A self-cleaning portable optical fiber fusion method and system

Abstract

The application discloses a kind of self-cleaning portable optical fiber fusion method and system, method includes: S100, start optical fiber fusion device, its built-in environmental sensing starts monitoring and records current environmental condition, automatically adjusts air cleanliness;S200, automatically identify the type of optical fiber to be fused, the processing including peeling, cutting and cleaning to optical fiber to be fused is carried out, monitors optical fiber processing process, ensures that cutting surface is flat;S300, start fusion program, optical fiber fusion ware executes fusion task, in the process of fusion, key indicators including discharge intensity, fusion speed are tracked throughout the process;S400, after fusion is completed, the quality of junction is analyzed using image recognition technology, to determine whether it meets the standard;S500, record the data including environmental conditions, parameters used, fusion time and quality evaluation results when each time is fused, according to the accumulated data, predict the wear condition of component. Meet the high-cleanliness fusion needs of special optical fiber in outdoor and emergency situations.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of optical fiber fusion, and more particularly to a self-cleaning portable optical fiber fusion method and system. BACKGROUND

[0002] At present, the application field of high-power laser is expanding, including biological medicine, sensing detection, information confrontation, laser processing, etc. These fields have higher requirements for optical fiber fusion technology, especially in outdoor and emergency situations. The existing portable optical fiber fusion machine integrates the optical fiber fusion machine with the storage box. The handle can drive the entire optical fiber fusion machine and the box to move, achieving portability. However, high-precision and high-efficiency optical fiber fusion machines generally have complex internal structures, requiring intelligent operation systems and high-precision optical systems and mechanical structures. The high-precision optical system is used to realize optical fiber recognition and contour alignment to ensure fusion quality. The discharge voltage and discharge position are automatically corrected by recognizing environmental temperature and air pressure changes. However, the above optical fiber fusion equipment cannot meet the high-cleanness fusion requirements of special optical fibers in outdoor and emergency situations.

[0003] There is a combined optical fiber fusion operation box on the market, which includes a shell, a cavity inside the shell, a stripping mechanism, a second cleaning mechanism, a first cleaning mechanism, a collection mechanism, a cutting mechanism, and a monitoring mechanism. The application can automatically fuse optical fibers, improving the efficiency of optical fiber fusion. The stripping pawl can automatically strip the optical fiber jacket, avoiding the need for manual stripping of the optical fiber, improving fusion efficiency and reducing worker's work intensity. The arc-shaped cotton strip automatically cleans the bare fiber, removing the coating layer on the surface of the bare fiber. The motor and cutting knife can automatically or manually cut the optical fiber. The operator can choose different cutting methods according to the environment. Finally, the scraper can automatically clean the inside of the fusion machine before fusing the bare fiber.

[0004] The optical fiber fusion operation box can automatically clean the bare fiber, removing the coating layer on the surface of the bare fiber, preventing the fiber from being contaminated twice. However, only the optical fiber itself is cleaned, and the fusion environment of the optical fiber is not cleaned, which is affected by the external environment. When the external environment has debris or the air cleanliness is not high, it will cause excessive fusion loss, fiber light overflow, and heat accumulation, which not only affects the optical performance of the system, but also may cause safety accidents and economic losses. Therefore, a self-cleaning portable optical fiber fusion method and system automatically monitor the environmental cleanliness during the optical fiber fusion process, meeting the high-cleanness fusion requirements of special optical fibers in outdoor and emergency situations. SUMMARY

[0005] In view of the above defects or improvement needs of the prior art, the present application provides a self-cleaning portable optical fiber fusion method and system, which monitors and records the current environmental conditions through the built-in environmental sensor, automatically adjusts the air cleanliness through the sensor data, and automatically monitors the environmental cleanliness, so as to meet the high cleanliness fusion requirements of special optical fibers in outdoor and emergency situations. By automatically identifying the type of optical fiber to be fused and calling the corresponding best matching data, precise parameter setting is realized, human error and debugging time are reduced, the success rate and efficiency of fusion are improved, and it is ensured that different types of optical fibers can be optimally processed, and the overall production quality is significantly improved.

