] In this embodiment, based on the crack initiation point, the extended finite element method (XFEM) is used to simulate the crack propagation process. A crack propagation model is constructed, with the crack initiation point as the initial crack position, and cyclic loading conditions are applied to simulate the growth of the crack under repeated tensile loads. The crack propagation calculation adopts the Paris law, calculates the crack growth rate according to the change in crack size, and compares the crack propagation paths under different load conditions. A non-contact optical interferometer is used to measure the crack propagation length, and the crack propagation path, crack length increment, crack propagation rate, and final crack size are recorded. All crack propagation data are stored in the database and indexed to the optical fiber number for subsequent evaluation of the optical signal transmission intensity.

[0068] Step S256: Evaluate the optical signal transmission intensity of the low-tensile optical fiber section according to the crack propagation data.

[0069] In this embodiment, based on the crack propagation data, the optical fiber scattering analysis method is used to evaluate the optical signal transmission intensity. An optical fiber Rayleigh scattering analyzer (OBR) is used to test the signal loss in the crack area. The test wavelength is set to 1550 nm, and the measurement resolution is set to 10 μm to accurately detect the local optical loss at the crack. An optical power monitoring module is used to record the optical signal intensity in the crack area and calculate the optical loss coefficient. For the optical fiber section with a loss coefficient exceeding 0.5 dB / km, it is marked as a high-loss section, and the loss data are stored in the database for subsequent optical fiber signal compensation analysis.

[0070] Preferably, step S26 is specifically:

[0071] Step S261: Determine the signal attenuation degree according to the optical signal transmission intensity;

[0072] In this embodiment, it is necessary to measure the transmission intensity of the optical signal in the optical fiber. This process is usually carried out by an optical power meter or an optical time domain reflectometer (OTDR). The OTDR measures the signal attenuation in the optical fiber by transmitting an optical signal with a known intensity and detecting the returned reflected signal. In this step, the obtained optical signal transmission intensity value is used to evaluate the degree of signal attenuation. The degree of attenuation can be determined by comparing the difference between the transmission intensity and the original input optical intensity. Usually, a relatively standard optical fiber transmission performance measurement standard (for example, ITU-T G.652) is used to evaluate the attenuation of the optical signal, specifically calculating the transmission loss (dB / km) and comparing it with the specified standard. According to this data, it can be confirmed whether the optical signal meets the performance requirements and whether there is abnormal attenuation.

[0073] Step S262: Statistically analyze the high-attenuation signal data based on the degree of signal attenuation;

[0074] In this embodiment, according to the degree of signal attenuation, the signal data with attenuation exceeding the set threshold is statistically analyzed and screened. The set attenuation threshold is usually based on the design standard of the optical fiber. For example, an optical fiber section with an attenuation rate exceeding 0.3 dB / km is considered to have a high attenuation risk. In this step, first, the attenuation data of all optical fiber sections are traversed, and the optical fiber sections with higher attenuation are screened out through the threshold. The signal attenuation degree of these optical fiber sections is usually greater than the maximum value of normal transmission attenuation. Record the attenuation data of these optical fiber sections and classify them as "high-attenuation signal data". The high-attenuation signal data should include information such as the attenuation degree, the position of the optical fiber section, and the optical signal intensity, providing a basis for subsequent optical power adjustment and the deployment of gain optical amplifiers.

[0075] Step S263: Determine the high-attenuation signal optical fiber sections of the flat-structured optical fiber data according to the high-attenuation signal data;

[0076] In this embodiment, according to the high-attenuation signal data, the specific high-attenuation optical fiber sections are analyzed and determined. The specific position, attenuation value, and the related signal transmission intensity data of each optical fiber section are recorded in detail. These optical fiber sections usually show relatively large transmission losses, which are related to the optical fiber material, laying method, environmental factors, etc. By comparing the attenuation value of each optical fiber section with the set threshold, the high-attenuation optical fiber sections can be marked. For each high-attenuation optical fiber section, obtain its exact position and attenuation value, which is convenient for signal power adjustment and gain compensation in subsequent steps. The marked high-attenuation signal optical fiber sections also provide a reference for the subsequent deployment of gain optical amplifiers.

[0077] Step S264: Adjust the input optical power based on the high-attenuation signal optical fiber sections;

[0078] In this embodiment, by analyzing the attenuation value of the high-attenuation optical fiber section, the degree of increasing the input optical power is determined. The adjustment of the input optical power is calculated based on the specific attenuation data of the high-attenuation signal optical fiber section. Generally, the input optical power can be increased to compensate for the attenuation, so as to ensure the signal quality. The goal of the adjustment is to ensure that the optical signal does not cause signal loss or serious distortion due to excessive attenuation during transmission. The specific adjustment amplitude depends on the attenuation data and the design standards of the system. For example, if the attenuation of a certain optical fiber section is 0.5 dB / km, 10% of the input power needs to be increased to ensure that the signal intensity meets the standard requirements.

[0079] Step S265: Lay a gain optical amplifier based on the high-attenuation signal optical fiber section; identify the pump light source according to the gain optical amplifier; excite erbium ions based on the pump light source; amplify the signal photons of the high-attenuation signal optical fiber section according to the erbium ions; determine the signal increase intensity according to the signal photons;

[0080] In this embodiment, a gain optical amplifier is a device used in an optical fiber communication system. It compensates for signal attenuation caused by transmission loss by amplifying the optical signal during transmission. The working principle of the gain optical amplifier is based on optical amplification technology. Generally, optical fiber materials doped with rare earth elements (such as erbium, thulium, etc.) are used. Under the excitation of a pump light source with a specific wavelength, the energy conversion mechanism of these rare earth ions is utilized to amplify the signal light. Common gain optical amplifiers include erbium-doped fiber amplifiers (EDFAs) and thulium-doped fiber amplifiers (TDFAs). According to the distribution and attenuation degree of the high-attenuation signal fiber section, the installation position of the gain optical amplifier is determined. Specifically, first, identify the areas in the optical fiber transmission path where the signal attenuation is relatively severe, that is, those fiber sections with attenuation exceeding a predetermined threshold. The specific calculation of the attenuation value can be obtained by analyzing the power change of the signal. Usually, a power monitoring device or a spectrum analyzer is used to obtain the attenuation value of this section of the optical fiber. Then, a gain optical amplifier (for example, an erbium-doped fiber amplifier EDFA) is selected as the amplification device. The installation position of the gain optical amplifier is usually before and after the fiber section with relatively severe signal attenuation, that is, between the starting point of the optical fiber and the area with higher attenuation, to ensure sufficient enhancement during signal transmission. The installation position of the gain optical amplifier should take into account the optical fiber layout and the gain coverage range to ensure that it covers the high-attenuation area and effectively compensates for the loss. According to the selected type of gain optical amplifier, the specifications and operating parameters of the pump light source are determined. The pump light source needs to select a suitable wavelength according to the requirements of the gain optical amplifier. Usually, a laser light source with a wavelength of 980 nm or 1480 nm is selected. These wavelengths can efficiently excite the erbium ions in the erbium-doped fiber. During this process, the power and output wavelength of the pump light source must be optimized to ensure the efficiency of the excitation process. Usually, the selected power range will be adjusted according to the length of the optical fiber, the transmission distance, and the attenuation degree to ensure that the erbium ions can reach the optimal excitation state and amplify the signal photons during transmission. By optimizing the power and wavelength settings of the pump light source, it is ensured that the gain optical amplifier can effectively amplify the high-attenuation signal passing through the optical fiber, so that the optical signal is fully enhanced after passing through the amplifier and can continue to be transmitted to the downstream fiber section, ensuring that the signal quality is not affected by attenuation.

