Self-cleaning portable optical fiber welding method and system

Abstract

The invention discloses a self-cleaning portable optical fiber welding method and system, and the method comprises the steps: S100, starting an optical fiber welding device, enabling a built-in environment sensor of the optical fiber welding device to start monitoring and recording the current environment condition, and automatically adjusting the air cleanliness; s200, automatically identifying the type of the to-be-welded optical fiber, performing treatment including peeling, cutting and cleaning on the to-be-welded optical fiber, and monitoring the optical fiber treatment process to ensure that the cut surface is flat; s300, starting a fusion splicing program, executing a fusion splicing task by the optical fiber fusion splicer, and tracking key indexes including the discharge intensity and the fusion splicing speed in the whole process in the fusion splicing process; s400, after fusion welding is completed, the quality of the contact is analyzed through the image recognition technology, and whether the contact meets the standard or not is judged; and S500, recording data including environmental conditions, used parameters, welding time and quality evaluation results during each welding, and predicting the wear condition of the component according to the accumulated data. And the high-cleanliness welding requirements of special optical fibers under outdoor and emergency conditions are met.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical fiber fusion splicing, and more specifically, relates to a self-cleaning portable optical fiber fusion splicing method and system. Background Art

[0002] At present, the application fields of high-power lasers are constantly expanding, including biomedicine, sensor detection, information confrontation, laser processing, etc. These fields have put forward higher requirements for fiber fusion splicing technology, especially the needs of fusion splicing outdoors and in emergency situations. The existing portable fiber fusion splicer integrates the fiber fusion splicer with the storage box, and can move the entire fiber fusion splicer and the box by pulling the handle to achieve a portable effect. However, high-precision and high-efficiency fiber fusion splicers generally have complex internal structures and require intelligent operating systems, high-precision optical systems, and mechanical structures. Fiber recognition and contour alignment are achieved through high-precision optical systems to ensure the quality of fusion splicing. The discharge voltage and discharge position are automatically corrected by identifying changes in ambient temperature and air pressure. However, the above fiber fusion splicing equipment cannot meet the high-cleanliness fusion splicing requirements of special optical fibers outdoors and in emergency situations.

[0003] At present, there is a combined optical fiber fusion operation box on the market, including an outer shell, a cavity is arranged in the outer shell, and a stripping mechanism, a second cleaning mechanism, a first cleaning mechanism, a collecting mechanism, a cutting mechanism and a monitoring mechanism are arranged in the cavity; the present invention can automatically fuse the optical fiber, thereby improving the efficiency of optical fiber fusion, and at the same time, the optical fiber jacket can be automatically stripped by the stripping teeth, avoiding the need for manual stripping of the optical fiber, improving the fusion efficiency and reducing the work intensity of the workers, and the bare fiber is automatically cleaned by the arc-shaped cotton strip to remove the coating layer on the surface of the bare fiber that is difficult to remove, and the optical fiber can be cut automatically or manually by the motor and the cutting knife, and the operator can choose different cutting methods according to different environments. Finally, the scraper can automatically clean the inside of the fusion machine before fusing the bare fiber.

[0004] The fiber fusion operation box can automatically clean the bare fiber and remove the coating layer on the bare fiber surface that is difficult to remove, thus preventing the probe from being contaminated twice. However, if only the fiber itself is cleaned without cleaning the fiber fusion environment, it will be affected by the external environment. When there are foreign objects or the air cleanliness is not high, it will cause excessive fusion loss, fiber light overflow and heat accumulation, which will not only affect the optical performance of the system, but may also cause safety accidents and economic losses. Therefore, a self-cleaning portable fiber fusion method and system automatically monitors the environmental cleanliness during the fiber fusion process, so that it can meet the high-cleanliness fusion requirements of special optical fibers outdoors and in emergency situations. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a self-cleaning portable optical fiber fusion splicing method and system, which monitors and records the current environmental conditions through built-in environmental sensors, automatically adjusts the air cleanliness through sensor data, and automatically monitors the environmental cleanliness, so as to meet the high cleanliness fusion splicing requirements of special optical fibers outdoors and in emergency situations. By automatically identifying the type of optical fiber to be fused and retrieving the corresponding best matching data, accurate parameter setting is achieved, human errors and debugging time are reduced, which not only improves the success rate and efficiency of fusion splicing, but also ensures that different types of optical fibers can be optimally processed, significantly improving the overall production quality.