[0006] To achieve the above object, according to a first aspect of an embodiment of the present application, a self-cleaning portable optical fiber fusion method is provided, comprising the following steps:

[0007] S100, starting the optical fiber fusion device, the built-in environmental sensor starts monitoring and recording the current environmental conditions, and automatically adjusting the air cleanliness through the environmental sensor data;

[0008] S200, automatically identifying the type of optical fiber to be fused, and performing processing including peeling, cutting and cleaning on the optical fiber to be fused, monitoring the optical fiber processing process, and ensuring the flatness of the cutting surface;

[0009] S300, starting the fusion program, the optical fiber fusion device performs the fusion task, and during the fusion process, the key indicators including discharge intensity and fusion speed are tracked;

[0010] S400, after the fusion is completed, the image recognition technology is applied to analyze the quality of the joint point, and it is judged whether it meets the standard;

[0011] S500, recording the data including environmental conditions, used parameters, fusion time and quality evaluation results during each fusion, and predicting the wear condition of the components according to the accumulated data.

[0012] Further, during the entire fusion process, the fusion environment needs to be continuously monitored and controlled according to the steps in step S100, the optical fiber fusion device is a closed device, which is provided with an air purifier and a temperature and humidity controller, and step S100 specifically comprises:

[0013] S110, the operator turns on the power switch, and the system performs self-checking to check whether all hardware and software are normally operated;

[0014] S120, the built-in environmental sensor starts monitoring and recording the current environmental conditions;

[0015] S130, the air purification device is automatically started to ensure that the air cleanliness meets the requirements, and the operator is prompted that the environmental preparation is completed;

[0016] S140, the temperature and humidity controller adjusts the environmental parameters according to the sensor data to maintain the micro-positive pressure state to prevent external pollutants from entering.

[0017] Further, in step S130, the air cleanliness is adjusted by a sliding mode control, wherein the switching function is:

[0018] ,

[0019] wherein, is the difference between the current cleanliness and the target value,

[0020] is a constant, determining the slope of the sliding surface and the response speed of the system;

[0021] The difference between the current cleanliness and the target value is specifically:

[0022] ,

[0023] wherein, is the target cleanliness,

[0024] is the current cleanliness.

[0025] Further, in step S140, a fuzzy set is also needed to be established, mapping the specific values of the environmental parameters to the fuzzy set, and making each specific value of the environmental parameters correspond to the temperature label, the humidity label and the pressure label, and then establishing a fuzzy rule base, adjusting the output variables according to the labels in the fuzzy set to control the adjustment of the environmental parameters.

[0026] The output in the fuzzy rule base is a fuzzy output, which needs to be converted into an actual operable value by defuzzification, specifically:

[0027] ,

[0028] wherein, is the output value corresponding to each rule in the fuzzy rule base,

[0029] is the membership degree of the output value.

[0030] Further, before running, it also includes:

[0031] S600, a large number of optical fiber fusion videos are collected, and the corresponding device parameters, environmental conditions and optical fiber information during fusion are collected, and after being sorted, a comparison set is established, and the data required for different optical fiber fusion is learned through the comparison set.

[0032] Further, in step S600, specifically further comprising:

[0033] S610, determining the type of data needed to be collected, including but not limited to fusion video, device parameters, environmental conditions, and fiber information;

[0034] S620, collecting successful and failed cases covering different types of optical fibers, various environmental conditions, and using different device parameters;

[0035] S630, preliminary processing of the collected raw data, removing irrelevant or redundant information, and adding labels to each set of data according to the quality of fusion;

[0036] S640, preprocessing the data in the control set to ensure that the data format input to the machine learning algorithm is uniform and effective;

[0037] S650, establishing a model, using the data in the control set to train the model through machine learning;

[0038] S660, regularly updating the control set by adding the latest fusion cases to maintain the timeliness and adaptability of the model.

[0039] Further, in step S200, the processing of the optical fiber to be fused specifically includes:

[0040] S210, stripping the optical fiber, the system monitors the stripping process in real time to ensure that the optical fiber core is not damaged;

[0041] S220, cutting the optical fiber end face, the system uses high-precision cutting tools to ensure that the cutting surface is smooth and flawless, while monitoring the cutting angle and quality;

[0042] S230, cleaning the optical fiber end face to remove any residue or contamination and ensuring the cleanliness of the optical fiber surface before fusion;

[0043] S240, placing the processed optical fiber in a V-shaped groove and fixing it with a clamp, preparing for the fusion stage.

