CN115468739A - Processing method and system of high-integration ribbon optical cable - Google Patents
Processing method and system of high-integration ribbon optical cable Download PDFInfo
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- CN115468739A CN115468739A CN202211355092.6A CN202211355092A CN115468739A CN 115468739 A CN115468739 A CN 115468739A CN 202211355092 A CN202211355092 A CN 202211355092A CN 115468739 A CN115468739 A CN 115468739A
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Abstract
The invention provides a processing method and a system of a high-integration ribbon optical cable, and relates to the technical field of optical cable processing, wherein a plurality of loose tube thickness parameters, a plurality of fullness parameters and a plurality of clearance parameters in a target optical cable are obtained, a loose tube data analysis result is obtained based on the plurality of loose tube thickness parameters and the plurality of fullness parameters, the loose tube data analysis result is input into a loose tube quality analysis module to obtain a loose tube quality analysis result, similarly, a sheath quality analysis result is obtained based on the sheath quality analysis module, a target optical cable processing evaluation result is obtained based on an optical cable processing evaluation space, and processing adjustment of the target optical cable is carried out.
Description
Technical Field
The invention relates to the technical field of optical cable processing, in particular to a processing method and a processing system of a high-integration ribbon optical cable.
Background
The optical cable is a cable formed by optical fibers through a certain process, and is a communication line for realizing optical signal transmission. The basic structure of the optical cable generally comprises a cable core, a reinforcing steel wire, a sheath and the like, the structure of the ribbon optical cable leads to poor compression resistance of the ribbon optical cable at present, large optical fiber attenuation is easily generated after stress, and the transmission performance is weakened.
In the prior art, when the ribbon optical cable is processed, the quality of the ribbon optical cable is different due to inevitable errors of a processing process, which is specifically reflected in that the quality of a loose tube and the quality of a sheath are different, so that the performance of the optical cable cannot reach an expected standard, and potential risks exist during signal transmission.
Disclosure of Invention
The application provides a processing method and a system of a high-integration ribbon optical cable, which are used for solving the technical problems that the optical cable quality is different due to the universality of a cable processing technology in the prior art, the performance of the optical cable cannot reach an expected standard, and potential risks exist during signal transmission.
In view of the foregoing, the present application provides a method and system for processing a highly integrated optical fiber ribbon cable.
In a first aspect, the present application provides a method of processing a highly integrated optical ribbon cable, the method comprising: processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly-integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers; after the preliminary processing is finished, carrying out multiple thickness detection on the loose tube in the target optical cable to obtain multiple loose tube thickness parameters, and carrying out multiple factice fullness detection in the loose tube to obtain multiple fullness parameters; detecting gaps between the sheath and the plurality of groups of cable cores in the target optical cable to obtain a plurality of gap parameters; constructing a ribbon cable quality analysis model, wherein the ribbon cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module; performing data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result; performing data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and inputting the sheath data analysis result into the sheath quality analysis module to obtain a sheath quality analysis result; inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continuously processing and adjusting the target optical cable according to the target optical cable processing evaluation result.
In a second aspect, the present application provides a system for processing a highly integrated ribbon cable, the system comprising: the technical processing module is used for processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers; the parameter detection module is used for detecting the thicknesses of the loose tubes in the target optical cable at multiple positions after the preliminary processing is finished to obtain multiple loose tube thickness parameters, and detecting the fullness of ointment at multiple positions in the loose tubes to obtain multiple fullness parameters; the gap detection module is used for detecting the gaps between the sheath and the plurality of cable cores in the target optical cable to obtain a plurality of gap parameters; the system comprises a model construction module, a quality analysis module and a quality analysis module, wherein the model construction module is used for constructing a ribbon cable quality analysis model, and the ribbon cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module; the loose tube quality determination module is used for carrying out data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result; the sheath quality determining module is used for carrying out data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and inputting the sheath data analysis result into the sheath quality analysis module to obtain a sheath quality analysis result; and the processing adjusting module is used for inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continuously processing and adjusting the target optical cable according to the target optical cable processing evaluation result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the processing method of the high-integration ribbon optical cable, a preset manufacturing process is adopted for processing a target optical cable, after primary processing is finished, multiple thickness detection is carried out on a loose tube in the target optical cable, multiple loose tube thickness parameters are obtained, multiple ointment fullness detection is carried out in the loose tube, multiple fullness parameters are obtained, and gap detection of a sheath and multiple groups of cable cores is carried out to obtain multiple gap parameters; the method comprises the steps of constructing a ribbon optical cable quality analysis model, analyzing a plurality of loose tube thickness parameters and a plurality of fullness parameters to obtain a loose tube data analysis result, inputting the loose tube data analysis result into a loose tube quality analysis module to obtain a loose tube quality analysis result, similarly, obtaining a sheath quality analysis result based on the sheath quality analysis module, obtaining a target optical cable processing evaluation result based on an optical cable processing evaluation space, and carrying out processing adjustment on the target optical cable.