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

[0007] S100, start the optical fiber fusion splicing device, and its built-in environmental sensor starts to monitor and record the current environmental conditions, and automatically adjusts the air cleanliness through the sensor data;

[0008] S200, automatically identifies the type of optical fiber to be spliced, performs processing including peeling, cutting and cleaning on the optical fiber to be spliced, monitors the optical fiber processing process, and ensures that the cut surface is flat;

[0009] S300, start the fusion splicing procedure, the optical fiber fusion splicer performs the fusion splicing task, and during the fusion splicing process, the key indicators including the discharge intensity and the fusion splicing speed are tracked throughout the process;

[0010] S400, after the welding is completed, the quality of the joint is analyzed by image recognition technology to determine whether it meets the standards;

[0011] S500, record data including environmental conditions, parameters used, welding time and quality assessment results during each welding, and predict the wear of the components based on the accumulated data.

[0012] Furthermore, during the entire fusion splicing process, the fusion splicing environment needs to be continuously monitored and controlled according to the steps in step S100. The optical fiber fusion splicing device is a closed device with a built-in air purifier and a temperature and humidity controller. Step S100 specifically includes:

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

[0014] S120, the built-in environmental sensor starts to monitor and record the current environmental conditions;

[0015] S130, the air purification device automatically turns on to ensure that the air cleanliness meets the requirements and prompts the operator that the environment preparation is complete;

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

[0017] Furthermore, in step S130, the air cleanliness is adjusted by sliding mode control, wherein the switching function s(t) is:

[0018]

[0019] Where e(t) is the difference between the current cleanliness and the target value,

[0020] λ is a constant that determines the slope of the sliding surface and the response speed of the system;

[0021] The difference e(t) between the current cleanliness and the target value is specifically:

[0022] e(t)=x ref (txt),

[0023] Among them, x ref (t) is the target cleanliness,

[0024] x(t) is the current cleanliness.

[0025] Furthermore, in step S140, a fuzzy set needs to be established to map specific values ​​of environmental parameters into the fuzzy set, and to make the specific values ​​of each environmental parameter correspond to the temperature label, humidity label and pressure label, and then a fuzzy rule base is established to adjust the output variables according to the labels in the fuzzy set to control and adjust the environmental parameters;

[0026] The output in the fuzzy rule base is fuzzy output, and defuzzification is required to convert the fuzzy output into actual operable values, specifically:

[0027]

[0028] Among them, y i is the output value corresponding to each rule in the fuzzy rule base,

[0029] μ(y i ) is the membership degree of the output value.

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

[0031] S600, collect a large number of videos of fiber optic fusion, as well as the corresponding equipment parameters, environmental adjustments, and fiber information during fusion, organize them and establish a reference set, and learn the data required for different fiber optic fusion through the reference set.

[0032] Furthermore, step S600 specifically includes:

[0033] S610, determining the type of data to be collected, including but not limited to fusion video, equipment parameter environment parameters and optical fiber information;

[0034] S620. Collect success and failure cases covering different types of optical fibers, various environmental conditions, and using different equipment parameters;

[0035] S630, preliminarily processing the collected raw data, removing irrelevant or redundant information, and adding a label to each set of data according to the quality of the welding;

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

[0037] S650, establish a model, and use the data in the control set to train the model through machine learning;

[0038] S660. Regularly update the reference set and add the latest welding 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, peeling the optical fiber, and the system monitors the peeling process in real time to ensure that the optical fiber core is not damaged;

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

[0042] S230, clean the fiber end face, remove any residue or contamination, and ensure that the fiber surface is clean before fusion splicing;

[0043] S240, place the processed optical fiber in the V-groove and fix it with a clamp, ready to enter the fusion splicing stage.