[0044] Further, in step S300, specifically comprising the following steps:

[0045] S310, according to the automatically identified type and characteristics of the optical fiber to be fused, retrieve the ten most similar sets of data in the control set as the similarity set;

[0046] S320, further weight calculation and scoring of the data in the similarity set, and selecting the set of data closest to the optical fiber to be fused;

[0047] S330, retrieve a set of data closest to the to-be-fused optical fiber, and correct the data according to specific information of the to-be-fused optical fiber, so as to serve as setting data for the to-be-fused optical fiber;

[0048] S340, adjust parameters for the fusion according to the setting data, and start the fusion process by the optical fiber fusion device according to the adjusted parameters;

[0049] S350, track key data in the fusion process, and automatically adjust the fusion parameters according to abnormal conditions, and stop discharging and release the optical fiber after the fusion is completed.

[0050] Further, the step S400 specifically comprises the following steps:

[0051] S410, capture a high-definition image of the optical fiber fusion point, and perform preprocessing including denoising, contrast enhancement, and edge detection on the high-definition image;

[0052] S420, automatically locate the fusion point, extract key features of the fusion point, and measure key size parameters of the fusion point;

[0053] S430, calculate quality indexes of the fusion point including alignment error, fusion strength estimation, and surface smoothness according to the extracted features and size parameters;

[0054] S440, compare the calculated quality indexes with a preset standard range, and automatically judge whether the fusion quality is qualified according to a comparison result;

[0055] S450, if not qualified, provide detailed improvement suggestions according to the quality indexes, adjust the fusion parameters, the optical fiber preprocessing method, and the environmental conditions, and perform the fusion again.

[0056] According to a second aspect of the embodiment of the present application, a self-cleaning portable optical fiber fusion system is provided, comprising:

[0057] A first cleaning module is configured to start the optical fiber fusion device, and an environment sensor built in the optical fiber fusion device starts to monitor and record current environmental conditions, and air cleanliness is automatically adjusted through the environment sensor data;

[0058] A second cleaning module is configured to automatically identify a type of the to-be-fused optical fiber, and perform processing including peeling, cutting, and cleaning on the to-be-fused optical fiber, and monitor the optical fiber processing process to ensure that the cutting surface is flat;

[0059] An optical fiber fusion module is configured to start a fusion program, and an optical fiber fusion device performs a fusion task, and key indexes including discharge strength and fusion speed are tracked throughout the fusion process.

[0060] Quality detection module: used for analyzing the quality of the contact after fusion, judging whether it meets the standard or not;

[0061] Data recording module: used for recording data including environmental conditions, used parameters, fusion time and quality evaluation results during each fusion, predicting the wear condition of the assembly according to the accumulated data;

[0062] Data sorting module: used for collecting a large number of optical fiber fusion videos, and corresponding device parameters, environmental conditions and optical fiber information, establishing a contrast set after sorting, and learning the data required for different optical fiber fusion through the contrast set.

[0063] Overall, compared with the prior art, the above technical solutions conceived by the present application can achieve the following beneficial effects:

[0064] 1. The optical fiber fusion method of the present application monitors and records the current environmental conditions through the built-in environmental sensor, automatically adjusts the air cleanliness through the environmental sensor data, and automatically monitors the environmental cleanliness, so as to meet the high cleanliness fusion requirements of special optical fibers in outdoor and emergency situations.

[0065] 2. The optical fiber fusion method of the present application realizes precise parameter setting by automatically identifying the type of optical fiber to be fused and retrieving the corresponding best matching data, reduces human error and debugging time, not only improves the success rate and efficiency of fusion, but also ensures that different types of optical fibers can be optimally processed, significantly improving the overall production quality.

[0066] 3. The optical fiber fusion method of the present application realizes scientific optimization of fusion parameters by selecting the most similar data from the contrast set as a reference, ensures the consistency and reliability of each fusion process, and adapts to the needs of various optical fiber types, reduces trial and error costs, and greatly improves the flexibility and accuracy of fusion operation.

