Optical fiber fusion control method and system

Abstract

The embodiments of the present specification provide a method and system for controlling fiber fusion splicing, which is implemented based on a fiber fusion splicing device, and includes: obtaining fiber parameters of the optical fiber to be fused; obtaining environmental parameters, and predicting the degree of environmental influence of the environmental parameters on the fiber fusion splicing, wherein the environmental parameters include at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time; determining the working parameters of the automatic cleaning component based on the environmental influence degree and the optical fiber parameters; determining the optical fiber advancement parameters based on the automatic cleaning results and historical fusion splicing abnormal data, and fusing the optical fiber to be fused according to the optical fiber advancement parameters.

Description

Technical Field

[0001] This specification relates to the field of optical fiber fusion splicing, and in particular to an optical fiber fusion splicing control method and system. Background Art

[0002] Fiber fusion splicing involves aligning the ends of two untouched fibers, then using a pulsed discharge to melt the ends and simultaneously butt-join the two fibers, fusing them together to form a single fiber. Improper splicing (e.g., due to dust or misalignment) can lead to problems such as bubbles, cracks in the fibers, and excessive splice thickness. Existing techniques often rely on manual cleaning of the fibers and splicing equipment, which can introduce dust from the air and reduce splice reliability.

[0003] Furthermore, fiber splicing results are significantly affected by the fiber advancement rate and the environment. Advancing too quickly results in a thicker fiber at the splice; advancing too slowly results in a thinner fiber. Furthermore, factors such as ambient air temperature and pressure affect the pulse discharge intensity, which in turn impacts the splicing process.

[0004] In view of this, it is hoped to provide a fiber fusion splicing control method and system to intelligently control the fiber fusion splicing process and improve the reliability of fiber fusion splicing. Summary of the Invention

[0005] One or more embodiments of the present specification provide a method for controlling optical fiber splicing, which is implemented based on an optical fiber splicing device and includes: obtaining optical fiber parameters of an optical fiber to be spliced; obtaining environmental parameters and predicting the degree of environmental impact of the environmental parameters on optical fiber splicing, wherein the environmental parameters include at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time; determining working parameters of an automatic cleaning component based on the environmental impact degree and the optical fiber parameters; determining optical fiber advancement parameters based on automatic cleaning results and historical splicing abnormality data, and splicing the optical fiber to be spliced ​​according to the optical fiber advancement parameters.

[0006] One or more embodiments of the present specification provide a fiber fusion splicing control device, comprising a processor, a fusion splicing device and an automatic cleaning component communicatively connected to the processor; wherein the fusion splicing device is used to fusion splice optical fibers to be fused; the fusion splicing device comprises a fusion splicing slot and a camera; the fusion splicing slot is used to place the optical fibers to be fused for fusion splicing; the camera is used to obtain alignment inspection results and fusion inspection results of the optical fibers to be fused; the automatic cleaning component is used to clean the fusion splicing device, and the automatic cleaning component at least comprises a fusion splicing slot cleaning subcomponent, and the fusion splicing slot cleaning subcomponent comprises at least one vent pipe, a vent valve, and a compressed gas tank; the processor is used at least to: obtain optical fiber parameters of the optical fibers to be fused; obtain environmental parameters and predict the degree of environmental impact of the environmental parameters on optical fiber fusion splicing, the environmental parameters comprising at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time; determine operating parameters of the automatic cleaning component based on the environmental impact degree and the optical fiber parameters; determine optical fiber advancement parameters based on the automatic cleaning results and historical fusion splicing abnormality data, and fusion splice the optical fibers to be fused according to the optical fiber advancement parameters.

[0007] One or more embodiments of the present specification provide a fiber fusion splicing control system, the system comprising: an acquisition module for acquiring fiber parameters of the optical fiber to be fused; a processing module for acquiring environmental parameters and predicting the degree of environmental impact of the environmental parameters on the fiber fusion splicing, the environmental parameters comprising at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time; a determination module for determining the working parameters of an automatic cleaning component based on the degree of environmental impact and the optical fiber parameters; a fusion splicing module for determining optical fiber advancement parameters based on automatic cleaning results and historical fusion splicing abnormality data, and fusion splicing the optical fiber to be fused according to the optical fiber advancement parameters.

[0008] One or more embodiments of this specification provide a computer-readable storage medium, characterized in that the storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the optical fiber fusion splicing control method described in any one of the above embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0010] Figure 1 is an exemplary structural diagram of an optical fiber fusion splicing control device according to some embodiments of this specification;

[0011] Figure 2is an exemplary flow chart of a method for controlling optical fiber fusion splicing according to some embodiments of this specification;

[0012] Figure 3 is an exemplary schematic diagram of adjusting the optical fiber advancement rate according to some embodiments of this specification. Implementation Method

[0013] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0014] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0015] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0016] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0017] During the fiber fusion splicing process, improper splicing (e.g., due to dust or misalignment) can lead to problems such as bubbles and cracks in the fiber, as well as excessive splice thickness. Existing techniques often rely on manual cleaning of the fiber and splicing equipment, which can introduce airborne dust and reduce splice reliability. Furthermore, fiber splicing results are significantly affected by the fiber advancement rate and the environment. Advancing too quickly results in a thicker fiber at the splice, while advancing too slowly results in a thinner fiber at the splice. Furthermore, factors such as ambient air temperature and pressure can affect the pulse discharge intensity, which in turn impacts the splicing process.