[0081] Step S266: Perform intelligent loss compensation on the flat structure optical fiber data according to the signal increase intensity and the input optical power to obtain signal compensation data.

[0082] In this embodiment, intelligent loss compensation is implemented by measuring the signal intensity amplified by the gain optical amplifier and combining the adjusted input optical power data in the previous steps. In specific operations, first, the signal intensity after passing through the gain optical amplifier needs to be obtained through a real-time optical signal monitoring system installed on the optical fiber path. These signal intensity data provide the actual condition of the amplified signal and reflect the enhancement effect of the gain optical amplifier on the optical signal. At the same time, the data of the input optical power needs to be monitored and recorded, which are usually obtained through a power meter at the input end or other optical power measurement devices. Then, based on these signal intensity and input optical power data, a compensation algorithm model is established. This model combines the losses in optical fiber transmission, the gain effect of the gain optical amplifier, and other factors affecting signal quality to calculate the required loss compensation amount in real time. The key to the compensation algorithm model is to dynamically adjust according to the real-time optical fiber transmission state, signal attenuation, the effect of the gain optical amplifier, and external environmental factors (such as temperature, humidity, etc.). In actual operations, the model continuously updates the compensation parameters according to real-time data (such as the attenuation coefficient of the optical fiber segment, the output power of the amplifier, etc.). These compensation parameters include the signal amplification factor, the power of the pump light source, the signal attenuation compensation coefficient, etc. When a high attenuation occurs during transmission, the compensation algorithm automatically adjusts the compensation value, such as increasing the power of the pump light source or adjusting the amplification factor of the gain optical amplifier, to ensure that the signal intensity can reach the optimal transmission level. During the compensation process, the algorithm performs real-time correction on the signal intensity of each section of the optical fiber, reduces signal loss caused by attenuation and other external factors (such as temperature changes, mechanical stress, etc.) by increasing the transmission efficiency of the compensation signal. During this process, the hardware of the system (such as the optical power monitor, the amplifier control system, etc.) needs to be adjusted to ensure that the signal loss is effectively compensated. Finally, the compensated signal will have more stable transmission performance, and the compensated signal data is recorded as signal compensation data for subsequent transmission analysis and maintenance.

[0083] Preferably, the calculation of the distribution uniformity in step S3 includes:

[0084] Extract the layer-stranded optical fiber arrangement data based on the layer-stranded optical fiber data;

[0085] In this embodiment, it is necessary to obtain the structural data of the stranded optical fiber through an optical fiber sensor or a high-resolution microscopic scanning device, especially the arrangement information of the optical fibers. These data include the number, position of each layer of optical fibers, and the relative relationship with other optical fibers. Using three-dimensional scanning technology or computed tomography (CT) technology, data of the optical fiber structure are collected from different angles. By processing the scanned data, the specific position coordinates and relative angles of each optical fiber in the layer are extracted. The extracted stranded optical fiber arrangement data will provide basic data for subsequent calculations, and the specific values will be represented by the interlayer angle, the number of optical fiber layers, and the arrangement spacing of each layer of optical fibers.

[0086] Identify the interlayer optical fiber distribution based on the stranded optical fiber arrangement data to obtain the interlayer optical fiber distribution data;

[0087] In this embodiment, based on the extracted stranded optical fiber arrangement data, first analyze the positional relationship between the optical fibers in different layers. Image processing techniques, such as image segmentation algorithms, can be used to segment the projection of the stranded optical fiber in a two-dimensional plane or three-dimensional space, identify the position of each layer of optical fibers, and calculate the distribution characteristics of the optical fibers between each layer according to the distance between the optical fibers. By calculating the relative distance, angle, and positional relationship of each layer of optical fibers, the interlayer optical fiber distribution data are obtained. These data include the spacing between different optical fiber layers, the change trend of the optical fiber positions, and whether there is an irregular distribution, etc. The key technology of this step is to standardize the data based on image processing and coordinate transformation methods to obtain accurate interlayer optical fiber distribution data.

[0088] Calculate the optical fiber spacing based on the interlayer optical fiber distribution data;

[0089] In this embodiment, the cross-section of the optical fiber is scanned using optical microscopy imaging or laser ranging technology to obtain the two-dimensional or three-dimensional coordinate data of all optical fibers, and the abnormal points caused by measurement errors or environmental interference are removed to ensure the integrity and accuracy of the data. Then, the optical fibers within each layer are paired, and the actual spacing between adjacent optical fibers is calculated. For the layer-stranded structure arranged in a ring, the optical fiber coordinates are first arranged in a clockwise or counterclockwise order, and then the distance between each optical fiber and its adjacent optical fiber is calculated to ensure that the calculation results can accurately reflect the true situation of the optical fiber spacing. For the optical fiber spacing between different layers, the shortest distance between the optical fibers in the upper layer and the optical fibers in the lower layer is calculated, and the crossing and offset conditions between the optical fiber layers are analyzed to ensure that the calculated optical fiber spacing covers all the arrangement methods. After obtaining all the optical fiber spacing data, statistical analysis is carried out, and the average value, minimum value, maximum value, and standard deviation of the optical fiber spacing in each layer are calculated to evaluate the uniformity and stability of the optical fiber arrangement. At the same time, considering the arrangement errors existing in the production process of the layer-stranded optical fibers, error correction is performed on the data, including standardizing the optical fiber coordinates using matrix transformation, removing extreme values using filtering methods, and performing multiple measurements and calculating the average value to improve stability. After the data analysis is completed, the optical fiber spacing information is stored in a database, and a data report is generated to record the statistical parameters of the optical fiber spacing in each layer. At the same time, a data visualization tool is used to graphically display the optical fiber arrangement and spacing data to ensure that the calculation results can be visually presented, providing accurate data support for subsequent optical fiber uniformity analysis, cladding gap calculation, and digital twin model parameter update.

[0090] Calculate the cladding gap distribution based on the interlayer optical fiber distribution data;

[0091] In this embodiment, it is necessary to use high-precision measurement equipment (such as industrial CT scanners, laser interferometers or precision optical microscopes) to obtain the actual arrangement of optical fibers inside the cladding, so as to accurately extract the spatial position of the optical fibers relative to the cladding. The cladding gap refers to the minimum distance between each layer of optical fibers and its outer cladding. This parameter has an important impact on the mechanical strength, environmental adaptability and signal transmission characteristics of the optical cable. Before measurement, it is necessary to ensure that the fixing method of the optical fiber sample does not affect the original structure to prevent the position of the optical fiber from shifting due to external forces. In the data acquisition stage, a 360° circumferential scanning method is used to obtain the external contour data of the optical fiber, and the position of the optical fiber in the cladding is accurately matched by combining the interlayer optical fiber distribution data. In the data processing stage, first, the boundary information of the outer cladding of the optical fiber is extracted, and the shortest distance from the center of the optical fiber to the inner surface of the cladding is calculated. This distance is the cladding gap. For the optical fibers in the same layer, due to different optical fiber arrangement methods, the gaps are not uniform. Therefore, it is necessary to measure in multiple directions to obtain the gap data in different directions, and calculate statistical parameters such as the minimum gap, maximum gap, average gap and standard deviation between the optical fibers in this layer and the cladding to evaluate the uniformity of the cladding gap distribution. In the calculation process, factors such as the diameter of the optical fiber itself, the interlayer arrangement density, the spacing between optical fibers, the thermal expansion characteristics of the cladding material, and the processing deviations existing in the production process also need to be considered to ensure the accuracy of the gap calculation. For optical fibers in different layers, a hierarchical analysis method is adopted to calculate the cladding gap distribution of each layer of optical fibers respectively, and combined with the overall optical fiber structure, the variation trends of the cladding gap along the axial and radial directions of the optical fiber are analyzed. After the data analysis is completed, data visualization tools (such as 3D point cloud maps, heat maps or color distribution maps) are used to display the spatial distribution of the cladding gap, so as to visually evaluate the arrangement uniformity of the optical fibers and the cladding matching degree, and provide accurate data support for the subsequent calculation of the optical fiber uniformity index and the update of the digital twin model parameters.