[0044] Furthermore, step S300 specifically includes the following steps:

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

[0046] S320, further weight calculation is performed on the data in the similarity set, and scoring is performed on the data to select a set of data closest to the optical fiber to be fused;

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

[0048] S340, adjusting the fusion parameters according to the setting data, and the optical fiber fusion splicing device discharges according to the adjusted parameters to start the fusion splicing process;

[0049] S350, tracks the key data in the whole welding process, and automatically adjusts the welding parameters according to abnormal conditions. After the welding is completed, it stops discharging and releases the optical fiber.

[0050] Furthermore, step S400 specifically includes the following steps:

[0051] S410, capturing high-definition images of optical fiber splicing points and performing pre-processing including denoising, contrast enhancement and edge detection;

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

[0053] S430, calculating quality indicators of the welding point including alignment error, welding strength estimation value and surface smoothness according to the extracted features and size parameters;

[0054] S440, comparing the calculated quality index with a preset standard range, and automatically determining whether the welding quality is qualified according to the comparison result;

[0055] S450: If unqualified, provide detailed improvement suggestions based on quality indicators, adjust welding parameters, fiber pretreatment methods and environmental conditions, and re-weld.

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

[0057] The first cleaning module is used to start the optical fiber fusion splicing device. Its built-in environmental sensor starts to monitor and record the current environmental conditions and automatically adjusts the air cleanliness based on the sensor data.

[0058] The second cleaning module is used to automatically identify the type of optical fiber to be spliced, perform treatments including peeling, cutting and cleaning on the optical fiber to be spliced, monitor the optical fiber processing process, and ensure that the cut surface is flat;

[0059] Fiber fusion splicing module: used to start the fusion splicing program. The fiber fusion splicer performs the fusion splicing task. During the fusion splicing process, key indicators including discharge intensity and fusion splicing speed are tracked throughout the process.

[0060] Quality inspection module: After the welding is completed, the image recognition technology is used to analyze the quality of the joints to determine whether they meet the standards;

[0061] Data recording module: used to record data including environmental conditions, parameters used, welding time and quality assessment results during each welding process, and predict the wear of components based on the accumulated data;

[0062] Data collation module: It is used to collect a large number of videos of fiber optic splicing, as well as the equipment parameters, environmental adjustments, and fiber information during the corresponding splicing, and then organize them to create a reference set, through which the data required for different fiber optic splicing can be learned.

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

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

[0065] 2. The optical fiber fusion splicing method of the present invention realizes accurate parameter setting by automatically identifying the type of optical fiber to be fused and retrieving the corresponding optimal matching data, thereby reducing human errors and debugging time. It not only improves the success rate and efficiency of fusion splicing, 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 splicing method of the present invention realizes scientific optimization of fusion splicing parameters by selecting the most similar data from the reference set as a reference, ensures the consistency and reliability of each fusion splicing process, adapts to the needs of various optical fiber types, reduces the cost of trial and error, and greatly improves the flexibility and accuracy of the fusion splicing operation.

[0067] 4. The optical fiber fusion splicing method of the present invention realizes the stability and high-quality output of the fusion splicing process by real-time monitoring of key data in the fusion splicing process and automatically adjusting the fusion splicing parameters according to abnormal situations. It can respond to environmental changes and equipment fluctuations in a timely manner, ensure that each fusion splicing achieves the best effect, and reduce the scrap rate.

[0068] 5. The optical fiber fusion splicing method of the present invention realizes the refined management of the fusion splicing process by tracking the key parameters such as temperature, humidity, current, etc. during the fusion splicing process and introducing a feedback control system. It not only ensures the high precision and stability of the fusion splicing process, but also enhances the adaptive ability of the system, thereby greatly improving product quality and production efficiency.