Drawings
FIG. 1 is a schematic flow chart of a method for processing a highly integrated ribbon cable according to the present application;
FIG. 2 is a schematic representation of a ribbon cable quality analysis model construction process in a method of processing a highly integrated ribbon cable according to the present application;
FIG. 3 is a schematic view of a process for obtaining a processing evaluation result of a target optical cable in a method for processing a highly integrated optical ribbon cable according to the present application;
fig. 4 is a schematic diagram of a highly integrated ribbon cable processing system according to the present application.
Description of the reference numerals: the device comprises a process processing module 11, a parameter detection module 12, a gap detection module 13, a model construction module 14, a loose tube quality determination module 15, a sheath quality determination module 16 and a processing adjustment module 17.
Detailed Description
The application provides a processing method and a system of a high-integration optical fiber ribbon cable, a plurality of loose tube thickness parameters, a plurality of fullness parameters and a plurality of clearance parameters in a target optical cable are obtained, a loose tube data analysis result is obtained based on the loose tube thickness parameters and the fullness parameters, the loose tube data analysis result is input into a loose tube quality analysis module to obtain a loose tube quality analysis result, similarly, a sheath quality analysis result is obtained based on the sheath quality analysis module, a target optical cable processing evaluation result is obtained based on an optical cable processing evaluation space, and the target optical cable is processed and adjusted to solve the technical problems that optical cable quality is poor due to universality of a processing technology, optical cable performance cannot reach an expected standard, and potential risks exist during signal transmission in the prior art.
Example one
As shown in fig. 1, the present application provides a method of processing a highly integrated ribbon cable, the method comprising:
step S100: processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly-integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers;
specifically, the optical cable is used as an important line component for signal transmission and is widely applied, in order to guarantee the application performance of the optical cable, the quality detection needs to be carried out on main process components of the optical cable after the optical cable is processed.
Firstly, obtaining the preset manufacturing process, namely an initial processing process for processing the optical cable, wherein the initial processing process mainly comprises a coloring process, an optical fiber two-in-one process, a cabling process and a sheath process, the optical fiber two-in-one process and the sheath process are main performance influence processes, processing is carried out based on the preset manufacturing process to generate a high-integration ribbon optical cable consisting of a plurality of groups of cable cores, each group of optical cable comprises a plurality of optical fibers, a processed finished product is obtained to be used as the target optical cable, the target optical cable is used as a sample to be subjected to quality detection, and a foundation is tamped for subsequent optical cable quality evaluation and process optimization.
Step S200: after the preliminary processing is finished, carrying out multiple thickness detection on the loose tube in the target optical cable to obtain multiple loose tube thickness parameters, and carrying out multiple factice fullness detection in the loose tube to obtain multiple fullness parameters;
specifically, the optical fiber cable is processed based on the preset manufacturing process, the target optical cable is obtained after the preliminary processing is completed, the process parameters of the target optical cable are detected, a loose tube is used as a processing result of an optical fiber two-in-one process, a protective sleeve made of a high polymer material and directly sleeved on the optical fiber is adopted, a preset detection distance can be set in order to guarantee the qualification and uniformity of the thickness of the loose tube, a plurality of detection positions of the target optical fiber are determined based on the preset detection distance, thickness detection is further performed to generate a plurality of loose tube thickness parameters, the plurality of loose tube thickness parameters correspond to the plurality of detection positions one to one, an optical fiber special ointment completely compatible with a tube material can be filled between the loose tube and the optical fiber, the performance is stable, the optical fiber has good protection performance, the ointment fullness in the loose tube is detected based on the plurality of detection positions, exemplarily, a plurality of fullness parameters can be obtained by setting a plurality of fullness grades to perform visual representation of detection results, and basic data are provided for subsequent loose tube quality analysis.