[0092] Determine the optical fiber uniformity index according to the optical fiber spacing and the cladding gap distribution;

[0093] In this embodiment, based on the fiber spacing data, the mean, maximum, minimum and standard deviation of all adjacent fiber spacings are calculated to evaluate the consistency of fiber spacing. The standard deviation is used to measure the fluctuation range of fiber spacing. A smaller standard deviation indicates that the fiber spacing is more uniform, while a larger standard deviation indicates that there is a large deviation in the fiber arrangement. In addition, the coefficient of variation (the ratio of the standard deviation to the mean) needs to be calculated to evaluate the relative uniformity of the fiber spacing. For the cladding gap distribution data, the mean, maximum, minimum, standard deviation and coefficient of variation of the cladding gap of each layer of optical fiber are also calculated to determine the uniformity of the distribution of the optical fiber in the cladding. For layer-twisted optical fibers, in addition to the uniformity analysis of the optical fiber in a single layer, the uniformity changes between different layers also need to be considered. Therefore, it is necessary to calculate the mean spacing of each layer of optical fiber, analyze the spacing differences between different layers, calculate the maximum and minimum values of the inter-layer optical fiber spacing changes, and calculate the inter-layer uniformity coefficient based on the statistical analysis method to measure the uniformity of the overall distribution of the optical fiber. In addition, in order to more intuitively evaluate the uniformity of optical fiber, it is necessary to use the spatial distribution deviation analysis method to compare the actual optical fiber arrangement data with the ideal uniform arrangement model, calculate the deviation of each optical fiber relative to the ideal position, and statistically calculate the mean, maximum, and minimum values of the deviation. Calculate the root mean square value of the overall deviation to measure the overall consistency of the optical fiber arrangement. Furthermore, two-dimensional and three-dimensional data visualization tools, such as contour maps, density distribution maps, and color gradient maps, can be used to display the spacing distribution and cladding gap distribution of optical fibers to intuitively reflect the changing trend of optical fiber uniformity indicators. Finally, based on the above statistics and visualization analysis results, the optical fiber uniformity index is comprehensively evaluated, and the index is used to update the parameters of the digital twin model of the optical cable to optimize the design and manufacturing process of the optical cable and improve the transmission stability and structural uniformity of the optical fiber.

[0094] The distribution uniformity of the layer-twisted optical fiber data is calculated based on the optical fiber uniformity index.

[0095] In this embodiment, considering the distribution of optical fibers in the entire layer-twisted structure, a weighted average method is used to comprehensively calculate the optical fiber uniformity index of each layer to obtain an overall optical fiber distribution uniformity index. This index will reflect the uniform distribution of optical fibers in the entire optical cable. In specific implementation, a weighted algorithm is used to summarize the uniformity data of each layer of optical fiber, and the uniformity index is weighted according to factors such as the number of optical fibers in each layer and the number of optical fiber layers. Finally, by comparing the distribution characteristics of different optical fibers, the distribution uniformity of layer-twisted optical fiber data is obtained, providing data support for optical cable manufacturing and performance optimization.

[0096] Preferably, determining optical fiber misalignment in step S3 includes:

[0097] Extract the optical fiber center coordinates based on the layer-twisted optical fiber data;

[0098] In this embodiment, in the stranded optical fiber structure, the central coordinates of each optical fiber need to be extracted through optical measurement or laser scanning technology. First, a high-precision three-dimensional scanning device (such as a laser confocal microscope or an industrial CT scanner) is used to obtain the cross-sectional data of the optical fibers inside the optical cable and generate the three-dimensional point cloud data of the stranded optical fibers. Then, based on image processing technology, an edge detection algorithm (such as the Canny edge detection) is used to identify the outer contour of the optical fibers, and morphological operations are combined to separate the regions of single optical fibers. For each optical fiber, the least squares circle fitting method is used to calculate the central point coordinates of the optical fiber, and an optical fiber central coordinate data set is established in the three-dimensional coordinate system. To ensure the accuracy of the data, the extracted optical fiber central coordinates need to be error-corrected. The random error can be reduced by performing multiple scans and taking the average value, and abnormal data points are excluded. Finally, the central coordinate data of all stranded optical fibers are stored in a structured database, providing basic data support for subsequent calculation of the optical fiber spacing and deviation analysis.

[0099] Calculate the adjacent optical fiber spacing of the stranded optical fiber data according to the optical fiber central coordinates;

[0100] In this embodiment, according to the optical fiber hierarchical structure, each layer of optical fibers is processed layer by layer to ensure that the adjacent optical fiber spacing is calculated within the same layer. Then, the Euclidean distance is used to calculate the actual spacing between the centers of adjacent optical fibers, and the result is stored as an adjacent optical fiber spacing data set. In the calculation process, the influence of the optical fiber diameter needs to be considered. Therefore, for the center spacing of adjacent optical fibers, the sum of the radii of the two optical fibers needs to be subtracted to obtain the net spacing data. In addition, to analyze the spacing change of optical fibers between different layers, the spacing between adjacent layers of optical fibers also needs to be statistically analyzed, and the axial offset distance between the centers of the optical fibers between layers is calculated. After the calculation is completed, the distribution analysis of the spacing data needs to be carried out, the maximum spacing, minimum spacing, average spacing and standard deviation are statistically analyzed, and the abnormal spacing data exceeding the set threshold (such as ±5%) are marked, providing a reference basis for subsequent calculation of the optical fiber arrangement deviation.

[0101] Calculate the optical fiber arrangement deviation based on the adjacent optical fiber spacing;

[0102] In this embodiment, according to the design parameters of the stranded optical fiber, the standard spacing of the ideal optical fiber arrangement is determined, which can be obtained from the optical cable design specification or manufacturing process parameters. Then, calculate the actual spacing deviation of each optical fiber position, that is, subtract the standard spacing from the actually measured adjacent optical fiber spacing to obtain the optical fiber arrangement deviation data. Further, it is necessary to analyze the deviation distribution, calculate the average deviation, maximum deviation, minimum deviation and standard deviation of each layer of optical fibers, and conduct grouped statistics on the deviation data to identify the areas with larger deviations. For deviations exceeding the set threshold (such as the deviation being greater than ±10% of the design standard), it is necessary to further mark the abnormal data points and draw a deviation heat map in the data visualization system to intuitively display the spatial distribution of the optical fiber arrangement deviation.

[0103] Calculate the offset direction of the optical fiber center coordinates based on the optical fiber arrangement deviation;

[0104] In this embodiment, after calculating the optical fiber arrangement deviation, it is necessary to further analyze the offset direction of the optical fiber to determine whether the optical fiber is misaligned or unevenly arranged. The offset direction of the optical fiber is determined by the uneven change of the adjacent optical fiber spacing, so it is necessary to calculate the spacing increase and decrease trend of each optical fiber relative to its surrounding optical fibers. First, based on the optical fiber arrangement deviation data, calculate the spacing change gradient of each optical fiber relative to its surrounding optical fibers, and analyze the displacement trend of the optical fiber in different directions. If the spacing of the optical fiber significantly increases in a certain direction while decreasing in the opposite direction, it indicates that the optical fiber has offset in that direction. In addition, in order to improve the calculation accuracy, it is necessary to smooth the offset direction data of each layer of optical fibers, use Gaussian filtering or weighted average method to reduce the random fluctuation of local data, and draw the optical fiber offset direction map through vector field analysis method to more intuitively identify the local offset trend in the optical fiber arrangement.