[0018] Some embodiments of the present specification determine the parameters of the automatic cleaning component through the degree of environmental impact and fiber parameters during fiber fusion splicing, and determine the fiber advancement parameters based on the automatic cleaning results and historical fusion abnormality data, which helps to fuse the optical fibers to be fused according to the fiber advancement parameters, and can realize automatic cleaning of the fusion splicing device and intelligent control of the fiber fusion splicing process.

[0019] Figure 1 It is a structural schematic diagram of an optical fiber fusion splicing control device according to some embodiments of this specification.

[0020] like Figure 1 As shown, the optical fiber fusion splicing control device 100 may include a processor 110 , a fusion splicing device 120 and an automatic cleaning component 130 .

[0021] The processor 110 refers to a device or apparatus for processing data / information related to the optical fiber fusion splicing control apparatus 100. In some embodiments, the processor 110 may process data, information, and / or processing results obtained from other devices or system components, and generate and / or execute program instructions based on the data, information, and / or processing results to perform one or more functions described in this specification.

[0022] In some embodiments, the processor 110 can be in communication with the fusion splicing device 120 and the automatic cleaning component 130. For example, the processor 110 can determine the fiber advancement parameters based on the automatic cleaning results collected by the camera 122 in the fusion splicing device 120 and combined with historical fusion abnormality data. For another example, the processor 110 can generate instructions based on the fiber advancement parameters and send them to the fusion splicing device 120 to control the fusion splicing device 120 to perform fusion splicing. In some embodiments, the processor 110 can include one or more sub-processing devices (for example, a single-core processing device or a multi-core multi-core processing device). For more information about the processor 110, please refer to Figure 2 、 Figure 3 Related description.

[0023] The fusion splicing device 120 is a device for splicing optical fibers to be fused, such as a fusion splicer. In some embodiments, the fusion splicing device 120 includes at least a fusion slot 121 and a camera 122.

[0024] The fusion groove 121 is a groove for placing the optical fibers to be fused during fusion. The fusion groove 121 can be of various shapes, such as a V-shaped groove.

[0025] The camera 122 is a device or apparatus for observing optical fiber splicing. In some embodiments, the camera 122 is used to obtain alignment inspection results and splicing inspection results of the optical fibers to be spliced. For more information on alignment inspection results and splicing inspection results, please refer to Figure 2 and its related descriptions.

[0026] The automatic cleaning component 130 is a component for cleaning the welding device 120. In some embodiments, the automatic cleaning component 130 can automatically clean the welding device 120 based on power drive and control instructions.

[0027] In some embodiments, the automatic cleaning component 130 includes at least a welding groove cleaning subcomponent 131 .

[0028] The welding tank cleaning subassembly 131 is a component for cleaning the welding tank 121. In some embodiments, the welding tank cleaning subassembly 131 may include at least one vent pipe 131-1, a vent valve 131-2, and a compressed gas tank 131-3.

[0029] Vent pipe 131-1 is a pipe connecting welding tank 121 and compressed gas tank 131-3. Vent pipe 131-1 is used to transport gas from compressed gas tank 131-3 to welding tank 121. Vent valve 131-2 is a device used to open and close the pipe passage or regulate the gas pressure within the pipe. For example, vent valve 131-2 can include a solenoid valve, a pressure reducing valve, etc. Compressed gas tank 131-3 is a pressure tank used to store compressed clean gas (e.g., nitrogen, carbon dioxide).

[0030] In some embodiments, the processor 110 can control the opening and closing of the vent valve 131-2. In response to the opening of the vent valve 131-2, the compressed gas tank 131-3 can pass clean gas into the welding tank 121 through at least one vent pipe 131-1 to purge glass fiber debris, dust and other impurities in the welding tank 121.

[0031] In some embodiments, the automatic cleaning component 130 further includes a suction device ( Figure 1 (not shown). In some embodiments, a suction device may be connected to the fusion tank 121 via a vent pipe 131-1 to assist in extracting gas from the fusion tank 121, thereby controlling the air quality of the fusion tank and thereby controlling the quality of optical fiber fusion.

[0032] In some embodiments, the automatic cleaning component 130 may further include a lens cleaning subcomponent ( Figure 1 (not shown). For example, the lens cleaning sub-component may be an automatic cleaning brush, etc. The automatic cleaning brush may be used to clean the camera 122.

[0033] In some embodiments, the optical fiber fusion splicing control device 100 may further include an air particle concentration monitoring component ( Figure 1(not shown). For example, the air particle concentration monitoring component may include a suspended particle counter, etc. The air particle concentration monitoring component may be used to monitor the air particle concentration in the ventilation pipe 131-1. For more information on air particle concentration, see Figure 3 .

[0034] It should be noted that the above description of the optical fiber fusion splicing control device 100 and its various components is for illustrative purposes only and does not limit this specification to the illustrated embodiments. It is understood that those skilled in the art, after understanding the principles of this device, may freely combine the various components or construct sub-devices that can be connected to other components without departing from these principles.