[0067] 4. The optical fiber fusion method of the present application realizes the stability and high-quality output of the fusion process by monitoring the key data during the fusion process in real time and automatically adjusting the fusion parameters according to abnormal conditions, can timely respond to environmental changes and equipment fluctuations, ensures that each fusion reaches the best effect, and reduces the scrap rate.

[0068] 5. The optical fiber fusion method of the present application realizes fine management of the fusion process by tracking the temperature, humidity, current and other key parameters during the fusion process, and introducing a feedback control system, not only ensures the high precision and stability of the fusion process, but also enhances the self-adaptability of the system, greatly improves the product quality and production efficiency.

[0069] 6. The fiber optic fusion splicing method of the present invention achieves preventive maintenance and extends equipment life by recording key data of each fusion splice and predicting component wear based on the accumulated data. It also enables advance planning of maintenance and replacement schedules, reduces unexpected downtime, significantly lowers operating costs, and improves equipment reliability and availability.

[0070] 7. The fiber optic fusion splicing method of the present invention, through systematic recording and analysis of data from each fusion splice, enables continuous improvement of processes and technologies, supports long-term data analysis and model optimization, promotes the advancement of fusion splicing technology, ensures continuous upgrading of production processes, and lays a solid foundation for future innovation and development. Attached Figure Description

[0071] Figure 1 This is a schematic flowchart of a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram illustrating the steps of a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of step S100 in a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of step S600 in a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0075] Figure 5 This is a schematic diagram of step S200 in a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0076] Figure 6 This is a schematic diagram of step S300 in a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention;

[0077] Figure 7 This is a schematic diagram of step S400 in a self-cleaning portable optical fiber fusion splicing method according to an embodiment of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0079] Example 1

[0080] like Figure 1 , 2As shown, the embodiment of the present application provides a self-cleaning portable optical fiber fusion method, comprising the following steps:

[0081] S100, start the optical fiber fusion device, the built-in environmental sensor starts to monitor and record the current environmental conditions, and the air cleanliness is automatically adjusted through the environmental sensor data;

[0082] S200, automatically identify the type of optical fiber to be fused, and perform processing including peeling, cutting and cleaning on the optical fiber to be fused, monitor the optical fiber processing process, and ensure the flatness of the cutting surface;

[0083] S300, start the fusion program, the optical fiber fusion device performs the fusion task, and during the fusion process, the key indicators including discharge intensity and fusion speed are tracked;

[0084] S400, after the fusion is completed, the image recognition technology is applied to analyze the quality of the contact point, and whether it meets the standard is judged;

[0085] S500, record the data including environmental conditions, used parameters, fusion time and quality evaluation results during each fusion, and predict the wear condition of the component according to the accumulated data.

[0086] As shown Figure 3 During the entire fusion process, the fusion environment needs to be continuously monitored and controlled according to the steps in step S100. The optical fiber fusion device is a closed device, which is provided with an air purifier and a temperature and humidity controller. Step S100 specifically includes:

[0087] S110, the operator turns on the power switch, and the system performs self-checking to check whether all hardware and software are normally operated;

[0088] S120, the built-in environmental sensor starts to monitor and record the current environmental conditions;

[0089] S130, the air purification device is automatically started to ensure that the air cleanliness meets the requirements, and the operator is prompted that the environment is ready;

[0090] S140, the temperature and humidity controller adjusts the environmental parameters according to the sensor data to maintain a slight positive pressure state to prevent external pollutants from entering.

[0091] In step S130, the air cleanliness is adjusted by sliding mode control, wherein the switching function is:

[0092] ,

[0093] wherein, is the difference between the current cleanliness and the target value,

[0094] is constant, determining the slope of the sliding surface and the response speed of the system.

[0095] the difference between the current cleanliness and the target value , in particular:

[0096] ,

[0097] wherein is the target cleanliness,

[0098] is the current cleanliness.