[0105] Calculate the offset amount of the optical fiber center coordinates based on the distribution uniformity;

[0106] In this embodiment, the offset amount of the optical fiber center coordinates is used to quantify the severity of the optical fiber misalignment, and its calculation is based on the optical fiber arrangement deviation and the overall distribution uniformity index. First, use the optical fiber offset direction data to calculate the actual displacement distance of each optical fiber along this direction. Then, combined with the optical fiber arrangement deviation, statistically analyze the distribution uniformity of the entire optical fiber layer, including calculating parameters such as the standard deviation of the optical fiber spacing and the root mean square of the deviation, to evaluate the consistency of the optical fiber arrangement. When calculating the offset amount, it is necessary to distinguish between local offset and overall uniformity deviation, so it is necessary to use the regional statistical method to divide the entire optical cable cross-section into multiple regions and calculate the average offset amount of the optical fibers in each region. For optical fibers with an offset amount exceeding the set threshold (such as greater than ±5% of the standard spacing), they need to be further marked as abnormal optical fibers for subsequent misalignment determination.

[0107] Determine the misalignment of the optical fiber based on the offset and the offset direction to obtain the optical fiber misalignment data.

[0108] In this embodiment, a threshold for optical fiber misalignment determination is set. For example, when the optical fiber offset exceeds ±10% of the standard spacing and the offset direction is inconsistent with the arrangement trend of adjacent optical fibers, it can be determined that the optical fiber is misaligned. Then, based on the offset data of all optical fibers, a misalignment determination matrix is constructed, and the offset amount and direction of each optical fiber are analyzed one by one, and the optical fibers that meet the misalignment conditions are marked. During the determination process, the helical structure characteristics of the stranded optical fiber also need to be considered. For the helically arranged optical fibers, it is necessary to calculate their offset trend along the helical path and compare it with the ideal helical trajectory to determine whether there is misalignment. In addition, in order to more intuitively display the misalignment data, the misaligned optical fibers can be marked on the optical fiber cross-section diagram by means of color coding, and an optical fiber misalignment data table can be generated for use in subsequent digital twin model parameter update and optimization adjustment. Finally, the optical fiber misalignment data is stored in the database, and the production process of the optical cable is optimized in combination with subsequent data analysis processes to ensure the uniform arrangement and stable transmission performance of the optical fibers inside the optical cable.

[0109] Preferably, the optical fiber Brillouin scattering analysis in step S3 includes:

[0110] Calculate the axial stress of the optical fiber based on the optical fiber misalignment data; calculate the radial stress of the optical fiber based on the optical fiber misalignment data;

[0111] In this embodiment, the calculation of the axial stress of the optical fiber is based on the optical fiber misalignment data and analyzed in combination with the physical parameters of the optical cable material. First, according to the spatial coordinates of the optical fiber, the displacement gradient of the misaligned optical fiber in the axial direction is calculated, and the axial deformation distribution matrix of the optical fiber is established. The finite difference method is used to calculate the strain of each optical fiber segment, that is, the axial strain data is obtained by dividing the relative displacement difference between adjacent optical fibers by the original optical fiber spacing. Combining with the elastic modulus of the optical fiber material (for example, the elastic modulus of silica optical fiber is about 72 GPa), the axial stress distribution is calculated through the stress-strain relationship. During the calculation process, the residual stress of the optical fiber during manufacturing and operation needs to be considered. A high-precision optical fiber Brillouin optical time domain reflectometer (BOTDR) is used to measure the true stress distribution of the optical fiber along the axial direction and perform data correction. For the stress concentration area, a refined analysis is required, and combined with the fracture threshold (for example, the typical tensile strength of the optical fiber is 5 GPa) to judge whether there is a risk of structural damage. Finally, the calculated axial stress data is stored as high-precision grid data for subsequent optical wave simulation. The calculation of the radial stress of the optical fiber is based on the optical fiber misalignment data and analyzed in combination with the material mechanical properties of the optical fiber cladding and core. First, the center offset of the optical fiber is extracted, and the contact pressure distribution between it and the surrounding optical fibers and cladding is calculated. The contact mechanics method is used to analyze the local stress concentration caused by the optical fiber misalignment through the Hertz contact theory, and combined with the compression modulus of the optical fiber outer cladding (for example, the compression modulus of a typical polymer cladding is about 2 GPa), the force distribution of the optical fiber in the radial direction is calculated. Using the finite element analysis (FEA) tool, the stress state of the optical fiber in the radial direction is simulated and calculated to determine the maximum radial stress area, and the part where the radial stress exceeds the material yield strength (for example, the yield stress of the optical fiber silica material is about 7 GPa) is marked. For the special stress distribution of the stranding structure in the optical cable, the additional radial stress of the optical fiber in the torsion and bending states also needs to be considered, and a nonlinear material model is used for correction to ensure the accuracy of the radial stress data.

[0112] Based on the axial stress and radial stress of the optical fiber, optical wave simulation of the optical fiber is carried out, and the refractive index during the simulation process is calculated;

[0113] In this embodiment, the optical fluctuation simulation of the optical fiber is based on the calculated axial stress and radial stress data, and is calculated in combination with the refractive index strain response characteristics of the optical fiber. First, based on the photoelastic effect of the optical fiber, the influence of stress on the refractive index is calculated using the photoelastic coefficient (for example, the photoelastic coefficient of a typical optical fiber is 0.22), and an optical fiber refractive index distribution model is established. Then, the finite difference beam propagation method (FD-BPM) is used to numerically simulate the optical field distribution inside the optical fiber, and the refractive index changes in different stress regions are calculated. For regions with a large stress gradient, it is necessary to improve the calculation accuracy, and an adaptive refinement strategy is adopted during the calculation grid division to ensure that the spatial resolution of the refractive index change is not lower than 0.1 μm. For the refractive index changes inside the optical fiber cladding, the influence of temperature and humidity factors also needs to be considered, and environmental correction is carried out. Finally, the refractive index distribution data obtained from the simulation calculation is exported as a high-precision optical parameter file for subsequent Brillouin scattering calculations.

[0114] Calculate the Brillouin scattering frequency shift based on the refractive index to obtain frequency shift data;

[0115] In this embodiment, the calculation of the Brillouin scattering frequency shift is based on the refractive index distribution data of the optical fiber and is analyzed in combination with the elastic wave characteristics of the optical fiber material. First, according to the density of the optical fiber material (for example, the density of a typical optical fiber is 2200 kg / m 3 ) and the sound velocity (for example, the longitudinal wave sound velocity in a typical optical fiber is 5970 m / s), the Brillouin scattering center frequency is calculated. Then, the refractive index distribution data is substituted into the Brillouin scattering frequency shift formula to calculate the Brillouin frequency shift changes in different stress regions. For regions with a large stress, it is necessary to improve the calculation resolution, and a spectral analysis method is adopted to extract the frequency shift curve of the optical fiber along the axial direction. Using the optical time domain reflectometry (OTDR) technique, the Brillouin scattering frequency shift of the actual optical fiber is measured and compared with the calculated data to correct the calculation parameters. Finally, the obtained Brillouin scattering frequency shift data is stored as a high-precision frequency distribution table and data visualization processing is carried out for subsequent optical fiber stress analysis.