Step S300: detecting gaps between the sheath and the plurality of groups of cable cores in the target optical cable to obtain a plurality of gap parameters;
specifically, a sheath is required to be added outside a cable core in the target optical cable, the sheath serves as a protective layer for the target optical cable to resist the external environment, the target optical cable can adapt to different use environments to mechanically protect the optical fiber, the target optical cable comprises multiple groups of cable cores, gaps between the multiple groups of cable cores and the sheath are detected respectively, namely relative distances between the multiple groups of cable cores and the sheath are detected, detection parameters and the multiple groups of cable cores are correspondingly marked so as to be distinguished, multiple gap parameters are obtained to analyze rationality of the gaps, the multiple gap parameters are indexes of quality detection of the sheath, and basic data support is provided for subsequent quality analysis of the sheath.
Step S400: constructing a ribbon cable quality analysis model, wherein the ribbon cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module;
specifically, the main structure influenced by the target optical cable performance is used as a direction to be analyzed, the main structure comprises a loose tube and a sheath, the loose tube quality analysis module and the sheath quality analysis module are respectively constructed, the loose tube quality analysis module mainly determines the quality of the loose tube based on the thickness of the loose tube and the filling degree of factice, the sheath quality analysis module carries out quality evaluation based on a sheath gap, the loose tube quality analysis module and the sheath quality analysis module are further used as a main model analysis framework to complete construction of the ribbon optical cable quality analysis model, and the construction of the ribbon optical cable quality analysis model provides an auxiliary analysis tool for subsequent parameter analysis and evaluation.
Further, as shown in fig. 2, a ribbon cable quality analysis model is constructed, and step S400 of the present application further includes:
step S410: constructing the loose tube quality analysis module;
step S420: constructing the sheath quality analysis module;
step S430: and obtaining the constructed ribbon optical cable quality analysis model according to the constructed loose tube quality analysis module and the sheath quality analysis module.
Specifically, a primary loose tube quality analysis module and a primary sheath quality analysis module are respectively constructed based on a BP neural network, a plurality of target optical cables are further determined as samples, parameter detection and evaluation analysis are carried out, sample data analysis results including thickness parameters and fullness parameters of the loose tube and gap parameters of the sheath are determined, quality evaluation is carried out on the samples to determine corresponding quality analysis results, further, mapping correspondence is carried out on the sample data analysis results and the quality analysis results of the loose tube and the sheath, generated data sets are used as data sets and are respectively input into the primary loose tube quality analysis module and the primary sheath quality analysis module, module training verification and testing are carried out, construction of the loose tube quality analysis module and the sheath quality analysis module is completed, construction of the ribbon optical cable quality analysis model is carried out based on the data sets, the loose tube quality analysis module and the sheath quality analysis module are embedded into a module analysis layer in the ribbon optical cable quality analysis model, the ribbon optical cable quality analysis model is a multi-level network layer, the data identification layer and the module analysis layer carry out quality analysis module through the construction model, and the effective quality analysis results of the target optical cables can be improved, and the accuracy of the quality analysis of the ribbon optical cables can be improved.
Further, constructing the loose tube quality analysis module, step S410 of the present application further includes:
step S411: acquiring loose tube thickness parameters and fullness parameters of a plurality of target optical cables to obtain a plurality of loose tube thickness parameter sets and a plurality of fullness parameter sets;
step S412: performing data analysis on the plurality of loose tube thickness parameter sets and the plurality of fullness parameter sets to obtain a plurality of sample loose tube data analysis results;
step S413: performing loose tube quality evaluation analysis according to the analysis results of the plurality of sample loose tube data to obtain a plurality of sample loose tube quality analysis results;
step S414: performing data identification on the multiple sample sheath data analysis effects and the multiple sample loose tube quality analysis results to obtain a first constructed data set;
step S415: and constructing the loose tube quality analysis module by adopting the first construction data set.
Specifically, loose tube thickness detection and factice fullness detection are performed on a plurality of processed target optical cables, a plurality of groups of loose tube thickness parameters and a plurality of groups of fullness parameters are obtained, wherein the plurality of target optical cables correspond to the plurality of groups of loose tube thickness parameters and the plurality of groups of fullness parameters, a plurality of loose tube thickness parameter sets and a plurality of fullness parameter sets are generated through data integration, further, mean value calculation is performed on the plurality of target optical cables respectively based on the corresponding loose tube thickness parameters to determine thickness uniformity, variance calculation is further performed on the loose tube thickness parameters on the basis to determine parameter data deviation degree, the steps are used as a data analysis process to perform data analysis, exemplarily, analysis results can be orderly expressed through setting analysis grades, analysis results are correspondingly integrated, and a plurality of sample loose tube data analysis results are generated.