[0099] in order to bring the control state of the switching function onto and keep it on the sliding surface, a control law is required, in particular:

[0100] ,

[0101] wherein is the equivalent control, used to ensure that the behavior of the system on the sliding surface is as expected,

[0102] is the discontinuity, used to overcome uncertainties and disturbances, forcing the system into the sliding mode.

[0103] the equivalent control is:

[0104] ,

[0105] wherein is the natural decay rate or growth rate of the system,

[0106] is the coefficient of the influence of the control input on the state of the system;

[0107] the discontinuity is:

[0108] ,

[0109] wherein is a gain coefficient,

[0110] is a sign function, used to provide sufficient counterforce to pull the system back onto the sliding surface.

[0111] In step S140, a fuzzy set is also established, the specific values of the environment parameters are mapped into the fuzzy set, and each specific value of the environment parameters is corresponded to the temperature label, the humidity label and the pressure label, a fuzzy rule base is further established, and the output variable is adjusted according to the labels in the fuzzy set to control and adjust the environment parameters. The output in the fuzzy rule base is a fuzzy output, and the fuzzy output needs to be converted into an actual operable value by defuzzification. Specifically, the fuzzy output is converted into an actual operable value by defuzzification.

[0112] ,

[0113] wherein, is the output value corresponding to each rule in the fuzzy rule base,

[0114] is the membership degree of the output value.

[0115] The optical fiber fusion method provided by the embodiment of the present application further comprises the following steps before running:

[0116] S600, a large number of optical fiber fusion videos are collected, and the corresponding device parameters, environment conditions and optical fiber information are collected, and the collected data are sorted and established into a comparison set, and the data required for different optical fiber fusion are learned through the comparison set.

[0117] As shown in Figure 4 , in step S600, the following steps are further included:

[0118] S610, the type of data to be collected is determined, including but not limited to fusion video, device parameter, environment condition and optical fiber information;

[0119] S620, successful and failed cases covering different types of optical fibers, various environment conditions and different device parameters are collected;

[0120] S630, the collected raw data are preliminarily processed, irrelevant or redundant information is removed, and a label is added to each group of data according to the quality of fusion;

[0121] S640, the data in the comparison set are preprocessed to ensure that the data format input into the machine learning algorithm is uniform and effective;

[0122] S650, a model is established, and the data in the comparison set are used to train the model through machine learning;

[0123] S660, the comparison set is updated regularly, the latest fusion cases are added, and the timeliness and adaptability of the model are maintained.

[0124] Step S630 specifically involves: identifying and removing outliers that may affect model performance; imputing records containing missing values; selecting the most influential features using feature selection techniques; reducing dimensionality using dimensionality reduction techniques while preserving important information; and adding labels to each data set based on the fusion quality results. To ensure that different features are on the same scale, the data also needs to be standardized or normalized.

[0125] In step S640, when preprocessing the data in the control set, Mahalanobis distance is used to measure whether a point is an outlier for the multidimensional data, and outliers are removed. Specifically:

[0126] ,

[0127] in, For the first One sample,

[0128] It is the mean vector.

[0129] Let covariance matrix be the variance matrix.

[0130] For the determination value, if If the value is greater than the threshold, the sample is considered an outlier.

[0131] like Figure 5 As shown, in step S200, the processing of the optical fiber to be fused specifically includes:

[0132] S210. The system removes the outer sheath of the optical fiber and monitors the process in real time to ensure that the optical fiber core is not damaged.

[0133] S220, Cut the fiber end face. The system uses high-precision cutting tools to ensure that the cut surface is flat and flawless, while monitoring the cutting angle and quality.

[0134] S230. Clean the fiber end face to remove any residue or contamination and ensure that the fiber surface is clean before fusion splicing.

[0135] S240. Place the processed optical fiber in the V-groove and fix it with a clamp, ready to enter the fusion splicing stage.

[0136] like Figure 6 As shown, step S300 specifically includes the following steps:

[0137] S310. Based on the automatically identified type and characteristics of the optical fiber to be fused, retrieve the ten most similar sets of data from the comparison set as the similarity set.