[0069] 6. The optical fiber fusion splicing method of the present invention realizes preventive maintenance and extension of equipment life by recording key data of each fusion splicing and predicting the wear of components based on the accumulated data, plans maintenance and replacement plans in advance, reduces unexpected downtime, significantly reduces operating costs, and improves equipment reliability and availability.

[0070] 7. The optical fiber fusion splicing method of the present invention realizes the continuous improvement of processes and technologies by systematically recording and analyzing the data of each fusion splicing, supports long-term data analysis and model optimization, promotes the advancement of fusion splicing technology, ensures the continuous upgrading of production processes, and lays a solid foundation for future innovation and development. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0072] Figure 2 A schematic diagram of 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 DESCRIPTION

[0078] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present 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, an embodiment of the present invention provides a self-cleaning portable optical fiber fusion splicing method, comprising the following steps:

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

[0082] S200, automatically identifies the type of optical fiber to be spliced, performs processing including peeling, cutting and cleaning on the optical fiber to be spliced, monitors the optical fiber processing process, and ensures that the cut surface is flat;

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

[0084] S400, after the welding is completed, the quality of the joint is analyzed by image recognition technology to determine whether it meets the standards;

[0085] S500, record data including environmental conditions, parameters used, welding time and quality assessment results during each welding, and predict the wear of the components based on the accumulated data.

[0086] like Figure 3 As shown, 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 with a built-in 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 a self-check to check whether all hardware and software are operating normally;

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

[0089] S130, the air purification device automatically turns on to ensure that the air cleanliness meets the requirements and prompts the operator that the environment preparation is complete;

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

[0091] Step S 130 In the example, air cleanliness is adjusted by sliding mode control, where the switching function s(t) is:

[0092]

[0093] Where e(t) is the difference between the current cleanliness and the target value,

[0094] λ is a constant that determines the slope of the sliding surface and the response speed of the system.

[0095] The difference e(t) between the current cleanliness and the target value is specifically:

[0096] e(t)=x ref (txt),

[0097] Among them, x ref (t) is the target cleanliness,

[0098] x(t) is the current cleanliness.

[0099] In order to make the control state of the switching function s(t) reach and maintain on the sliding surface, a control law u(t) is required, which is:

[0100] u(t)=u eq (t)+u dis (t),

[0101] Among them, u eq (t) is the equivalent control, which is used to ensure that the system behaves as expected on the sliding surface,

[0102] u dis (t) is a discontinuous term, which is used to overcome uncertainty and disturbance and force the system into sliding mode.

[0103] The equivalent control u eq (t) is:

[0104]

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

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

[0107] The discontinuous term u dis (t) is:

[0108] u dis (t) = -ksign(s(t)),

[0109] Where k is the gain coefficient,

[0110] sign(s(t)) is the sign function, which is used to provide sufficient counter force to pull the system back to the sliding surface.

[0111] In step S140, it is also necessary to establish a fuzzy set, map the specific values ​​of the environmental parameters to the fuzzy set, and make the specific values ​​of each environmental parameter correspond to the temperature label, humidity label and pressure label, and then establish a fuzzy rule base to adjust the output variables according to the labels in the fuzzy set to control and adjust the environmental parameters. The output in the fuzzy rule base is a fuzzy output, and defuzzification is required to convert the fuzzy output into an actual operable value, specifically:

[0112]

[0113] Among them, yi is the output value corresponding to each rule in the fuzzy rule base,

[0114] μ(y i ) is the membership degree of the output value.

[0115] The optical fiber fusion splicing method provided in the embodiment of the present invention further includes, before operation:

[0116] S600, collect a large number of videos of fiber optic fusion, as well as the corresponding equipment parameters, environmental adjustments, and fiber information during fusion, organize them and establish a reference set, and learn the data required for different fiber optic fusion through the reference set.