[0035] Figure 2 FIG2 is an exemplary flow chart of a method for controlling optical fiber fusion splicing according to some embodiments of the present specification. In some embodiments, process 200 may be executed by a processor. Figure 2 As shown, process 200 includes the following steps:

[0036] Step 210: Obtain the optical fiber parameters of the optical fibers to be fused.

[0037] Fibers to be spliced ​​are fibers ready for fusion splicing. Before fusion splicing, optical fibers undergo a series of pretreatments to create the fibers. For example, pretreatments include stripping the fiber's coating and other protective layers, cleaning the fiber, and cleaving it. When cleaving the fiber, it's crucial to ensure a smooth and clean cut surface.

[0038] Fiber parameters refer to parameters related to the nature of the fiber. Examples include fiber grade, fiber material, and fiber type. The fiber grade reflects fiber quality; the better the quality, the higher the grade. Fiber materials can include glass and plastic. Fiber types include single-mode and multimode fibers, dispersion-shifted and non-zero dispersion-shifted fibers.

[0039] In some embodiments, the processor can obtain fiber parameters through various methods. For example, the processor can obtain fiber parameters by accessing a fiber database based on the fiber model used for splicing. The fiber database stores various fiber models and their corresponding fiber parameters. For another example, the fiber parameters can be determined through manual testing, and the processor can obtain the fiber parameters input after manual testing.

[0040] Step 220: Acquire environmental parameters and predict the degree of environmental impact of the environmental parameters on the optical fiber fusion splicing.

[0041] Environmental parameters refer to parameters related to the environment during fiber splicing. In some embodiments, these parameters include at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset timeframe. Air particle concentration refers to the concentration of particulate matter (e.g., dust) in the air and can reflect the cleanliness of the air.

[0042] In some embodiments, the preset time can be system- or manually preset. In some embodiments, the length of the preset time is related to the fiber grade and historical fiber splice quality. A higher fiber grade and a lower historical splice quality correspond to a longer preset time. The preset time is less than a threshold length, which can be preset based on experience.

[0043] The quality of a fiber fusion splice reflects the quality of the splice. This quality can be determined by whether there are any abnormalities in the splice. Abnormalities indicate poor splice quality. Examples of abnormalities include axial misalignment, angular misalignment, bubbles, and separation.

[0044] You can set multiple time points within a preset time. Each time point corresponds to an environmental parameter. The number of time points is determined by the length of the preset time. The longer the preset time, the more time points there are.

[0045] In some embodiments, within the preset time, the processor may determine a time point at each preset time interval, and the preset time interval may be pre-set based on experience. The preset time and time point are merely exemplary and do not constitute a limitation to the implementation method.

[0046] In some embodiments, the processor may obtain environmental parameters through various sensors. For example, the processor may obtain pressure, temperature, and air particle concentration through a temperature sensor, a pressure sensor, and an air quality sensor, respectively.

[0047] The degree of environmental impact refers to the impact of environmental parameters on fiber optic splicing.

[0048] In some embodiments, the processor can analyze environmental parameter data such as air pressure, temperature, and air particle concentration at multiple time points, perform cluster analysis based on numerical values, and group environmental parameters with the same or similar numerical values ​​into one category to determine the corresponding degree of environmental impact. In some embodiments, the processor can determine the corresponding degree of environmental impact based on the current environmental parameters by querying a preset table. The preset table includes a correspondence between different types of environmental parameters and different degrees of environmental impact. The preset table can be determined based on prior knowledge or historical data.

[0049] In some embodiments, the degree of environmental impact includes at least the degree of anti-interference of the welding discharge intensity; the processor can determine the upper and lower limits of the environmental parameters and the environmental fluctuation range within a preset time based on the environmental parameters; and determine the degree of anti-interference based on the upper and lower limits of the environmental parameters and the environmental fluctuation range.

[0050] The degree of interference resistance of welding discharge intensity refers to the ability to maintain welding discharge intensity when disturbed by environmental parameters during the welding process. A higher degree of interference resistance indicates less interference with welding discharge intensity due to environmental parameters. Maintaining the welding discharge intensity means that the change in welding discharge intensity is less than a threshold, which can be preset based on experience.

[0051] Environmental parameter limits refer to the upper and lower limits of multiple environmental parameters at multiple time points within a preset time period. These limits can include pressure limits, temperature limits, and air particle concentration limits.

[0052] The environmental fluctuation amplitude refers to the amplitude of the numerical fluctuation of the environmental parameters within a preset time.

[0053] In some embodiments, the processor can determine the upper and lower limits of environmental parameters among multiple environmental parameters through statistical analysis based on environmental parameters monitored by multiple sensors at multiple time points within a preset time, and use the difference between the upper and lower limits as the environmental fluctuation amplitude.

[0054] In some embodiments, the environmental fluctuation amplitude also includes the number of times the difference between environmental parameters corresponding to multiple time points within a unit time is greater than the fluctuation threshold. The greater the number of times, the greater the environmental fluctuation amplitude. The difference may include the difference between the environmental parameter values ​​of two adjacent time points within the unit time.

[0055] Fluctuation refers to the difference in environmental parameters between two adjacent time points. The fluctuation threshold is the critical value used to determine whether a fluctuation is abnormal. The fluctuation threshold can be pre-set based on experience or needs.