[0116] Determine the stress condition of the optical fiber according to the frequency shift data; based on the stress condition of the optical fiber, perform mode field distribution analysis to obtain mode field distribution data;

[0117] In this embodiment, the determination of the stress condition of the optical fiber is based on Brillouin scattering frequency shift data and is analyzed in combination with the stress-frequency shift response relationship of the optical fiber material. First, according to the Brillouin frequency shift sensitivity coefficient of the optical fiber (for example, the frequency shift sensitivity of a typical optical fiber is about 0.05 GHz / GPa), the stress distributions along the axial and radial directions of the optical fiber are calculated. Then, stress inversion analysis is performed using the frequency shift data to extract the true stress conditions in different regions of the optical fiber. For regions with relatively high stress, refined analysis is required and cross-validation is carried out in combination with finite element simulation data. For local stress anomaly points, correction is also required in combination with the manufacturing process parameters of the optical fiber, and false signals caused by measurement errors are eliminated. Finally, the calculated stress data of the optical fiber are stored as a structured data file and used for subsequent mode field distribution analysis. The mode field distribution analysis is calculated based on the stress condition of the optical fiber and in combination with the waveguide characteristics of the optical fiber. First, according to the refractive index gradient distribution of the optical fiber, a mode solving algorithm (such as the finite element mode solving method FEM) is used to calculate the optical field mode distribution of the optical fiber. Then, the changes in the mode field within different stress regions are analyzed, and the main mode characteristic parameters of the mode field are extracted, including the mode field diameter, mode overlap factor, etc. For regions of the optical fiber with relatively high stress, the density of the calculation grid needs to be increased to improve the accuracy of the mode field calculation. Finally, the obtained mode field distribution data are stored as a high-resolution optical field data file for subsequent calculation of scattering loss.

[0118] Rayleigh scattering is calculated based on the mode field distribution data; bending loss is calculated based on the mode field distribution data; inhomogeneity scattering loss is calculated based on the mode field distribution data; Rayleigh scattering, bending loss, and homogeneity scattering loss are integrated to obtain the total scattering loss;

[0119] In this embodiment, the scattering loss of the optical fiber is calculated based on the mode field distribution data and analyzed in combination with the optical loss characteristics of the optical fiber material. When calculating the Rayleigh scattering loss, it is first necessary to obtain the microstructure data of the optical fiber material, including the doping concentration, refractive index distribution, and the density of nanoscale scattering centers. By using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) techniques, the doping ion concentration of the fiber core and cladding is measured, and the Rayleigh scattering loss coefficient is calculated in combination with the optical scattering characteristics of the doping type (such as GeO2, P2O5). The spectral transmission method is used to measure the scattering loss curve of the optical fiber at different wavelengths, and the Rayleigh scattering formula is used to calculate the scattering loss caused by the microstructure inhomogeneity inside the optical fiber. For the spatial distribution of Rayleigh scattering, the propagation characteristics of light waves in the optical fiber are simulated and calculated by using the fiber mode field distribution data in combination with the finite element method (FEM), so as to obtain the distribution of the Rayleigh scattering intensity along the fiber axis. When calculating the bending loss of the optical fiber, the fiber axial curvature distribution data is first extracted, and the mode leakage loss of the optical fiber under different curvature conditions is calculated in combination with the mode field radius of the optical fiber. A three-dimensional fiber morphology measurement device is used to obtain the actual layout of the optical fiber and extract the local bending radius of the optical fiber. For regions smaller than the critical bending radius of the optical fiber (the critical bending radius of a typical single-mode optical fiber is about 30 mm), key analysis is required. The beam propagation method (BPM) is used to simulate and calculate the energy leakage degree of the fiber mode field under different curvature conditions, and the bending loss curve is extracted. During the calculation process, the constraint ability of the refractive index change of the fiber cladding on the mode field is considered, and the modal coupling loss caused by bending is analyzed by using the multimode interference theory. In regions with large bending losses, a local grid refinement strategy is adopted to improve the calculation accuracy, and error correction is performed in combination with experimental measurement data. For the optical fiber layout in special environments, such as the coiling, bending, or mechanically damaged areas of the optical cable, nonlinear loss compensation analysis is also required to improve the accuracy of the calculation results. When calculating the inhomogeneous scattering loss of the optical fiber, it is first necessary to obtain the structural inhomogeneity data generated during the optical fiber manufacturing process, including refractive index perturbation, core diameter change, doping concentration gradient, and cladding interface roughness. The optical microscopy (OM) technique of the fiber cross-section is used to measure the refractive index distribution of the fiber core and cladding, and the local optical homogeneity of the optical fiber material is analyzed by Fourier transform infrared spectroscopy (FTIR). Based on finite element optical simulation, the influence of refractive index perturbation on the mode field distribution is calculated, and the energy loss caused by inhomogeneous scattering is calculated in combination with the scattering cross-section theory. For local refractive index mutation regions, high-resolution optical coherence tomography (OCT) measurement is also required, and error correction is performed in combination with experimental data. When calculating the inhomogeneous scattering loss, the influence of local stress of the optical fiber on the refractive index distribution needs to be considered, the refractive index change under different stress conditions is calculated by using photoelastic analysis, and comprehensive evaluation is performed in combination with the optical field distribution data.Accumulate the data of Rayleigh scattering loss, bending loss, and inhomogeneous scattering loss, and normalize the loss distribution to obtain the total scattering loss of the optical fiber. Use an optical fiber Brillouin scattering spectrum analyzer (BOTDA) to measure the actual scattering loss of the optical fiber, compare it with the calculation result, and adjust the optical parameters in the calculation model to optimize the accuracy of scattering loss calculation. After the total scattering loss calculation is completed, store the loss data in the form of a high-precision curve and provide data input for subsequent Brillouin scattering gain detection.

[0120] Perform Brillouin scattering gain detection based on the total scattering loss to obtain Brillouin scattering data.

[0121] In this embodiment, Brillouin scattering gain detection is based on the total scattering loss data of the optical fiber and is analyzed in combination with the Brillouin gain spectrum to determine the stress distribution, temperature change, and nonlinear effect characteristics of the optical fiber. First, a distributed Brillouin optical time domain analyzer (BOTDA) is used to perform global Brillouin gain measurement on the optical fiber. During the measurement process, a pulsed probe light source and a continuous pump light source are used. The frequency step range of the light source is adjusted to cover the Brillouin scattering frequency shift range of the optical fiber (usually 10 - 13 GHz), and a high-precision photodetector is used to receive the backscattered signal. When measuring the Brillouin gain spectrum of the optical fiber, it is necessary to set an appropriate probe light pulse width. The typical pulse width range is 10 ns to 50 ns to ensure both high spatial resolution (generally less than 1 m) and increased signal intensity. For long-distance optical fibers (exceeding 10 km), Raman amplification technology is required to improve the signal-to-noise ratio of the backscattered signal, and an optical fiber ring filter is used to eliminate system noise interference. In the data acquisition stage, the obtained Brillouin gain signal is signal-modulated. The lock-in amplification technology is used to improve the detection sensitivity of weak signals, and multiple averaging processes are performed to reduce the influence of random noise on the measurement accuracy. In the data analysis stage, first, the Brillouin gain spectrum is nonlinearly fitted to extract the Brillouin scattering peak frequency, gain intensity, and full width at half maximum (FWHM), and the change gradient of the gain curve is calculated to analyze the stress distribution in different regions of the optical fiber. Then, the measured Brillouin scattering gain data is compared with the theoretical calculation results. The theoretical calculation is based on the Brillouin gain equation, considering the refractive index of the optical fiber, material nonlinear coefficient, acousto-optic coupling coefficient, and local environmental parameters of the optical fiber (such as temperature and stress). To improve the measurement accuracy, the differential method is used to calculate the change rate of Brillouin gain with the axial position of the optical fiber, and Fourier transform is used to remove high-frequency noise components to obtain a smooth gain spectrum curve. For the case of abnormal gain in a specific region, secondary measurement is performed by adjusting the light source power and modulation frequency to exclude external environmental interference or system errors. During the process of extracting Brillouin scattering gain data of the optical fiber, the nonlinear effect characteristics of the local part of the optical fiber are mainly analyzed, including the stress enhancement effect, temperature sensitivity effect, and optical mode coupling effect. For the stress enhancement effect, the correlation between the Brillouin scattering frequency shift and local stress is calculated, and the frequency shift data is converted into the axial stress distribution based on the known elasto-optic coefficient of the optical fiber. For the temperature sensitivity effect, the temperature response characteristics of the Brillouin scattering spectrum are extracted, and the Brillouin frequency shift drift caused by temperature change is corrected by a thermal field sensor. In the analysis of the mode coupling effect, combined with the optical fiber mode field distribution data, the gain distortion region caused by multimode interference is calculated, and a correction coefficient is used to normalize the gain curve. The obtained Brillouin scattering gain data is stored as a high-precision spectral file, and the hierarchical storage technology is used to classify and store the data according to the axial position of the optical fiber, gain intensity, frequency shift characteristics, etc., for subsequent data calling and analysis.During the parameter update process of the optical cable digital twin model, these data are used to correct the non-uniform stress distribution model of the optical fiber and optimize the performance prediction accuracy of the optical fiber under complex environmental conditions. When storing the data, a binary compression format is adopted to reduce storage occupancy, and the measurement time is marked in combination with timestamp information for long-term trend analysis and historical data backtracking. Finally, a complete optical cable digital twin model is established by combining other optical parameters to achieve real-time monitoring and dynamic optimization of the optical fiber performance.