Further, a plurality of sample loose tube data analysis results are correlated and corresponding to the plurality of target optical cables, loose tube quality analysis is carried out based on the corresponding results, wherein the higher the thickness uniformity and the smaller the deviation degree, the higher the quality qualification degree of the loose tube is indicated, the quality analysis results of the loose tube are obtained, the data analysis effects of the sample sheaths and the quality analysis results of the sample loose tubes are in one-to-one correspondence, data identification is carried out based on the corresponding results, the data identification is used as the first construction data set, namely initial sample data for training and optimization of the loose tube quality analysis module, and construction of the loose tube quality analysis module is completed according to the first construction sample data so as to carry out quality evaluation and analysis on the loose tube in the target optical cable.
Further, the first construction data set is used to construct the loose tube quality analysis module, and step S415 of the present application further includes:
step S4151: constructing the loose tube quality analysis module based on a BP neural network;
step S4152: and performing iterative supervision training and verification on the loose tube quality analysis module by adopting the first construction data set until the accuracy of the loose tube quality analysis module meets the preset requirement, and obtaining the constructed loose tube quality analysis module.
Specifically, a main module framework of the loose tube quality analysis module is determined based on the BP neural network, a primary loose tube quality analysis module is constructed, the loose tube quality analysis module comprises an input layer, a quality evaluation layer and an output layer, the first constructed data set is further used as sample data and is divided into a training set and a verification set, the data division ratio of the training set and the verification set can be dynamically regulated, the training set and the verification set are input into the constructed primary loose tube quality analysis model, the analysis accuracy of the module is improved by performing module training and verification, the sample division ratio can be re-determined when the trained module analysis accuracy is low, the module training and verification are performed again until the module analysis accuracy reaches a preset requirement, for example, the module analysis accuracy 95% is set as the preset requirement, and a module which reaches the standard after training is used as the constructed loose tube quality analysis module, so that the accuracy of subsequent evaluation and analysis on input parameter data can be improved, and the conformity of an analysis result with the reality is guaranteed.
Further, constructing the sheath quality analysis module, step S420 of the present application further includes:
step S421: obtaining gap parameters of a plurality of target optical cables to obtain a plurality of gap parameter sets;
step S422: performing data analysis on the plurality of gap parameter sets to obtain a plurality of sample sheath data analysis results;
step S423: performing sheath quality analysis evaluation according to the plurality of sample sheath data analysis results to obtain a plurality of sample sheath quality analysis results;
step S424: performing data identification on the multiple sample sheath data analysis results and the multiple sample sheath quality analysis results to obtain a second constructed data set;
step S425: constructing the sheath quality analysis module based on the BP neural network;
step S426: and performing iterative supervision training and verification on the sheath quality analysis module by adopting the second construction data set until the accuracy of the sheath quality analysis module meets the preset requirement, and obtaining the constructed sheath quality analysis module.
Specifically, gap detection of the multiple groups of cable cores and sheaths is performed on the processed target optical cables, wherein each target optical cable corresponds to multiple gap parameters, the multiple target optical cables are subjected to parameter integration to generate multiple gap parameter sets, data analysis is further performed on the multiple gap parameter sets to determine the qualification rate of each gap parameter, distribution uniformity is determined, the multiple target optical cables are subjected to parameter evaluation to generate multiple sample sheath data analysis results, sheath quality analysis is further performed on the multiple target optical cables based on the corresponding data analysis results, the higher the qualification rate of the gap parameters and the distribution uniformity is, the higher the corresponding sheath quality is, multiple sample sheath quality analysis results are obtained, the multiple sample sheath data analysis results correspond to the multiple sample sheath quality analysis results one by one, data identification is performed based on the corresponding results, and the data identification is used as the second construction data set, namely, which is used as initial sample data for sheath quality analysis module training optimization.
Further, analogy is carried out on the construction process of the loose tube quality analysis module, a main body module framework of the sheath quality analysis module is determined based on the BP neural network, a primary sheath quality analysis model is obtained, the second construction data set is further used as sample data, a training set and a verification set are obtained through data division, the data division proportion can be dynamically regulated and controlled, the training set and the verification set are input into the constructed primary sheath quality analysis model, the module training and the verification are carried out to improve the analysis accuracy of the module until the module analysis accuracy reaches a preset requirement, the training is stopped, and the module which reaches the standard after the training is used as the constructed sheath quality analysis module to carry out the sheath quality evaluation and analysis of the target optical cable.