[0056] In some embodiments, the processor can determine the anti-interference degree based on the upper and lower limits of the environmental parameters and the environmental fluctuation range in various ways. For example, the processor can determine the anti-interference degree based on the upper and lower limits of the environmental parameters and the environmental fluctuation range using a preset determination rule.

[0057] In some embodiments, the preset determination rule may be: the greater the environmental fluctuation amplitude, the lower the anti-interference level. In some embodiments, the preset determination rule may also be: the further the upper and lower limits of the environmental parameters exceed the preset range of the environmental parameters, the lower the anti-interference level. The preset range may be manually set based on relevant knowledge or experience.

[0058] In some embodiments of this specification, the degree of anti-interference is determined by the number of times the difference in environmental parameters between multiple time points in a unit time is greater than the fluctuation threshold. This can more reasonably measure the degree of anti-interference and avoid the impact caused by the upper limit value of the environmental parameter at an individual time point being too high or the lower limit value being too low.

[0059] In some embodiments, the processor may evaluate the degree of interference resistance based on the interference model.

[0060] In some embodiments, the interference model may be a machine learning model with a custom structure as described below, or a machine learning model with other structures, for example, a neural network model (NN).

[0061] In some embodiments, the interference model may include a pre-processing layer and an evaluation layer.

[0062] The input to the preprocessing layer may include environmental parameters at multiple time points within a preset timeframe, and the output may include environmental features. Environmental features are data in a fixed format that can be recognized by a machine learning model. In some embodiments, environmental features may include at least fixed-formatted data such as upper and lower limits of environmental parameters and the magnitude of environmental fluctuations. In some embodiments, the preprocessing layer may be a long short-term memory (LSTM) model.

[0063] The input of the evaluation layer may include environmental features, and the output may include the degree of anti-interference. In some embodiments, the evaluation layer may be a neural network model (NN).

[0064] In some embodiments, the preprocessing layer and the evaluation layer can be obtained through joint training.

[0065] In some embodiments, the first training sample for joint training may include sample environment parameters. The first training sample may be obtained based on historical data.

[0066] In some embodiments, the first label of the first training sample is whether it is interference-resistant (which can be represented by 0 or 1, with 0 indicating no interference resistance and 1 indicating interference resistance). The first label can be determined based on whether a welding anomaly occurs in the historical welding results corresponding to the first training sample. If an anomaly occurs, the first label is 0; if no anomaly occurs, the first label is 1.

[0067] An exemplary joint training process includes: inputting sample environmental parameters into an initial preprocessing layer to obtain environmental features output by the initial preprocessing layer; and inputting the environmental features output by the initial preprocessing layer into an initial evaluation layer to obtain the degree of anti-interference output by the initial evaluation layer. The processor can construct a loss function based on the first label and the output of the initial evaluation layer, and update the parameters of the initial preprocessing layer and the initial evaluation layer until the preset conditions are met, the training is completed, and the trained preprocessing layer and evaluation layer are obtained. The preset conditions may include one or more of the following: the loss function is less than a threshold, converges, or the training cycle reaches a threshold.

[0068] In some embodiments of this specification, determining the degree of interference resistance based on an interference model can more quickly determine the upper and lower limits of environmental parameters, as well as the amplitude of environmental fluctuations, and thus the degree of interference resistance. This helps prepare a data foundation for the subsequent determination and adjustment of fiber splicing parameters, mitigating the adverse effects on fiber splicing quality. Furthermore, processing environmental parameters through an LSTM machine learning model can eliminate some of the noise interference data in the environmental parameters. Simultaneously, obtaining a trained interference model through joint training can help address the difficulty of obtaining labels when training the preprocessing layer alone in some cases.

[0069] Step 230: Determine the operating parameters of the automatic cleaning component based on the degree of environmental impact and the optical fiber parameters.

[0070] Working parameters refer to the data that controls the operation of the automatic cleaning components, such as the cleaning frequency.

[0071] In some embodiments, the processor can obtain the operating parameters of the automatic cleaning component through various methods. For example, the cleaning frequency of the automatic cleaning component can be preset based on experience. In another example, the cleaning frequency of the automatic cleaning component can be preset based on historical data. The historical cleaning frequencies of splicing processes that are identical or similar to the current environmental parameters and fiber parameters in the historical data are determined as the cleaning frequency for the current splicing process.

[0072] In some embodiments, the working parameters of the automatic cleaning component include at least the welding groove cleaning parameters; the processor can determine the welding groove cleaning parameters through a preset algorithm based on the anti-interference degree of the welding discharge intensity and the optical fiber parameters.

[0073] The welding tank cleaning parameters refer to the parameters for controlling the automatic cleaning components to clean the welding tank.

[0074] In some embodiments, the welding tank cleaning parameters include at least the opening duration of the vent valve. In some embodiments, the opening duration of the vent valve is related to the anti-interference degree. The lower the anti-interference degree, the longer the opening duration of the vent valve.

[0075] In some embodiments, the processor may determine the opening duration of the ventilation valve through a preset algorithm based on the anti-interference degree and the optical fiber parameters.

[0076] In some embodiments, when the anti-interference level is not lower than the anti-interference threshold, the preset algorithm may include: on-duration time=standard duration time / anti-interference level (percentage).