[0138] S320, further weight calculation is performed on the data in the similar set, and the data is scored to select a group of data closest to the optical fiber to be spliced;

[0139] S330, the group of data closest to the optical fiber to be spliced is called, and the data is corrected according to the specific information of the optical fiber to be spliced to serve as the setting data of the optical fiber to be spliced;

[0140] S340, the parameters of the splicing are adjusted according to the setting data, and the optical fiber splicing device performs discharge according to the adjusted parameters to start the splicing process;

[0141] S350, key data in the splicing process is tracked throughout the process, and the splicing parameters are automatically adjusted according to abnormal conditions, and after the splicing is completed, the discharge is stopped and the optical fiber is released.

[0142] In step S310, Euclidean distance is used to measure the similarity of the data in the control set and the characteristics of the optical fiber to be spliced, specifically:

[0143] ,

[0144] wherein, is the feature vector of the optical fiber to be spliced,

[0145] is the feature vector of the optical fiber in the group of data in the control set,

[0146] , is the , th element in the feature vector,

[0147] is the number of features;

[0148] The smaller the value is, the higher the similarity is.

[0149] In step S320, the scoring method is weighted scoring, specifically:

[0150] ,

[0151] wherein, is the weight of the th element,

[0152] The group of data with the highest score is selected according to

[0153] In step S330, specifically, the group of data closest to the optical fiber to be spliced is called ​​, according to the specific information of the to-be-welded optical fiber, the modified data is:

[0154] ,

[0155] wherein, is a correction increment, specifically:

[0156] ,

[0157] wherein, is a scaling factor for adjusting the correction strength,

[0158] is the feature vector of the group of data closest to the to-be-welded optical fiber selected from the control set.

[0159] In step S350, when an anomaly is detected, the particle swarm optimization algorithm is used to dynamically adjust the welding parameters to restore the normal welding process, specifically including:

[0160] a. Define the position and velocity vector of the particle in the to-be-welded optical fiber:

[0161] ,

[0162] wherein, represents uniform distribution,

[0163] represents the position range,

[0164] represents the speed range;

[0165] b. Each particle updates its position and speed according to its own best position and the global best position :

[0166] ,

[0167] ,

[0168] wherein, is the inertia weight,

[0169] , is the acceleration coefficient,

[0170] , is a random number.

[0171] c. Introduce a predictive maintenance mechanism to predict possible failures in advance:

[0172] ,

[0173] wherein, is the predicted value of the future time step,

[0174] is the key data of the current time,

[0175] is the size of the time window;

[0176] d. adjusting the fusion parameters according to the prediction results to ensure that the fusion process is always in the best state, and fusing the predicted value and the actual measured value, specifically:

[0177] ,

[0178] wherein, estimated state vector,

[0179] is the predicted state vector,

[0180] Kalman gain,

[0181] is the measurement value,

[0182] is the observation matrix.

[0183] As shown in Figure 7 , in step S400, the following steps are specifically included:

[0184] S410, capturing a high-definition image of the fiber fusion point and performing preprocessing including denoising, contrast enhancement and edge detection on the image;

[0185] S420, automatically positioning the fusion point, extracting key features of the fusion point, and measuring key size parameters of the fusion point;

[0186] S430, calculating quality indicators of the fusion point including alignment error, fusion strength estimate and surface smoothness according to the extracted features and size parameters;

[0187] S440, comparing the calculated quality indicators with the preset standard range, and automatically judging whether the fusion quality is qualified according to the comparison result;

[0188] S450, if not qualified, providing detailed improvement suggestions according to the quality indicators, adjusting the fusion parameters, fiber preprocessing methods and environmental conditions, and re-fusing.

[0189] In step S500, the wear condition of the component is predicted according to the accumulated data, and the wear prediction process is described by using the cumulative damage theory, specifically:

[0190] ,

[0191] wherein, is the damage caused by the nth welding to the component, which is calculated according to the welding parameters and environmental conditions, and is optimized by minimizing the error function:

[0192] ,

[0193] Not only can the key data of each welding be recorded, but also the wear condition of the component can be predicted based on the accumulated data to ensure that the equipment is always in the best working condition. At the same time, complex control formulas and real-time monitoring mechanisms are introduced to improve the reliability and maintenance efficiency of the system.

[0194] The data recorded in step S500 is synchronized to the control set for perfecting the data and improving the accuracy during welding.