[0117] like Figure 4 As shown, step S600 specifically includes:

[0118] S610, determining the type of data to be collected, including but not limited to fusion video, equipment parameter environment parameters and optical fiber information;

[0119] S620. Collect success and failure cases covering different types of optical fibers, various environmental conditions, and using different equipment parameters;

[0120] S630, preliminarily processing the collected raw data, removing irrelevant or redundant information, and adding a label to each set of data according to the quality of the welding;

[0121] S640, preprocessing the data in the control set to ensure that the data format input to the machine learning algorithm is uniform and valid;

[0122] S650, establish a model, and use the data in the control set to train the model through machine learning;

[0123] S660. Regularly update the reference set and add the latest welding cases to maintain the timeliness and adaptability of the model.

[0124] In step S630, specifically: identify and remove outliers that may affect model performance, select and delete records containing missing values ​​to interpolate them, select the most influential features through feature selection technology, use dimensionality reduction technology to reduce dimensions while maintaining important information, and add labels to each set of data based on the results of the welding quality. In order to ensure that different features are on the same scale, the data needs to be standardized or normalized.

[0125] In step S640, when preprocessing the data in the control set, for the multi-dimensional data therein, the Mahalanobis distance is used to measure whether a point is an outlier, and the outlier is removed, specifically:

[0126]

[0127] Among them, x i is the i-th sample,

[0128] μ is the mean vector,

[0129] S is the covariance matrix,

[0130] is the judgment value, if If the value is greater than the critical value, the sample is considered to be an outlier. Figure 5 As shown, in step S200, the processing of the optical fiber to be fused specifically includes:

[0131] S210, peeling the optical fiber, and the system monitors the peeling process in real time to ensure that the optical fiber core is not damaged;

[0132] S220, cutting the fiber end face, the system uses high-precision cutting tools to ensure the cut surface is flat and flawless, while monitoring the cutting angle and quality;

[0133] S230, clean the fiber end face, remove any residue or contamination, and ensure that the fiber surface is clean before fusion splicing;

[0134] S240, place the processed optical fiber in the V-groove and fix it with a clamp, ready to enter the fusion splicing stage.

[0135] like Figure 6 As shown, step S300 specifically includes the following steps:

[0136] S310, according to the automatically identified types and characteristics of the optical fibers to be fused, retrieve the ten most similar data sets in the reference set as similarity sets;

[0137] S320, further weight calculation is performed on the data in the similarity set, and scoring is performed on the data to select a set of data closest to the optical fiber to be fused;

[0138] S330, retrieving a set of data closest to the optical fiber to be fused, and modifying the data according to the specific information of the optical fiber to be fused, so as to serve as setting data for the optical fiber to be fused;

[0139] S340, adjusting the fusion parameters according to the setting data, and the optical fiber fusion splicing device discharges according to the adjusted parameters to start the fusion splicing process;

[0140] S350, tracks the key data in the whole welding process, and automatically adjusts the welding parameters according to abnormal conditions. After the welding is completed, it stops discharging and releases the optical fiber.

[0141] In step S310, the Euclidean distance is used to measure the similarity between the data in the reference set and the characteristics of the optical fiber to be fused, specifically:

[0142]

[0143] Among them, F t is the characteristic vector of the optical fiber to be spliced,

[0144] F i is the optical fiber feature vector of the i-th group of data in the control set,

[0145] f t，j 、f i，j The eigenvectors F t 、F i The jth element in

[0146] m is the number of features;

[0147] D(F t , F i ) value, the smaller the similarity.

[0148] In step S320, the scoring method is weighted scoring, specifically:

[0149]

[0150] Among them, ω j is the weight of the jth element,

[0151] According to S i Select the data set with the highest score.

[0152] In step S330, specifically: retrieve a set of data X closest to the optical fiber to be fused best , according to the specific information of the optical fiber to be fused, the corrected data X set for:

[0153] X set =X best +ΔX,

[0154] Among them, ΔX is the correction increment, specifically:

[0155] ΔX=α(F t -F best ),

[0156] Among them, α is a scaling factor used to adjust the correction strength,

[0157] F best It is the feature vector of a set of data closest to the optical fiber to be fused, selected from the reference set.