Step S500: performing data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result;
step S600: performing data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and inputting the sheath data analysis result into the sheath quality analysis module to obtain a sheath quality analysis result;
specifically, loose tube thickness and factice fullness detection is carried out on the target optical cable, a plurality of loose tube thickness parameters and a plurality of fullness parameters are obtained, a plurality of gap parameters are determined through sheath gap detection, parameter data analysis is further carried out to obtain a loose tube data analysis result and a sheath data analysis result, further, the loose tube data analysis result and the sheath data analysis result are input into the ribbon optical cable quality analysis model, source calculation is carried out on input data based on a data recognition layer, and the input data are further transmitted to a corresponding quality analysis module.
Inputting the loose tube data analysis result into the loose tube quality analysis module, determining the reference data with the highest fitting degree by analyzing and checking the adaptability result, further performing quality evaluation analysis to generate the loose tube quality analysis result, similarly, inputting the sheath data analysis result into the sheath quality analysis module, determining the sheath quality analysis result by performing the adaptability analysis evaluation, and taking the loose tube quality analysis result and the sheath quality analysis result as the evaluation basis of optical cable processing.
Further, for data analysis of the multiple loose tube thickness parameters and the multiple fullness parameters, step S500 of the present application further includes:
step S510: calculating the mean value of the thickness parameters of the loose tubes to obtain the mean value of the thickness parameters;
step S520: calculating the variance of the thickness parameters of the plurality of loose tubes to obtain the variance of the thickness parameters;
step S530: and taking the thickness parameter mean and the thickness parameter variance as the loose tube data analysis result.
Specifically, the plurality of loose tube thickness parameters of the detected target optical cable are subjected to data analysis, the plurality of loose tube thickness parameters correspond to different detection positions, mean calculation is performed on the plurality of loose tube thickness parameters, the mean value of the thickness parameters of the loose tube is determined to perform overall evaluation, variance calculation is further performed on the plurality of loose tube thickness parameters on the basis of a variance calculation formula on the basis of the mean value of the thickness parameters to determine the deviation degree between the plurality of loose tube thickness parameters and the mean value of the thickness parameters, the variance of the thickness parameters is obtained, the mean value of the thickness parameters and the variance of the thickness parameters are further taken as data analysis results, and similarly, the plurality of fullness parameters and the plurality of gap parameters can be analyzed by referring to the data analysis process, so that the accuracy of the data analysis results can be effectively guaranteed.
Step S700: inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continuously processing and adjusting the target optical cable according to the target optical cable processing evaluation result.
Specifically, the coordinate axis direction is determined based on the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths, a two-dimensional coordinate system is constructed, the coordinate points of sample data are further determined, dot matrix clustering is carried out on the sample loose tubes to determine a plurality of clustering results, quality grades corresponding to the clustering results are determined, the construction of an optical cable processing evaluation space is completed based on a KNN algorithm, the quality analysis results of the loose tubes and the quality analysis results of the sheaths are further input into the optical cable processing evaluation space, a clustering area space where the corresponding coordinate points are located is determined, the corresponding quality grades are used as the processing evaluation results of the target optical cable, the current processing disadvantage is determined based on the processing evaluation results of the target optical cable, the specific processing adjustment direction and scale are determined, and subsequent processing adjustment is carried out on the target optical cable, so that the processing quality of the optical cable is improved, and the application performance of the optical cable is enhanced.
Further, as shown in fig. 3, the loose tube quality analysis result and the sheath quality analysis result are input into the optical cable processing evaluation space to obtain a target optical cable processing evaluation result, where step S700 of the present application further includes:
step S710: obtaining a plurality of sample loose tube quality analysis results and a plurality of sample sheath quality analysis results;
step S720: dividing the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths according to the plurality of target optical fibers to obtain a plurality of optical cable processing data sets;
step S730: constructing a two-dimensional coordinate system based on the quality analysis result of the loose tube and the quality analysis result of the sheath;
step S740: respectively inputting the optical cable processing data sets into the two-dimensional coordinate system to obtain a plurality of sample coordinate points;
step S750: clustering the plurality of sample coordinate points to obtain a plurality of clustering results, and combining the two-dimensional coordinate system to obtain the optical cable processing evaluation space;
step S760: marking the plurality of clustering results based on a plurality of optical cable processing quality grades;
step S770: inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing and evaluating space to obtain a target coordinate point;
step S780: and obtaining a target clustering result where the target coordinate point is located, and outputting the optical cable processing quality grade corresponding to the target clustering result as the target optical cable processing evaluation result.