[0077] In some embodiments, when the anti-interference level is below an anti-interference threshold, the preset algorithm may include: On Duration = Standard Duration / Anti-interference Threshold. The anti-interference threshold can be pre-set based on experience; the Standard Duration can be determined based on fiber parameters by querying a standard ventilation schedule. The standard ventilation schedule stores multiple sets of different fiber parameters and their corresponding standard durations. The standard ventilation schedule can be obtained based on experience or historical data.

[0078] In some embodiments, the duration of the vent valve opening can also be related to the cleaning frequency of the automatic cleaning brush. In some embodiments, when the anti-interference level is not lower than the anti-interference threshold, the preset algorithm may include: opening duration = standard duration / anti-interference level (percentage) + automatic cleaning brush cleaning frequency * A, where A is a preset coefficient. In some embodiments, when the anti-interference level (percentage) is lower than the anti-interference threshold, the preset algorithm may include: opening duration = standard duration / anti-interference threshold.

[0079] In some embodiments, the splice tank cleaning parameters also include the number of pumping cycles of the suction device. In some embodiments, the processor may determine the number of pumping cycles of the suction device based on the anti-interference level and optical fiber parameters using a preset algorithm. For example, the preset algorithm may be such that the lower the anti-interference level, the greater the number of pumping cycles.

[0080] In some embodiments, the number of suctions by the suction device is related to the sweeping frequency of the automatic cleaning brush.

[0081] Among them, after the automatic cleaning brush cleans the lens, dust on the lens may be dispersed in the welding groove, affecting the cleaning effect of the welding groove. Before welding, a suction device is needed to extract the gas in the welding groove for cleaning.

[0082] In some embodiments, the processor can determine the number of sweeps of the automatic cleaning brush based on the sweeping frequency, and use the number of sweeps as the number of suctions of the suction device.

[0083] In some embodiments, the processor can determine a cleaning time point based on the cleaning frequency of the automatic cleaning brush, and further determine a sequence of suction start time points. Each time the automatic cleaning brush cleans, the suction device performs a suction operation. The suction time point occurs after the automatic cleaning brush completes cleaning. The suction time point can be determined based on the cleaning time of the automatic cleaning brush. The sequence of suction time points can be determined based on the cleaning time of each cleaning operation of the automatic cleaning brush.

[0084] In some embodiments of this specification, the time for introducing clean gas into the fusion tank and the frequency of extracting contaminated gas are determined based on the cleaning frequency, which can fully ensure the cleanliness of the fusion tank and meet the requirements of optical fiber fusion.

[0085] In some embodiments of the present specification, by using a vent valve, a vent tube, a suction device, etc., the duration of the vent valve opening is determined based on the degree of anti-interference and optical fiber parameters, and the duration of releasing clean gas is controlled, so that efficient and automatic cleaning of the fusion groove can be achieved, saving manpower and time costs, and helping to avoid impurities in the fusion groove from having a significant adverse effect on the quality of optical fiber fusion.

[0086] Step 240 : Determine the fiber advancement parameters based on the automatic cleaning result and the historical abnormal fusion data, and perform fusion splicing on the optical fiber to be fused according to the fiber advancement parameters.

[0087] The automatic cleaning result refers to the result after the welding tank is cleaned according to the working parameters of the automatic cleaning component, for example, clean or unclean.

[0088] In some embodiments, the processor can determine whether the cleanliness of the welding tank after automatic cleaning meets a preset cleanliness level. If the cleanliness level meets the preset cleanliness level, the automatic cleaning result is "clean"; if the cleanliness level does not meet the preset cleanliness level, the automatic cleaning result is "unclean." The cleanliness level is negatively correlated with the concentration of air particles; the higher the concentration of air particles, the lower the cleanliness level.

[0089] Historical abnormal welding data refers to historical welding data with abnormal welding results.

[0090] In some embodiments, the historical splicing abnormality data may be historical splicing data of abnormal splicing results when optical fibers of the same type as the optical fiber to be currently spliced ​​are spliced.

[0091] In some embodiments, the historical splice anomaly data may be historical splice anomaly data resulting from historical splicing of optical fibers of the same type as the currently spliced ​​optical fiber due to improper fiber advancement parameters. Examples of splice anomalies resulting from improper fiber advancement parameters include, but are not limited to, fiber separation, fibers that are too thick at the splice point, and fibers that are too thin at the splice point.

[0092] In some embodiments, the processor may obtain historical welding abnormality data based on historical welding record data stored in a storage device.

[0093] Fiber advancement parameters refer to the parameters that control the fusion splicing device's advancement of two optical fibers for butt-fusion splicing. In some embodiments, the fiber advancement parameters may include advancement rate, advancement time, and advancement distance. Advance rate * advancement time = advancement distance, where advancement distance refers to the distance the optical fibers are advanced during spark fusion splicing.

[0094] In some embodiments, the processor can determine fiber advancement parameters based on automated cleaning results and historical splice anomaly data in various ways. For example, the processor can pre-set the advancement amount based on actual needs. In some embodiments, the processor can pre-set the advancement rate based on historical splice anomaly data with the same fiber parameters. In response to thickening of the fiber splice, the advancement rate can be reduced; in response to thinning of the fiber splice, the advancement rate can be increased until an appropriate advancement rate is determined.