[0195] Embodiment 2

[0196] The embodiment of the application provides a self-cleaning portable optical fiber welding system, which comprises the following steps:

[0197] The first cleaning module is used to start the optical fiber welding device, and the built-in environmental sensor starts to monitor and record the current environmental conditions, and the air cleanliness is automatically adjusted through the environmental sensor data;

[0198] The second cleaning module is used to automatically identify the type of optical fiber to be welded, and the optical fiber to be welded is processed including peeling, cutting and cleaning, and the optical fiber processing process is monitored to ensure the flatness of the cutting surface;

[0199] The optical fiber welding module is used to start the welding program, and the optical fiber welding device performs the welding task, and during the welding process, the key indicators including the discharge intensity and the welding speed are tracked throughout the process;

[0200] The quality detection module is used to analyze the quality of the joint after the welding is completed by using image recognition technology, and to judge whether it meets the standard;

[0201] The data recording module is used to record the data including the environmental conditions, the parameters used, the welding time and the quality evaluation results during each welding, and to predict the wear condition of the component according to the accumulated data;

[0202] ​Data collation module: used to collect a large number of optical fiber fusion video, and the corresponding fusion equipment parameters, environmental conditions, optical fiber information, after collation, establish a contrast set, through the contrast set to learn the data required for different optical fiber fusion.

[0203] Those skilled in the art will easily understand that the above description is only the preferred embodiment of the present application, and is not intended to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A self-cleaning portable optical fiber fusion splicing method, characterized by, Comprise the following steps: S100, start the optical fiber fusion device, the built-in environmental sensor starts to monitor and record the current environmental conditions, and automatically adjust the air cleanliness through the environmental sensor data; S200, automatically identify the type of optical fiber to be fused, and perform processing including peeling, cutting and cleaning on the optical fiber to be fused, monitor the optical fiber processing process, and ensure that the cutting surface is flat; S300, start the fusion program, and the optical fiber fusion device performs the fusion task, and during the fusion process, the key indicators including discharge intensity and fusion speed are tracked; S400, after the fusion is completed, the image recognition technology is applied to analyze the quality of the contact point, and it is judged whether it meets the standard; S500, record the data including environmental conditions, used parameters, fusion time and quality evaluation results during each fusion, and predict the wear condition of the component according to the accumulated data; During the whole fusion process, the fusion environment needs to be continuously monitored and controlled according to the steps in step S100, the optical fiber fusion device is a closed device, which is built-in air purifier, temperature and humidity controller, and step S100 specifically comprises: S110, the operator turns on the power switch, the system performs self-checking, and checks whether all hardware and software are normally operated; S120, the built-in environmental sensor starts to monitor and record the current environmental conditions; S130, the air purification device is automatically started to ensure that the air cleanliness meets the requirements, and the operator is prompted that the environment is ready; S140, the temperature and humidity controller adjusts the environmental parameters according to the sensor data to maintain a slight positive pressure state to prevent external pollutants from entering; In step S130, the air cleanliness is regulated by a sliding mode control, wherein the switching function is: ， wherein is the difference between the current cleanliness and the target value, is a constant that determines the slope of the slip surface and the response speed of the system; a difference between the current cleanliness and a target value , in particular: ， wherein target cleanliness, Current cleanliness; Before running, it also includes: S600, collect a large number of optical fiber fusion videos, and the corresponding device parameters, environmental conditions and optical fiber information during fusion, organize them and establish a comparison set, and learn the data required for different optical fiber fusion through the comparison set.

2. The self-cleaning portable optical fiber fusion splicing method according to claim 1, wherein, In step S140, a fuzzy set also needs to be established, the specific values of the environmental parameters are mapped into the fuzzy set, and each specific value of the environmental parameters is corresponded to a temperature label, a humidity label and a pressure label, and then a fuzzy rule base is established, the output variable is adjusted according to the labels in the fuzzy set, so as to control and adjust the environmental parameters; The output in the fuzzy rule base is fuzzy output, which needs to be converted into actual operable values by using defuzzification, specifically: ， wherein, is the output value corresponding to each rule in the fuzzy rule base, is the membership of the output value.