[0158] In step S350, when an abnormality is detected, the particle swarm optimization algorithm is used to dynamically adjust the welding parameters to restore the normal welding process, specifically including:

[0159] a. Define the position and velocity vector of the particles in the optical fiber to be spliced:

[0160] x i (0)~u(x min , x max )

[0161] v i (0)~u(v min , v max ),

[0162] Among them, u represents uniform distribution,

[0163] (x min , x max ) indicates the position range,

[0164] (v min , v max ) indicates the speed range;

[0165] b. Each particle has its own optimal position p i And the global optimal position g updates its position and velocity:

[0166] v i (t+1)=w·v i (t)+c1·r1·(p i -x i (t))+c2·r2·(gx i (t)),

[0167] x i (t+1)=x i (t)+v i (t+1),

[0168] Where w is the inertia weight,

[0169] c1 and c2 are acceleration coefficients,

[0170] r1 and r2 are random numbers.

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

[0172] Y pred (t+τ)=LSTM(Y(t), Y(t-1),..., Y(t-Δt)),

[0173] Among them, Y pred (t+τ) is the predicted value after τ time steps in the future,

[0174] Y(t) is the key data at the current moment,

[0175] Δt is the time window size;

[0176] d. Adjust the welding parameters according to the prediction results to ensure that the welding process is always in the best state, and integrate the predicted values ​​and actual measured values, specifically:

[0177] R est (t) = R pred (t)+K(t)[Y(t)-HR pred (t)],

[0178] Among them, R est (t) estimated state vector,

[0179] R pred (t) is the predicted state vector,

[0180] K(t) Kalman gain,

[0181] Y(t) is the measured value,

[0182] H is the observation matrix.

[0183] like Figure 7 As shown, step S400 specifically includes the following steps:

[0184] S410, capturing high-definition images of optical fiber splicing points and performing pre-processing including denoising, contrast enhancement and edge detection;

[0185] S420, automatically locate the welding point, extract the key features of the welding point, and measure the key size parameters of the welding point;

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

[0187] S440, comparing the calculated quality index with a preset standard range, and automatically determining whether the welding quality is qualified according to the comparison result;

[0188] S450: If unqualified, provide detailed improvement suggestions based on quality indicators, adjust welding parameters, fiber pretreatment methods and environmental conditions, and re-weld.

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

[0190]

[0191] Among them, D i is the damage caused by the i-th welding to the component, calculated according to the welding parameters and environmental conditions, and optimized by minimizing the error function:

[0192]

[0193] It can not only record the key data of each welding, but also predict the wear of components based on the accumulated data to ensure that the equipment is always in the best working condition. At the same time, the introduction of complex control formulas and real-time monitoring mechanisms improves the reliability and maintenance efficiency of the system.

[0194] The data recorded in step S500 is synchronized to the comparison set to improve the data and increase the accuracy during welding.

[0195] Example 2

[0196] The embodiment of the present invention provides a self-cleaning portable optical fiber fusion splicing method, comprising the following steps:

[0197] The first cleaning module is used to start the optical fiber fusion splicing device. Its built-in environmental sensor starts to monitor and record the current environmental conditions and automatically adjusts the air cleanliness based on the sensor data.

[0198] The second cleaning module is used to automatically identify the type of optical fiber to be spliced, perform treatments including peeling, cutting and cleaning on the optical fiber to be spliced, monitor the optical fiber processing process, and ensure that the cut surface is flat;

[0199] Fiber fusion splicing module: used to start the fusion splicing program. The fiber fusion splicer performs the fusion splicing task. During the fusion splicing process, key indicators including discharge intensity and fusion splicing speed are tracked throughout the process.