Specifically, the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths are retrieved by performing parameter detection and quality evaluation on the plurality of target optical cables, and a plurality of target optical fibers corresponding to the plurality of target optical cables are determined, wherein each group of optical cables includes a plurality of optical fibers, the plurality of target optical fibers are used as data division standards, the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths are divided, and then the division results and the plurality of target optical fibers are correspondingly identified, so that the plurality of optical cable processing data sets are generated.
Further, the loose tube quality analysis result and the sheath quality analysis result are used as coordinate axes, wherein a horizontal axis and a vertical axis are not fixedly limited, a two-dimensional coordinate system is constructed, the two-dimensional coordinate system is an initial analysis space constructed, the plurality of optical cable processing data sets are input into the two-dimensional coordinate system, coordinate positions corresponding to the data are determined, the plurality of sample coordinate points are obtained, the plurality of sample coordinate points are distributed in the two-dimensional coordinate system, coordinate point clustering is further performed according to relative distances among the coordinate points based on a dot matrix diagram in the two-dimensional coordinate system, the closer the relative distances are, the higher the similarity of the optical cable processing data indicated by the corresponding coordinate points is, the dot matrix diagram is divided into a plurality of clustering modules which are used as the plurality of clustering results, the optical cable processing evaluation space is further constructed by combining the two-dimensional coordinate system, and the plurality of clustering results correspond to different evaluation levels.
Further, a plurality of grading intervals are determined based on loose tube quality analysis results and sheath quality analysis results, a plurality of optical cable processing quality grades are determined based on the grading intervals, the clustering results are subjected to grade matching based on the optical cable processing quality grades, the clustering results are marked based on the grade matching results, the loose tube quality analysis results and the sheath quality analysis results are further input into the optical cable processing evaluation space, corresponding coordinate positions serve as the target coordinate points, then clustering result areas corresponding to the target coordinate points are determined, the optical cable processing quality grades corresponding to the areas serve as the processing evaluation results of the target optical cables, and the optical cable processing evaluation space is constructed, so that the analysis efficiency can be improved on the basis of guaranteeing the evaluation accuracy.
Example two
Based on the same inventive concept as the processing method of a highly integrated optical ribbon cable in the foregoing embodiment, as shown in fig. 4, the present application provides a processing system of a highly integrated optical ribbon cable, the system including:
the technical processing module 11 is used for processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers;
the parameter detection module 12 is configured to perform multiple thickness detections on the loose tube in the target optical cable after the preliminary processing is completed, to obtain multiple loose tube thickness parameters, and perform multiple factice fullness detections on the loose tube, to obtain multiple fullness parameters;
the gap detection module 13 is used for detecting gaps of the sheath and the plurality of cable cores in the target optical cable to obtain a plurality of gap parameters;
a model building module 14, wherein the model building module 14 is used for building a ribbon cable quality analysis model, wherein the ribbon cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module;
the loose tube quality determining module 15 is used for performing data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result;
the sheath quality determination module 16 is configured to perform data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and input the sheath data analysis result to the sheath quality analysis module to obtain a sheath quality analysis result;
and the processing adjusting module 17 is configured to input the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continue to process and adjust the target optical cable according to the target optical cable processing evaluation result.
Further, the system further comprises:
the loose tube quality analysis module construction module is used for constructing the loose tube quality analysis module;
a sheath quality analysis module construction module for constructing the sheath quality analysis module;
and the quality analysis model building module is used for obtaining the built ribbon optical cable quality analysis model according to the built loose tube quality analysis module and the built sheath quality analysis module.
Further, the system further comprises:
the system comprises a parameter acquisition module, a parameter storage module and a parameter processing module, wherein the parameter acquisition module is used for acquiring loose tube thickness parameters and fullness parameters of a plurality of target optical cables to obtain a plurality of loose tube thickness parameter sets and a plurality of fullness parameter sets;
the data analysis module is used for carrying out data analysis on the plurality of loose tube thickness parameter sets and the plurality of fullness parameter sets to obtain a plurality of sample loose tube data analysis results;
the loose tube sample evaluation module is used for carrying out loose tube quality evaluation analysis according to the analysis results of the plurality of sample loose tube data to obtain a plurality of sample loose tube quality analysis results;
a first construction data set acquisition module, configured to perform data identification on the multiple sample sheath data analysis effects and the multiple sample loose tube quality analysis results to obtain a first construction data set;
and the loose tube quality analysis module acquisition module is used for constructing the loose tube quality analysis module by adopting the first construction data set.