[0122] Preferably, the improved stranding parameters described in step S3 include:

[0123] Calculate the local stress distribution of the optical fiber based on Brillouin scattering data to obtain stress distribution data;

[0124] In this embodiment, the Brillouin scattering gain spectrum in the axial direction of the optical fiber is demodulated with high precision, and the Brillouin frequency shift is extracted. The Brillouin frequency shift data is converted into local stress distribution data through the calibrated photoelastic coefficient of the optical fiber. Among them, the selection of the photoelastic coefficient of the optical fiber is based on the manufacturing material of the optical fiber and its temperature-dependent characteristics. To improve the calculation accuracy, multi-point data interpolation is required. The piecewise fitting method is used to calculate the change of stress with the axial position of the optical fiber, and the data is denoised to eliminate measurement errors. In long-distance optical fiber measurement, the Brillouin scattering signal will be affected by optical fiber attenuation and non-uniform scattering. Therefore, the Gaussian filtering algorithm is used to smooth the frequency shift curve to reduce noise interference. Finally, the stress distribution data is constructed based on the discrete data points in the optical fiber length direction and stored as a multi-dimensional matrix for subsequent analysis.

[0125] Identify the stress non-uniform region according to the stress distribution data; improve the lay length based on the stress non-uniform region;

[0126] In this embodiment, the difference method is used to calculate the stress change amount between adjacent measurement points, and the local extreme points are extracted based on the moving window technique. For the stress mutation region, the statistical method is used to calculate its distribution range, and the error points caused by measurement noise are excluded. To ensure the accuracy of the stress non-uniform region, a classification method based on the standard deviation is adopted to mark the region with large stress fluctuations as the stress non-uniform region, and the region with small stress changes is classified as the stable region. For the case where the measurement data has non-linear drift, the regression analysis method is used for data correction to ensure the accuracy of identifying the stress non-uniform region. Finally, the starting position, ending position, and stress fluctuation amplitude of all stress non-uniform regions are stored in a structured data table for subsequent optimization of the lay length of the optical fiber.

[0127] Evaluate the stress direction according to the stress distribution data; improve the stranding direction based on the stress direction;

[0128] In this embodiment, the original lay length parameters during optical cable manufacturing are extracted and matched with the data of the identified stress non-uniform regions for analysis to determine which stress abnormal regions are related to the current lay length design. Then, the finite difference method is used to calculate the optical fiber stress distribution under different lay length conditions, and the influence of different lay lengths on stress uniformity is evaluated based on the smoothness of the stress distribution curve. For regions with large stress, the lay length needs to be increased to reduce stress concentration, while for regions with small stress, the lay length needs to be decreased to optimize the mechanical properties. Finally, the optimized lay length data is stored and used to adjust the optical cable manufacturing process to reduce local stress non-uniformity. It is necessary to calculate the stress components in the axial and radial directions of the optical fiber and determine the main stress direction. First, the Brillouin scattering frequency shift data of the optical fiber at different positions is extracted and converted into axial stress values. Then, combined with the optical mode field data on the cross-section of the optical fiber, the radial stress distribution of the optical fiber is calculated. For regions with local stress anomalies, the elliptical fitting method is used to analyze the main stress direction and calculate the angle between the major axis of the stress ellipse and the axial direction of the optical fiber. If the angle exceeds the set threshold (such as 15 degrees), the optical fiber stranding direction needs to be adjusted to reduce the influence of transverse stress. Finally, the stress direction data is stored and used for subsequent optimization of the optical cable stranding direction. Obtain the current stranding direction of the optical cable and compare the stress changes under different stranding directions. If it is found that the current stranding direction leads to an increase in stress concentration regions, the stranding direction needs to be adjusted to make the forces on the optical fiber in the axial and radial directions more uniform. During the adjustment process, a mechanical simulation software is used to calculate the optical fiber stress distribution under different stranding directions, and the adjustment effect is evaluated based on the stress gradient change. For regions with large stress gradients, the stranding direction needs to be adjusted in the reverse direction to reduce stress concentration. Finally, the optimized stranding direction parameters are stored and applied to the optical cable manufacturing process.

[0129] Calculate the stress gradient based on the stress distribution data; Improve the stranding angle based on the stress gradient;

[0130] In this embodiment, stress data at different positions of the optical fiber are extracted, and the stress change rate between adjacent points is calculated using the difference method. For regions with rapid stress changes, the local regression analysis method is used to calculate the stress gradient curve, and the region with the largest stress change is extracted. To ensure the accuracy of stress gradient calculation, the measurement data are filtered to remove noise interference. Finally, the calculated stress gradient data are stored and used to optimize the stranding angle of the optical cable. The current stranding angle data of the optical cable are extracted and matched with the stress gradient data to determine which regions need to optimize the stranding angle. Then, the finite element simulation method is used to calculate the influence of different stranding angles on the stress of the optical fiber, and the optimal stranding angle is selected based on the simulation results. For regions with a large stress gradient, the stranding angle needs to be increased to reduce stress concentration, while for regions with a small stress gradient, the stranding angle needs to be decreased to optimize the force distribution. Finally, the optimized stranding angle parameters are stored and used to adjust the production process of the optical cable.

[0131] Integrate the lay length, stranding direction, and stranding angle to obtain the stranding parameters.

[0132] In this embodiment, the optimized lay length, stranding direction, and stranding angle data are extracted and integrated into a unified parameter database. Then, the optical cable structure simulation software is used to calculate the stress distribution of the optical fiber under different combinations of stranding parameters, and the optimal stranding parameters are selected based on the optimization goal. Finally, the calculated stranding parameters are applied to the optical cable manufacturing process, and the optical cable is experimentally verified to ensure that the optimized stranding structure can effectively reduce the stress concentration of the optical fiber and improve the mechanical properties and optical stability of the optical cable.