[0095] In some embodiments, the processor can obtain and determine the fiber propulsion parameters based on the air particle concentration change data. For more information, see Figure 3 and its related descriptions.

[0096] In some embodiments, the processor may control the fusion splicing device based on the optical fiber advancing parameters to advance the two optical fibers to be fused at an advancing rate and an advancing time for fusion splicing.

[0097] In some embodiments of the present specification, the degree of environmental impact is determined based on the optical fiber parameters and environmental parameters, and then the working parameters of the automatic cleaning components are determined, so that automatic cleaning of the fusion splicing device can be achieved, saving manpower and time costs. Based on the automatic cleaning results and historical fusion splicing abnormality data, the optical fiber advancement parameters are adjusted to improve the optical fiber fusion splicing quality.

[0098] It should be noted that the above description of process 200 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art may, under the guidance of this specification, make various modifications and alterations to process 200. However, such modifications and alterations remain within the scope of this specification. For example, step 210 and step 220 may be performed simultaneously.

[0099] Figure 3 is an exemplary schematic diagram of adjusting the optical fiber advancement rate according to some embodiments of this specification.

[0100] In some embodiments, the processor can obtain the air particle concentration change data 311 and adjust the optical fiber advancement rate based on the air particle concentration change data 311. For more information about the ventilation tube, please refer to Figure 1 and its related descriptions.

[0101] The air particle concentration change data refers to data used to reflect the concentration change of small particulate matter in the air. In some embodiments, the air particle concentration can be inside the welding tank. In some embodiments, the air particle concentration can be inside at least one ventilation pipe of the automatic cleaning component.

[0102] In some embodiments, the air particle concentration monitoring component connected to the ventilation tube can obtain air particle concentration data every preset time period and upload the air particle concentration data to the processor. The processor can obtain air particle concentration change data within the preset time period based on the continuously recorded air particle concentration data. The preset time period can be preset manually or by the system. For more information about the air particle concentration monitoring component, please refer to Figure 1 and its related descriptions.

[0103] In some embodiments, the processor may adjust the optical fiber advancement rate according to a preset rule based on the air particle concentration change data. The preset rule may be a rule determined based on experience. For example, the preset rule may be: when the air particle concentration change data indicates a gradual increase in air particle concentration, the optical fiber advancement rate is appropriately increased. For another example, the preset rule may be: when the air particle concentration change data indicates a small change in air particle concentration, but the air particle concentration remains consistently high, the optical fiber advancement rate is appropriately increased.

[0104] Since the concentration of air particles can affect the intensity of the splicing discharge, in some embodiments, the processor can adjust the fiber advancement rate based on the splicing discharge intensity. When the discharge intensity increases, the fiber advancement rate increases For more information on welding discharge intensity, please refer to Figure 2 and its related descriptions.

[0105] In some embodiments, the processor may predict the probability 330 of a fusion splice anomaly based on the air particle concentration change data 311 at multiple time points, and adjust the fiber advancement rate based on the prediction result. In particular, there may be one air particle concentration change data point between each two adjacent time points in the multiple time points.

[0106] Abnormal splicing refers to the potential for abnormal splicing quality due to changes in air particle concentration. Air particle concentration can affect the discharge intensity, which in turn affects the quality of the fiber splice.

[0107] In some embodiments, the processor may predict the probability 330 of a welding anomaly by using an anomaly model 320. The anomaly model may be a machine learning model, such as a recurrent neural network model.

[0108] In some embodiments, the input of the anomaly model 320 may include air particle concentration change data 311, fiber grade 312, and fiber type 313 at multiple time points; the output may include the probability 330 of a welding anomaly occurring at multiple future time points. The future time points can be preset by the system or manually. For more information about fiber grade and fiber type, please refer to Figure 2 and its related descriptions.

[0109] Anomaly model 320 is trained using a large number of second training samples with second labels. These second training samples may include sample air particulate concentration monitoring data, sample fiber grades, and sample fiber types at multiple sample time points. The second label indicates whether a splicing anomaly occurs at a time point subsequent to the sample time point. For example, the second label can be 0 or 1, with 0 indicating no splicing anomaly and 1 indicating a splicing anomaly. The second label can be obtained by annotating the splicing results corresponding to different air particulate concentration monitoring data in historical data.

[0110] In some embodiments, the prediction frequency is related to the degree of anti-interference. For example, the lower the degree of anti-interference, the greater the prediction frequency. For more information on the degree of anti-interference, see Figure 2 and its related descriptions.

[0111] The prediction frequency refers to the frequency of predicting the probability of welding anomalies using the anomaly model.

[0112] In some embodiments of this specification, the prediction frequency is adjusted according to the degree of anti-interference. When the degree of anti-interference is low, the probability of abnormal fusion can be predicted with a higher prediction frequency, which helps to adjust the optical fiber advancement rate more quickly.

[0113] In some embodiments, the processor may adjust the fiber advancement rate based on the prediction result using a preset adjustment rule. In some embodiments, the preset adjustment rule may be: when the probability of a splice anomaly exceeding a probability threshold, the fiber advancement rate is reduced. The probability threshold may be manually or system-preset.

[0114] In some embodiments, the processor may determine whether the time point of the prediction result is a credible time point 350 ; if so, the processor may adjust the optical fiber advancement rate 360 ​​in advance.