Further, the system further comprises:
the loose tube quality analysis module establishment module is used for establishing the loose tube quality analysis module based on a BP neural network;
and the training verification module is used for performing iterative supervision training and verification on the loose tube quality analysis module by adopting the first construction data set until the accuracy of the loose tube quality analysis module meets the preset requirement, and obtaining the constructed loose tube quality analysis module.
Further, the system further comprises:
the device comprises a gap parameter acquisition module, a gap parameter acquisition module and a control module, wherein the gap parameter acquisition module is used for acquiring gap parameters of a plurality of target optical cables and acquiring a plurality of gap parameter sets;
the parameter analysis module is used for carrying out data analysis on the plurality of gap parameter sets to obtain a plurality of sample sheath data analysis results;
the sheath quality evaluation module is used for carrying out sheath quality analysis evaluation according to the multiple sample sheath data analysis results to obtain multiple sample sheath quality analysis results;
a second constructed data set obtaining module, configured to perform data identification on the multiple sample sheath data analysis results and the multiple sample sheath quality analysis results to obtain a second constructed data set;
the sheath quality analysis module establishment module is used for establishing the sheath quality analysis module based on a BP neural network;
and the sheath quality analysis module optimization module is used for performing iterative supervision training and verification on the sheath quality analysis module by adopting the second construction data set until the accuracy of the sheath quality analysis module meets the preset requirement, and obtaining the constructed sheath quality analysis module.
Further, the system further comprises:
the mean value calculating module is used for calculating the mean value of the plurality of loose tube thickness parameters to obtain a mean value of the thickness parameters;
the variance calculation module is used for calculating the variance of the plurality of loose tube thickness parameters to obtain thickness parameter variance;
a result determination module to take the thickness parameter mean and the thickness parameter variance as the loose tube data analysis results.
Further, the system further comprises:
an analysis result acquisition module for acquiring the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths;
the result dividing module is used for dividing the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths according to the plurality of target optical fibers to obtain a plurality of optical cable processing data sets;
the coordinate system construction module is used for constructing a two-dimensional coordinate system based on the quality analysis result of the loose tube and the quality analysis result of the sheath;
the coordinate point acquisition module is used for respectively inputting the optical cable processing data sets into the two-dimensional coordinate system to obtain a plurality of sample coordinate points;
the space generation module is used for clustering the plurality of sample coordinate points to obtain a plurality of clustering results, and the optical cable processing evaluation space is obtained by combining the two-dimensional coordinate system;
a result marking module for marking the plurality of clustering results based on a plurality of optical cable processing quality grades;
the target coordinate point acquisition module is used for inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target coordinate point;
and the result output module is used for obtaining a target clustering result where the target coordinate point is located and outputting the optical cable processing quality grade corresponding to the target clustering result as the target optical cable processing evaluation result.
In the present specification, through the foregoing detailed description of the processing method of the highly integrated optical fiber ribbon cable, it is clear to those skilled in the art that the processing method and system of the highly integrated optical fiber ribbon cable in the present embodiment are disclosed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of processing a highly integrated ribbon cable, the method comprising:
processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly-integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers;
after the preliminary processing is finished, carrying out multiple thickness detection on the loose tube in the target optical cable to obtain multiple loose tube thickness parameters, and carrying out multiple factice fullness detection in the loose tube to obtain multiple fullness parameters;
detecting the gaps between the sheath and the plurality of cable cores in the target optical cable to obtain a plurality of gap parameters;
constructing a ribbon cable quality analysis model, wherein the ribbon cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module;
performing data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result;
performing data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and inputting the sheath data analysis result into the sheath quality analysis module to obtain a sheath quality analysis result;
inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continuously processing and adjusting the target optical cable according to the target optical cable processing evaluation result.
2. The method of claim 1, wherein constructing a ribbon quality analysis model comprises:
constructing the loose tube quality analysis module;
constructing the sheath quality analysis module;
and obtaining the constructed ribbon optical cable quality analysis model according to the constructed loose tube quality analysis module and the sheath quality analysis module.
3. The method of claim 2, wherein constructing the loose tube quality analysis module comprises:
acquiring loose tube thickness parameters and fullness parameters of a plurality of target optical cables to obtain a plurality of loose tube thickness parameter sets and a plurality of fullness parameter sets;
performing data analysis on the plurality of loose tube thickness parameter sets and the plurality of fullness parameter sets to obtain a plurality of sample loose tube data analysis results;
performing loose tube quality evaluation analysis according to the analysis results of the plurality of sample loose tube data to obtain a plurality of sample loose tube quality analysis results;
performing data identification on the multiple sample sheath data analysis effects and the multiple sample loose tube quality analysis results to obtain a first constructed data set;
and constructing the loose tube quality analysis module by adopting the first construction data set.