[0133] Preferably, step S4 is specifically as follows:

[0134] Step S41: Transmit the signal compensation data and the stranding parameters to the optical cable digital twin model, and perform signal transmission simulation to obtain signal transmission simulation data; extract the low-success-rate transmission data based on the signal transmission simulation data;

[0135] In this embodiment, the signal compensation data includes the optical cable transmission loss compensation coefficient, the nonlinear distortion correction parameter, the phase compensation factor, etc., and the acquisition method is calculated based on the analysis results of the previous Brillouin scattering data. The stranding parameters include the optimized fiber pitch, stranding direction, and stranding angle, which are derived from the parameter set optimized based on the stress distribution. For data transmission, all numerical values are input into the data interface of the optical cable digital twin model using a high-speed data transmission protocol (such as TCP / IP or CAN bus), and data format standardization conversion is performed to ensure compatibility of data from different sources. For signal transmission simulation, an optical signal propagation calculation tool based on the finite element method, such as OptiSystem or COMSOL Multiphysics, is used. The power of the input optical signal (such as 10 dBm), the light source wavelength (such as 1550 nm), and the fiber geometric structure parameters are set, and the beam propagation method (BPM) or the finite difference time domain method (FDTD) is used to simulate the propagation of the optical signal in the optical cable. During the simulation process, the refractive index of the fiber cladding (such as 1.444), the refractive index of the fiber core (such as 1.468), and the dispersion parameters need to be set to calculate the transmission loss and dispersion effect of the optical signal. Finally, the signal transmission simulation data of the entire length of the optical cable is obtained through simulation calculation and stored as a structured database file for subsequent analysis. The extraction of low-success-rate transmission data depends on the statistical analysis of the signal transmission simulation data. The specific steps include signal attenuation rate calculation, bit error rate statistics, and signal-to-noise ratio evaluation. First, compare the signal power at the input and output ends of the optical cable, and calculate the attenuation amplitude of each sampling point through the interpolation algorithm. If the attenuation of a certain section of the optical cable exceeds the set threshold (such as 0.22 dB / km), it is marked as a low-success-rate transmission area. Then, use the bit error rate detection algorithm to analyze the optical signal transmission quality in this area, extract the data segments with a bit error rate exceeding the specified standard (such as 10 -9 ), and record the bit error occurrence frequency and location. Further combined with the signal-to-noise ratio (SNR) analysis, calculate the signal spectrum characteristics through the fast Fourier transform (FFT), and screen the signal data areas with a signal-to-noise ratio lower than the standard value (such as 20 dB). All low-success-rate transmission data, including information such as the occurrence location, attenuation amplitude, bit error rate, and signal-to-noise ratio, is stored in the database and used to analyze the influence of the fiber cladding material performance on signal transmission.

[0136] Step S42: Collect the fiber cladding material according to the low-success-rate transmission data;

[0137] In this embodiment, the optical cable section corresponding to the data with low transmission success rate is determined, and the information of the cladding material used for this section of optical cable is matched by using the optical cable structure database, including material composition, manufacturing process and thickness data. Then, in a laboratory environment, the optical cable sample in the area with low transmission success rate is cut, and the optical fiber cladding material sample is extracted. The extraction process of the cladding material needs to use precision cutting equipment, such as a diamond blade cutting machine, to ensure the integrity of the cladding material, and the cutting size needs to meet the standard (such as 5mm×5mm×1mm). Subsequently, the samples are chemically cleaned to remove surface contaminants and ensure the test accuracy. Finally, all the cladding material samples are numbered and archived, and performance tests are carried out to analyze the influence of the cladding material on the signal transmission characteristics.

[0138] Step S43: Detect the cladding thickness based on the optical fiber cladding material;

[0139] In this embodiment, the optical fiber cladding material sample is fixed on the microscope stage, the focal length is adjusted to the optimal position, and the scanning resolution is set (such as 10nm). Then, the cladding surface is scanned by laser, multiple-point thickness data is recorded, and the mean value is calculated. For white light interference measurement, the incident light wavelength range is set (such as 400–700nm), the interference fringe distribution is measured, and the cladding thickness data is calculated by using the phase-shift interference technique. For each sample, the number of measurement points needs to reach the specified number (such as 1000 points), and statistical analysis is carried out to obtain the mean value, variance and maximum and minimum value range of the cladding thickness. All the detection data are stored in the database for subsequent evaluation of the thickness uniformity.

[0140] Step S44: Determine the thickness non-uniformity of the optical fiber cladding material according to the cladding thickness, and obtain the thickness non-uniformity data;

[0141] In this embodiment, the cladding thickness detection data in the database is called, and the thickness deviation of each measurement point is calculated. Then, the standard deviation of the cladding thickness is calculated by using statistical methods, and the non-uniformity determination threshold is set (such as ±2%). If the thickness deviation in a certain area exceeds this threshold, this area is marked as a thickness non-uniform area. To improve the determination accuracy, spatial distribution analysis is also required. The Kriging interpolation algorithm is used to spatially fit the thickness data, continuous non-uniform areas are identified, and the coordinates, area and maximum deviation value of the non-uniform areas are recorded. Finally, all the thickness non-uniformity data are stored and used for the analysis of subsequent cladding material aging tests.

[0142] Step S45: Conduct an aging test based on the optical fiber cladding material to obtain aging test data;

[0143] In this embodiment, the aging test adopts three experimental methods: high-temperature accelerated aging, ultraviolet light aging, and damp-heat environment aging. For the high-temperature accelerated aging experiment, the set temperature is 85 °C and the duration is 1000 hours. During this period, the changes in the tensile strength and elongation at break of the cladding material are recorded every 100 hours. For the ultraviolet light aging experiment, a UV lamp is used for irradiation, the set ultraviolet wavelength (such as 365 nm), and the irradiation time is 500 hours. The light transmittance of the optical fiber cladding and the material degradation are tested. For the damp-heat environment aging experiment, the set temperature is 65 °C and the relative humidity is 90%. The test cycle is 1000 hours. During this period, the moisture absorption rate of the material and the changes in mechanical properties are measured every 200 hours. Finally, all aging test data are stored and used to evaluate the durability of the cladding material.

[0144] Step S46: Evaluate the durability of the cladding material based on the aging test data;

[0145] In this embodiment, the material strength, light transmittance, and moisture absorption rate data at each experimental stage are extracted, and an exponential decay model is used to fit their change trends. Then, the material property decay rate is calculated. If the decay rate exceeds the set threshold (such as 10% / 1000 h), the durability of the material is insufficient. Finally, according to the durability data under different aging environments, the materials are classified and stored in the database.

[0146] Step S47: Predict the optical cable signal attenuation degree based on the thickness non-uniformity data and the durability of the cladding material;

[0147] In this embodiment, based on the distribution of the thickness non-uniformity region, the change in the local optical fiber refractive index is calculated, and its influence on mode propagation is analyzed. Then, combined with the optical performance attenuation data caused by aging, the optical signal attenuation rate under different environmental conditions is calculated and stored in the database.

[0148] Step S48: Update the parameters of the optical cable digital twin model according to the optical cable signal attenuation degree to generate the updated parameters of the digital twin model.

[0149] In this embodiment, the original optical cable transmission model is called, and the attenuation degree prediction data are imported. Then, the loss coefficient, dispersion parameter, and compensation strategy of the optical fiber are adjusted, and simulation verification is performed. Finally, the updated digital twin model parameters are generated and stored in the database.

[0150] Therefore, from any perspective, the embodiment should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed by the present invention.

[0151] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A method for updating digital twin model parameters applied to an optical cable, characterized in that, It includes the following steps: Step S1: Collect fiber optic laying data; based on the fiber optic laying data, conduct laying structure division to obtain flat structure fiber optic data and stranded fiber optic data; Step S2: Evaluate the tensile strength of the optical cable based on the flat structure fiber optic data; Detect the optical signal transmission intensity of the flat structure fiber optic data based on the tensile strength of the optical cable; Perform intelligent loss compensation on the flat structure fiber optic data according to the optical signal transmission intensity to obtain signal compensation data; Step S3: Calculate the distribution uniformity based on the stranded fiber optic data; Determine fiber misalignment based on the distribution uniformity to obtain fiber misalignment data; Conduct fiber Brillouin scattering detection based on the fiber misalignment data to obtain Brillouin scattering data; Improve the stranding parameters of the stranded fiber optic data based on the Brillouin scattering data; Step S4: Transmit the signal compensation data and the stranding parameters to the optical cable digital twin model and predict the signal attenuation degree of the optical cable; update the parameters of the optical cable digital twin model according to the signal attenuation degree of the optical cable to generate digital twin model update parameters.