[0115] A credible time point refers to the future time point corresponding to the prediction result when the prediction result is credible.

[0116] In some embodiments, the processor can determine the confidence 340 of the prediction results corresponding to multiple future time points based on the comparison between the prediction results of multiple simulated predictions and the actual results; based on the confidence 340 of the prediction results, determine whether the time point of the prediction results is a credible time point.

[0117] Confidence indicates the reliability of the prediction results at a future time point. A higher confidence level indicates a more accurate prediction result at a future time point output by the anomaly model.

[0118] Multiple simulation prediction involves predicting the probability of a welding anomaly occurring at multiple future time points at multiple time points. This simulation prediction determines the confidence level of the model's prediction results for these multiple future time points. During the simulation prediction, the welding process proceeds normally. An exemplary multiple simulation prediction process includes predicting the probability of a welding anomaly occurring at one-minute intervals over the next ten minutes at sequentially arranged prediction time points A, B, C, etc., and obtaining ten prediction results at each prediction time point; ten time points are separated from each other by a time interval. When predicted time point B is reached, the actual results corresponding to the ten predicted results for the ten future time points of predicted time point A can be obtained. When the frequency of welding anomalies at any future time point is greater than a preset threshold (e.g., 50%), and the actual result is that a welding anomaly has occurred, this frequency can be used as the confidence level for that future time point. When the frequency of welding anomalies at any future time point is not greater than the preset threshold, and the actual result is that a welding anomaly has occurred, this frequency can be used as the confidence level for that future time point. When the frequency of welding anomalies at any future time point is greater than the preset threshold, and the actual result is that no welding anomaly has occurred, the result (1-this frequency) can be used as the confidence level for that future time point. When the frequency of welding anomalies at any future time point is not greater than the preset threshold, and the actual result is that no welding anomaly has occurred, the result (1-this frequency) can be used as the confidence level for that future time point. Similarly, the confidence levels for the ten future time points of predicted time points B, C, and so on can be determined. Furthermore, the processor can determine as a credible time point any future time point whose confidence level exceeds the confidence threshold more than a threshold number of times. The preset threshold value may be a system or human preset value.

[0119] A trusted time point can indicate a time point among multiple future time points output by the anomaly model for which the prediction result is reliable. For example, if the trusted time point is the second time point among multiple future time points, then the anomaly model's prediction for the second future time point is relatively accurate.

[0120] In some embodiments, the prediction results of multiple simulations can include prediction results for the same future time point. For example, the probability of a welding anomaly occurring within the next minute can be predicted every two minutes based on the anomaly model. In this case, the future time point 2 in the first prediction and the future time point 1 in the second prediction can be the same future time point. The frequency of multiple simulations is related to the degree of anti-interference. For more information, see the previous section.

[0121] The actual result refers to whether a welding anomaly actually occurs at the future time point. The actual result can be 0 or 1, where 0 indicates no anomaly and 1 indicates an anomaly.

[0122] In some embodiments, during the actual prediction process using the anomaly model, the processor can adjust the fiber advancement rate in advance based on the previously determined reliable time points. In some embodiments, the processor can adjust the fiber advancement rate in advance when the frequency of fusion anomalies corresponding to the reliable time points exceeds a frequency threshold. The frequency threshold can be preset by the system or manually.

[0123] Advance refers to adjusting the fiber advancement rate before the trusted time point arrives. Adjusting the fiber advancement rate can include pausing advancement or adjusting the fiber advancement rate.

[0124] In some embodiments, the direction of adjusting the optical fiber advancement rate can be determined based on the air particle concentration change data. The adjustment direction can include increasing or decreasing the optical fiber advancement rate. For more information on adjusting the optical fiber advancement rate based on air particle concentration change data, see the relevant description above.

[0125] After purging the welding tank with clean air, the air particle concentration monitoring component next to the vent pipe can continuously determine the cleanliness level (i.e., air particle concentration) of the air discharged from the welding tank. However, since the welding tank and the draft shield are not airtight, a small amount of outside air may enter. Clean air purging may also leave blind spots, and the air particle concentration in the welding tank may eventually stabilize at a constant level.

[0126] In some embodiments of this specification, adjusting the fiber advancement rate based on air particle concentration changes within at least one ventilation tube helps ensure fiber splicing quality and avoids the high labor and time costs associated with repeated splicing. By combining air particle concentration change data at multiple time points with fiber grade and type, an anomaly model can quickly and accurately determine the probability of a splicing anomaly and, based on this probability, determine whether to adjust the fiber advancement rate. By determining reliable time points, it is possible to reasonably and accurately assess whether a splicing anomaly will occur at a future time, facilitating advance adjustments or preparations (such as pausing the splice or adjusting the fiber advancement rate).

[0127] One or more embodiments of the present specification also provide a fiber fusion control system, including: an acquisition module for acquiring fiber parameters of the optical fiber to be fused; a processing module for acquiring environmental parameters and predicting the degree of environmental impact of the environmental parameters on the optical fiber fusion, the environmental parameters including at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time; a determination module for determining the working parameters of the automatic cleaning component based on the environmental impact degree and the optical fiber parameters; a fusion module for determining the optical fiber advancement parameters based on the automatic cleaning results and historical fusion abnormality data, and fusion splicing the optical fiber to be fused according to the optical fiber advancement parameters.