4. The method of claim 3, wherein constructing the loose tube quality analysis module using the first construction data set comprises:
constructing the loose tube quality analysis module based on a BP neural network;
and performing iterative supervision training and verification on the loose tube quality analysis module by adopting the first construction data set until the accuracy of the loose tube quality analysis module meets the preset requirement, and obtaining the constructed loose tube quality analysis module.
5. The method of claim 2, wherein constructing the sheath quality analysis module comprises:
obtaining gap parameters of a plurality of target optical cables to obtain a plurality of gap parameter sets;
performing data analysis on the plurality of gap parameter sets to obtain a plurality of sample sheath data analysis results;
performing sheath quality analysis evaluation according to the plurality of sample sheath data analysis results to obtain a plurality of sample sheath quality analysis results;
performing data identification on the multiple sample sheath data analysis results and the multiple sample sheath quality analysis results to obtain a second constructed data set;
constructing the sheath quality analysis module based on the BP neural network;
and performing iterative supervision training and verification on the sheath quality analysis module by adopting the second construction data set until the accuracy of the sheath quality analysis module meets the preset requirement, and obtaining the constructed sheath quality analysis module.
6. The method of claim 5, wherein performing data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters comprises:
calculating the mean value of the thickness parameters of the plurality of loose tubes to obtain the mean value of the thickness parameters;
calculating the variance of the thickness parameters of the plurality of loose tubes to obtain the variance of the thickness parameters;
and taking the thickness parameter mean and the thickness parameter variance as the loose tube data analysis result.
7. The method of claim 6, wherein inputting the loose tube quality analysis results and the sheath quality analysis results into a cable processing evaluation space to obtain target cable processing evaluation results comprises:
obtaining a plurality of sample loose tube quality analysis results and a plurality of sample sheath quality analysis results;
dividing the quality analysis results of the plurality of sample loose tubes and the quality analysis results of the plurality of sample sheaths according to the plurality of target optical fibers to obtain a plurality of optical cable processing data sets;
constructing a two-dimensional coordinate system based on the quality analysis result of the loose tube and the quality analysis result of the sheath;
respectively inputting the optical cable processing data sets into the two-dimensional coordinate system to obtain a plurality of sample coordinate points;
clustering the plurality of sample coordinate points to obtain a plurality of clustering results, and combining the two-dimensional coordinate system to obtain the optical cable processing evaluation space;
marking the plurality of clustering results based on a plurality of optical cable processing quality grades;
inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing and evaluating space to obtain a target coordinate point;
and obtaining a target clustering result where the target coordinate point is located, and outputting the optical cable processing quality grade corresponding to the target clustering result as the target optical cable processing evaluation result.
8. A system for processing highly integrated ribbon fiber cables, the system comprising:
the technical processing module is used for processing a target optical cable by adopting a preset manufacturing process, wherein the target optical cable is a highly integrated ribbon optical cable and comprises a plurality of groups of cable cores, and each group of optical cable comprises a plurality of optical fibers;
the parameter detection module is used for detecting the thicknesses of the loose tubes in the target optical cable at multiple positions after the preliminary processing is finished to obtain multiple loose tube thickness parameters, and detecting the fullness of ointment at multiple positions in the loose tubes to obtain multiple fullness parameters;
the gap detection module is used for detecting the gaps between the sheath and the plurality of cable cores in the target optical cable to obtain a plurality of gap parameters;
the optical fiber ribbon quality analysis system comprises a model construction module, a data analysis module and a data analysis module, wherein the model construction module is used for constructing a ribbon optical cable quality analysis model, and the ribbon optical cable quality analysis model comprises a loose tube quality analysis module and a sheath quality analysis module;
the loose tube quality determination module is used for carrying out data analysis on the plurality of loose tube thickness parameters and the plurality of fullness parameters to obtain a loose tube data analysis result, and inputting the loose tube data analysis result into the loose tube quality analysis module to obtain a loose tube quality analysis result;
the sheath quality determining module is used for carrying out data analysis on the plurality of gap parameters to obtain a sheath data analysis result, and inputting the sheath data analysis result into the sheath quality analysis module to obtain a sheath quality analysis result;
and the processing adjustment module is used for inputting the quality analysis result of the loose tube and the quality analysis result of the sheath into an optical cable processing evaluation space to obtain a target optical cable processing evaluation result, and continuously processing and adjusting the target optical cable according to the target optical cable processing evaluation result.
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