2. The method for updating digital twin model parameters applied to an optical cable according to claim 1, wherein Specifically, step S1 is as follows: Step S11: Collect fiber optic laying data; Step S12: Extract the fiber arrangement pattern based on the fiber optic laying data; extract the fiber wiring space based on the fiber optic laying data; Step S13: Construct a planar arrangement structure according to the fiber arrangement pattern; construct a spiral arrangement structure according to the fiber arrangement pattern; Step S14: Identify the spacious wiring space in the fiber wiring space; identify the high-density wiring space in the fiber wiring space; Step S15: Determine the flat structure fiber optic type according to the planar arrangement structure and the spacious wiring space to obtain flat structure fiber optic data; Step S16: Determine the stranded fiber optic type according to the spiral arrangement structure and the high-density wiring space to obtain stranded fiber optic data.

3. The method for updating digital twin model parameters applied to an optical cable according to claim 1, characterized in that Specifically, step S2 is as follows: Step S21: Identify the fiber cross-sectional shape based on the flat structure fiber optic data; Step S22: Determine the fiber cross-sectional material according to the fiber cross-sectional shape; Draw a fiber cross-sectional material distribution map according to the fiber cross-sectional material; Calculate the material stress based on the fiber cross-sectional material; Step S23: Map the material stress to the fiber cross-sectional material distribution map to generate a cross-sectional material stress distribution map; Step S24: Identify the stress concentration area in the cross-sectional material stress distribution map; Evaluate the tensile strength of the optical cable based on the stress concentration area; Step S25: Detect the optical signal transmission intensity of the flat structure fiber optic data based on the tensile strength of the optical cable; Step S26: Perform intelligent loss compensation on the flat structure fiber optic data according to the optical signal transmission intensity to obtain signal compensation data.

4. The method for updating digital twin model parameters applied to an optical cable according to claim 3, wherein Specifically, step S25 is as follows: Step S251: Determine the low-tensile fiber segments of the flat structure fiber optic data based on the tensile strength of the optical cable; Step S252: Conduct tensile load simulation on the low-tensile fiber segments to obtain tensile load simulation data; Step S253: Identify the fiber anti-fatigue state based on the tensile load simulation data; Step S254: Predict the crack initiation point according to the fiber anti-fatigue state; Step S255: Conduct crack propagation based on the crack initiation point to obtain crack propagation data; Step S256: Evaluate the optical signal transmission intensity of the low-tensile fiber segments according to the crack propagation data.

5. The method for updating digital twin model parameters applied to an optical cable according to claim 3, wherein Specifically, step S26 is as follows: Step S261: Determine the signal attenuation degree according to the optical signal transmission intensity; Step S262: Statistically analyze the high-attenuation signal data based on the signal attenuation degree; Step S263: Determine the high-attenuation signal fiber segments of the flat-structured optical fiber data according to the high-attenuation signal data; Step S264: Adjust the input optical power based on the high-attenuation signal fiber segments; Step S265: Lay a gain optical amplifier based on the high-attenuation signal fiber segments; Identify the pump light source based on the gain optical amplifier; Excite erbium ions based on the pump light source; Amplify the signal photons of the high-attenuation signal fiber segments according to the erbium ions; Determine the signal increase intensity according to the signal photons; Step S266: Perform intelligent loss compensation on the flat-structured optical fiber data according to the signal increase intensity and the input optical power to obtain signal compensation data.

6. The method for updating digital twin model parameters applied to an optical cable according to claim 1, characterized in that The calculation of the distribution uniformity in Step S3 includes: Extract the layer-stranded optical fiber arrangement data based on the layer-stranded optical fiber data; Identify the interlayer optical fiber distribution according to the layer-stranded optical fiber arrangement data to obtain the interlayer optical fiber distribution data; Calculate the optical fiber spacing based on the interlayer optical fiber distribution data; Calculate the cladding gap distribution based on the interlayer optical fiber distribution data; Determine the optical fiber uniformity index according to the optical fiber spacing and the cladding gap distribution; Calculate the distribution uniformity of the layer-stranded optical fiber data according to the optical fiber uniformity index.

7. The method for updating digital twin model parameters applied to an optical cable according to claim 1, wherein, The determination of optical fiber misalignment in Step S3 includes: Extract the optical fiber center coordinates based on the layer-stranded optical fiber data; Calculate the adjacent optical fiber spacing of the layer-stranded optical fiber data according to the optical fiber center coordinates; Calculate the optical fiber arrangement deviation based on the adjacent optical fiber spacing; Calculate the offset direction of the optical fiber center coordinates based on the optical fiber arrangement deviation; Calculate the offset amount of the optical fiber center coordinates based on the distribution uniformity; Determine the optical fiber misalignment based on the offset amount and the offset direction to obtain the optical fiber misalignment data.

8. The method for updating digital twin model parameters applied to an optical cable according to claim 1, wherein The optical fiber Brillouin scattering analysis in Step S3 includes: Calculate the axial stress of the optical fiber based on the optical fiber misalignment data; Calculate the radial stress of the optical fiber based on the optical fiber misalignment data; Perform optical fiber optical wave simulation according to the axial stress and the radial stress of the optical fiber, and calculate the refractive index during the simulation process; Calculate the Brillouin shift frequency based on the refractive index to obtain the frequency shift data; Determine the stress condition of the optical fiber according to the frequency shift data; Perform mode field distribution analysis based on the stress condition of the optical fiber to obtain the mode field distribution data; Calculate Rayleigh scattering based on the mode field distribution data; Calculate bending loss based on the mode field distribution data; Calculate inhomogeneous scattering loss based on the mode field distribution data; Integrate Rayleigh scattering, bending loss, and homogeneous scattering loss to obtain the total scattering loss; Perform Brillouin scattering gain detection according to the total scattering loss to obtain the Brillouin scattering data.

9. The method for updating digital twin model parameters applied to an optical cable according to claim 1, wherein The improvement of the stranding parameters in Step S3 includes: Calculate the local stress distribution of the optical fiber based on the Brillouin scattering data to obtain the stress distribution data; Identify the stress uneven region according to the stress distribution data; Improve the lay length based on the stress uneven region; Evaluate the stress direction according to the stress distribution data; Improve the stranding direction based on the stress direction; Calculate the stress gradient according to the stress distribution data; Improve the stranding angle based on the stress gradient; Integrate the lay length, the stranding direction, and the stranding angle to obtain the stranding parameters.

10. The method for updating digital twin model parameters applied to an optical cable according to claim 1, wherein, Step S4 is specifically: Step S41: Transmit the signal compensation data and the stranding parameters to the optical cable digital twin model, and perform signal transmission simulation to obtain signal transmission simulation data; extract the low success rate transmission data based on the signal transmission simulation data; Step S42: Collect the optical fiber cladding material according to the low success rate transmission data; Step S43: Detect the cladding thickness based on the optical fiber cladding material; Step S44: Determine the thickness non-uniformity of the optical fiber cladding material according to the cladding thickness to obtain the thickness non-uniformity data; Step S45: Conduct an aging test on the optical fiber cladding material to obtain aging test data; Step S46: Evaluate the durability of the cladding material according to the aging test data; Step S47: Predict the signal attenuation degree of the optical cable according to the thickness non-uniformity data and the durability of the cladding material; Step S48: Update the parameters of the optical cable digital twin model according to the signal attenuation degree of the optical cable to generate the updated parameters of the digital twin model.

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