[0200] Quality inspection module: After the welding is completed, the image recognition technology is used to analyze the quality of the joints to determine whether they meet the standards;

[0201] Data recording module: used to record data including environmental conditions, parameters used, welding time and quality assessment results during each welding process, and predict the wear of components based on the accumulated data;

[0202] Data collation module: It is used to collect a large number of videos of fiber optic splicing, as well as the equipment parameters, environmental adjustments, and fiber information during the corresponding splicing, and then organize them to create a reference set, through which the data required for different fiber optic splicing can be learned.

[0203] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-cleaning portable optical fiber fusion splicing method, characterized in that: The following steps are involved: S100, start the optical fiber fusion splicing device, and its built-in environmental sensor starts to monitor and record the current environmental conditions, and automatically adjusts the air cleanliness through the sensor data; S200, automatically identifies the type of optical fiber to be spliced, performs processing including peeling, cutting and cleaning on the optical fiber to be spliced, monitors the optical fiber processing process, and ensures that the cut surface is flat; S300, start the fusion splicing procedure, the optical fiber fusion splicer performs the fusion splicing task, and during the fusion splicing process, the key indicators including the discharge intensity and the fusion splicing speed are tracked throughout the process; S400, after the welding is completed, the quality of the joint is analyzed by image recognition technology to determine whether it meets the standards; S500, record data including environmental conditions, parameters used, welding time and quality assessment results during each welding, and predict the wear of the components based on the accumulated data.

2. A self-cleaning portable optical fiber fusion splicing method according to claim 1, characterized in that: During the entire fusion splicing process, the fusion splicing environment needs to be continuously monitored and controlled according to the steps in step S100. The optical fiber fusion splicing device is a closed device with a built-in air purifier and a temperature and humidity controller. Step S100 specifically includes: S110: The operator turns on the power switch, and the system performs a self-check to check whether all hardware and software are operating normally; S120, the built-in environmental sensor starts to monitor and record the current environmental conditions; S130, the air purification device automatically turns on to ensure that the air cleanliness meets the requirements and prompts the operator that the environment preparation is complete; S140, the temperature and humidity controller adjusts the environmental parameters according to the sensor data and maintains a slightly positive pressure state to prevent external pollutants from entering.

3. A self-cleaning portable optical fiber fusion splicing method according to claim 2, characterized in that: In step S130, the air cleanliness is adjusted by sliding mode control, wherein the switching function s(t) is: Where e(t) is the difference between the current cleanliness and the target value, λ is a constant that determines the slope of the sliding surface and the response speed of the system; The difference e(t) between the current cleanliness and the target value is specifically: e(t)=x ref (t)-x(t), Among them, x ref (t) is the target cleanliness, x(t) is the current cleanliness.

4. A self-cleaning portable optical fiber fusion splicing method according to claim 2, characterized in that: In step S140, a fuzzy set needs to be established to map the specific values ​​of the environmental parameters into the fuzzy set, and the specific values ​​of each environmental parameter are made to correspond to the temperature label, the humidity label and the pressure label, and then a fuzzy rule base is established to adjust the output variables according to the labels in the fuzzy set to control and adjust the environmental parameters; The output in the fuzzy rule base is fuzzy output, and defuzzification is required to convert the fuzzy output into actual operable values, specifically: Among them, y i is the output value corresponding to each rule in the fuzzy rule base, μ(y i ) is the membership degree of the output value.

5. A self-cleaning portable optical fiber fusion splicing method according to any one of claims 1 to 4, characterized in that: Before running, it also includes: S600, collect a large number of videos of fiber optic fusion, as well as the corresponding equipment parameters, environmental adjustments, and fiber information during fusion, organize them and establish a reference set, and learn the data required for different fiber optic fusion through the reference set.

6. A self-cleaning portable optical fiber fusion splicing method according to claim 5, characterized in that: Step S600 specifically also includes: S610, determining the type of data to be collected, including but not limited to fusion video, equipment parameter environment parameters and optical fiber information; S620. Collect success and failure cases covering different types of optical fibers, various environmental conditions, and using different equipment parameters; S630, preliminarily processing the collected raw data, removing irrelevant or redundant information, and adding a label to each set of data according to the quality of the welding; S640, preprocessing the data in the control set to ensure that the data format input to the machine learning algorithm is uniform and valid; S650, establish a model, and use the data in the control set to train the model through machine learning; S660. Regularly update the reference set and add the latest welding cases to maintain the timeliness and adaptability of the model.