[0128] One or more embodiments of this specification further provide a computer-readable storage medium, wherein the storage medium stores computer instructions. When a computer reads the computer instructions, the computer executes the optical fiber fusion splicing control method according to any one of the above embodiments.

[0129] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0130] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0131] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0132] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0133] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may vary according to the required features of the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values ​​are as accurate as possible within the feasible range.

[0134] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This excludes any application history documents that are inconsistent with or conflicting with the content of this specification, as well as any documents (currently or subsequently appended to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0135] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. A method for controlling optical fiber fusion splicing, characterized in that: The method is implemented based on an optical fiber fusion splicing device and includes: Obtaining optical fiber parameters of the optical fiber to be spliced; Acquire environmental parameters and predict the environmental impact of the environmental parameters on optical fiber fusion splicing, wherein the environmental parameters include at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time, and the environmental impact includes at least the anti-interference degree of the fusion splicing discharge intensity; Determining working parameters of an automatic cleaning component based on the degree of environmental impact and the optical fiber parameters; Based on the automatic cleaning result and the historical abnormal fusion data, the optical fiber advancement parameters are determined, and the optical fiber to be fused is fused according to the optical fiber advancement parameters; the optical fiber advancement parameters at least include the optical fiber advancement rate, and the method further includes: Obtain air particle concentration change data; Based on the air particle concentration change data at multiple time points, predict the probability of welding abnormality, wherein the predicted frequency of the welding abnormality probability is related to the anti-interference degree; Based on the prediction result, adjusting the optical fiber advancement rate; and In response to the time point at which the prediction result is located being a credible time point, the optical fiber advancement rate is adjusted in advance.

2. The method according to claim 1, characterized in that The method for determining the degree of anti-interference includes: Determine the upper and lower limits of the environmental parameters and the environmental fluctuation range within the preset time based on the environmental parameters; The anti-interference degree is determined based on the upper and lower limits of the environmental parameters and the environmental fluctuation amplitude.

3. The method according to claim 2, characterized in that The automatic cleaning component at least includes a welding groove cleaning subcomponent, and the welding groove cleaning subcomponent includes at least one ventilation pipe, a ventilation valve, and a compressed gas tank. The working parameters of the automatic cleaning component at least include welding groove cleaning parameters; the working parameters of the automatic cleaning component determined based on the environmental impact degree and the optical fiber parameters include: Based on the anti-interference degree and the optical fiber parameters, the fusion groove cleaning parameters are determined by a preset algorithm, and the fusion groove cleaning parameters at least include the opening duration of the vent valve.

4. An optical fiber fusion control device, characterized in that: The device includes a processor, a welding device and an automatic cleaning component that are communicatively connected to the processor; wherein, The fusion splicing device is used to perform fusion splicing on the optical fiber to be fused; the fusion splicing device comprises a fusion splicing slot and a camera; The fusion splicing groove is used to place the optical fiber to be fusion spliced ​​for fusion splicing; The camera is used to obtain the alignment inspection result and the fusion inspection result of the optical fiber to be fusion-spliced; The automatic cleaning component is used to clean the welding device; The processor is at least configured to: Obtaining optical fiber parameters of the optical fiber to be spliced; Acquire environmental parameters and predict the environmental impact of the environmental parameters on optical fiber fusion splicing, wherein the environmental parameters include at least one of air pressure, temperature, and air particle concentration at multiple time points within a preset time, and the environmental impact includes at least the anti-interference degree of the fusion splicing discharge intensity; Determining the working parameters of the automatic cleaning component based on the environmental impact degree and the optical fiber parameters; Based on the automatic cleaning result and the historical fusion abnormality data, determine the optical fiber advancement parameter, and fuse the optical fiber to be fused according to the optical fiber advancement parameter, wherein the optical fiber advancement parameter at least includes the optical fiber advancement rate, and the processor is further used for: Obtain air particle concentration change data; Based on the air particle concentration change data at multiple time points, predict the probability of welding abnormality, wherein the predicted frequency of the welding abnormality probability is related to the anti-interference degree; Based on the prediction result, adjusting the optical fiber advancement rate; and In response to the time point at which the prediction result is located being a credible time point, the optical fiber advancement rate is adjusted in advance.

5. The device according to claim 4, characterized in that The processor is further configured to: Determine the upper and lower limits of the environmental parameters and the environmental fluctuation range within the preset time based on the environmental parameters; The anti-interference degree is determined based on the upper and lower limits of the environmental parameters and the environmental fluctuation amplitude.

6. The device according to claim 5, characterized in that The automatic cleaning component at least includes a welding groove cleaning subcomponent, and the welding groove cleaning subcomponent includes at least one ventilation pipe, a ventilation valve, and a compressed gas tank. The working parameters of the automatic cleaning component at least include welding groove cleaning parameters; the processor is further used to: Based on the anti-interference degree and the optical fiber parameters, the fusion groove cleaning parameters are determined by a preset algorithm, and the fusion groove cleaning parameters at least include the opening duration of the vent valve.

7. A computer-readable storage medium storing computer instructions, characterized in that: When a computer reads the computer instruction, the computer executes the optical fiber fusion splicing control method according to any one of claims 1 to 3.

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