3. The self-cleaning portable optical fiber fusion splicing method according to claim 1 or 2, characterized in that, In step S600, it also specifically comprises: S610, determine the type of data to be collected, including but not limited to fusion video, device parameter, environmental condition and optical fiber information; S620, collect successful and failed cases covering different types of optical fibers, various environmental conditions and different device parameters; S630, preliminarily process the collected raw data, remove irrelevant or redundant information, and add labels to each group of data according to the quality of fusion; S640, pre-process the data in the comparison set to ensure that the data format input to the machine learning algorithm is uniform and effective; S650, establish a model, use the data in the comparison set, and train the model through machine learning; S660, update the comparison set regularly, add the latest fusion cases, and keep the timeliness and adaptability of the model.

4. The self-cleaning portable optical fiber fusion splicing method according to claim 1 or 2, characterized in that, In step S200, the processing of the fiber to be spliced specifically includes: S210, stripping the fiber, the system monitors the stripping process in real time to ensure that the fiber core is not damaged; S220, cutting the fiber end face, the system uses high-precision cutting tools to ensure that the cutting surface is smooth and flawless, while monitoring the cutting angle and quality; S230, cleaning the fiber end face, removing any residue or contamination, ensuring that the fiber surface is clean before splicing; S240, place the processed fiber in the V-shaped groove and fix it with the clamp, ready for the splicing stage.

5. The self-cleaning portable optical fiber fusion splicing method according to claim 1 or 2, characterized in that, In step S300, it specifically includes the following steps: S310, according to the automatically identified type and characteristics of the fiber to be spliced, retrieve the most similar ten groups of data in the reference set as the similarity set; S320, further weight calculation is performed on the data in the similarity set, and the data is scored to select the group of data closest to the fiber to be spliced; S330, retrieve the group of data closest to the fiber to be spliced, and modify the data according to the specific information of the fiber to be spliced as the setting data for the fiber splicing; S340, adjust the parameters of the splicing according to the setting data, and the fiber splicing device discharges according to the adjusted parameters to start the splicing process; S350, track the key data in the splicing process, and automatically adjust the splicing parameters according to the abnormal situation, until the splicing is completed, stop discharging, and release the fiber.

6. The self-cleaning portable optical fiber fusion splicing method according to claim 1 or 2, wherein, In step S400, it specifically includes the following steps: S410, capture high-definition images of the fiber splicing point and perform preprocessing including denoising, contrast enhancement and edge detection; S420, automatically locate the splicing point, extract the key features of the splicing point, and measure the key size parameters of the splicing point; S430, calculate the quality indicators of the splicing point including alignment error, splicing strength estimate and surface smoothness according to the extracted features and size parameters; S440, compare the calculated quality indicators with the preset standard range, and automatically judge whether the splicing quality is qualified according to the comparison result; S450, if not qualified, provide detailed improvement suggestions according to the quality indicators, adjust the splicing parameters, fiber preprocessing method and environmental conditions, and re-splice.

7. A self-cleaning portable optical fiber fusion splicing system for implementing a self-cleaning portable optical fiber fusion splicing method according to any one of claims 1 to 6, characterized in that, It includes: The first cleaning module: used to start the fiber splicing device, the built-in environmental sensor starts to monitor and record the current environmental conditions, and automatically adjusts the air cleanliness through the environmental sensor data; The second cleaning module: used to automatically identify the type of fiber to be spliced, and to process the fiber to be spliced including stripping, cutting and cleaning, to monitor the fiber processing process and ensure that the cutting surface is smooth; The fiber splicing module: used to start the splicing program, the fiber splicer performs the splicing task, and the key indicators including discharge strength and splicing speed are tracked throughout the splicing process; The quality detection module: used to analyze the quality of the joint point after splicing is completed by using image recognition technology to judge whether it meets the standard; The data recording module: used to record the data including environmental conditions, parameters used, splicing time and quality evaluation results during each splicing, and to predict the wear of the components according to the accumulated data; Data collation module: for collecting a large number of optical fiber fusion video, and the corresponding fusion equipment parameters, environmental conditions, optical fiber information, after collation, establish a contrast set, through the contrast set to learn the data required for different optical fiber fusion.

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