7. A self-cleaning portable optical fiber fusion splicing method according to any one of claims 1 to 4, characterized in that: In step S200, the processing of the optical fiber to be fused specifically includes: S210, peeling the optical fiber, and the system monitors the peeling process in real time to ensure that the optical fiber core is not damaged; S220, cutting the fiber end face, the system uses high-precision cutting tools to ensure the cut surface is flat and flawless, while monitoring the cutting angle and quality; S230, clean the fiber end face, remove any residue or contamination, and ensure that the fiber surface is clean before fusion splicing; S240, place the processed optical fiber in the V-groove and fix it with a clamp, ready to enter the fusion splicing stage.

8. A self-cleaning portable optical fiber fusion splicing method according to any one of claims 1 to 4, characterized in that: Step S300 specifically includes the following steps: S310, according to the automatically identified types and characteristics of the optical fibers to be fused, retrieve the ten most similar data sets in the reference set as similarity sets; S320, further weight calculation is performed on the data in the similarity set, and scoring is performed on the data to select a set of data closest to the optical fiber to be fused; S330, retrieving a set of data closest to the optical fiber to be fused, and modifying the data according to the specific information of the optical fiber to be fused, so as to serve as setting data for the optical fiber to be fused; S340, adjusting the fusion parameters according to the setting data, and the optical fiber fusion splicing device discharges according to the adjusted parameters to start the fusion splicing process; S350, tracks the key data in the whole welding process, and automatically adjusts the welding parameters according to abnormal conditions. After the welding is completed, it stops discharging and releases the optical fiber.

9. A self-cleaning portable optical fiber fusion splicing method according to any one of claims 1 to 4, characterized in that: Step S400 specifically includes the following steps: S410, capturing high-definition images of optical fiber splicing points and performing pre-processing including denoising, contrast enhancement and edge detection; S420, automatically locate the welding point, extract the key features of the welding point, and measure the key size parameters of the welding point; S430, calculating quality indicators of the welding point including alignment error, welding strength estimation value and surface smoothness according to the extracted features and size parameters; S440, comparing the calculated quality index with a preset standard range, and automatically determining whether the welding quality is qualified according to the comparison result; S450: If unqualified, provide detailed improvement suggestions based on quality indicators, adjust welding parameters, fiber pretreatment methods and environmental conditions, and re-weld.

10. A self-cleaning portable optical fiber fusion splicing method for realizing a self-cleaning portable optical fiber fusion splicing method as claimed in any one of claims 1 to 9, characterized in that: include: The first cleaning module is used to start the optical fiber fusion splicing device. Its built-in environmental sensor starts to monitor and record the current environmental conditions and automatically adjusts the air cleanliness based on the sensor data. The second cleaning module is used to automatically identify the type of optical fiber to be spliced, perform treatments including peeling, cutting and cleaning on the optical fiber to be spliced, monitor the optical fiber processing process, and ensure that the cut surface is flat; Fiber fusion splicing module: used to start the fusion splicing program. The fiber fusion splicer performs the fusion splicing task. During the fusion splicing process, key indicators including discharge intensity and fusion splicing speed are tracked throughout the process. Quality inspection module: After the welding is completed, the image recognition technology is used to analyze the quality of the joints to determine whether they meet the standards; Data recording module: used to record data including environmental conditions, parameters used, welding time and quality assessment results during each welding process, and predict the wear of components based on the accumulated data; Data collation module: It is used to collect a large number of videos of fiber optic splicing, as well as the equipment parameters, environmental adjustments, and fiber information during the corresponding splicing, and then organize them to create a reference set, through which the data required for different fiber optic splicing can be learned.

Images