Single crystal optical fiber loss online monitoring method and device, storage medium and equipment

Abstract

The invention discloses a single-crystal optical fiber loss on-line monitoring method and device, a storage medium and equipment. In the process of preparing a single-crystal optical fiber, a target melting zone image sequence for drawing the single-crystal optical fiber in a specified time period is acquired through preset image acquisition equipment; furthermore, the quality of the drawn single crystal optical fiber can be monitored on line through a preset monitoring model according to the obtained target melting zone image sequence, and alarm information can be generated when the drawn single crystal optical fiber is monitored to be abnormal; therefore, the equipment for drawing the single-crystal optical fiber can make production adjustment in time according to the alarm information, so that the yield of the single-crystal optical fiber can be improved, and the cost for preparing the single-crystal optical fiber can be reduced.

Description

Technical Field

[0001] This specification relates to the field of optical fiber technology, and in particular to a method, device, storage medium, and equipment for online monitoring of single-crystal optical fiber loss. Background Art

[0002] Single-crystal optical fiber is a single-crystal material in the form of an optical fiber. It not only has the excellent physical, chemical, and optical properties of bulk crystal materials, but also has the unique characteristics of high heat dissipation efficiency and the ability to achieve long-distance total reflection waveguides. These characteristics make single-crystal optical fiber widely used in aerospace and other fields.

[0003] Typically, the transmission loss of single-crystal fibers directly affects the output power of fiber lasers, the detection sensitivity of fiber sensors, and the transmission distance of fiber-optic communication systems. Low transmission loss means minimal energy loss when transmitting light signals through single-crystal fibers, enabling efficient light transmission over longer distances. Therefore, controlling the transmission loss of single-crystal fibers is crucial for applications in high-power laser transmission, mid-infrared laser technology, high-energy ray detection, and high-temperature sensing.

[0004] During the laser-heated pedestal growth (LHPG) process for single-crystal optical fiber production, a laser heating system precisely focuses a laser beam on the top of a raw material rod. This focused laser beam melts the top of the rod, creating a molten zone. A motor-driven device then pulls the rod upward at a controlled speed, stretching the molten material in the molten zone into a long, thin fiber. With the continuous pulling of the raw material rod and the steady heating of the laser beam, the molten material gradually solidifies and eventually forms an optical fiber with a single crystal structure. In this process, due to the influence of factors such as environmental vibration, laser power fluctuation, and raw material uniformity, the diameter of the drawn optical fiber will fluctuate, and the transmission loss in different areas will also be different. That is, there may be local optical fibers in the drawn single crystal optical fiber that have large transmission losses due to impurity contamination or insufficient surface smoothness. The transmission loss of the single crystal optical fiber can only be measured after the entire optical fiber is removed from the system after drawing, and cannot be measured in real time during the preparation process. When an abnormality is found in the drawing of the single crystal optical fiber, the drawing is stopped at the abnormal point of the single crystal optical fiber, a part of the optical fiber and the raw material rod is cut off, and the drawing is continued to maintain the quality of the entire optical fiber. Therefore, the prepared single crystal optical fiber may need to be re-prepared due to the large transmission loss, which leads to a high preparation cost of the single crystal optical fiber.

[0005] Therefore, how to reduce the cost of preparing single crystal optical fibers is an urgent problem to be solved. Summary of the Invention

[0006] This specification provides a method, apparatus, storage medium and equipment for online monitoring of single crystal optical fiber loss, in order to partially solve the above-mentioned problems existing in the prior art.

[0007] This manual adopts the following technical solutions: This specification provides a method for online monitoring of single-crystal optical fiber loss, including: Acquire a target melt zone image sequence, wherein each target melt zone image is an image of a melt zone of a raw material rod used to prepare a single crystal optical fiber, acquired during the process of preparing the single crystal optical fiber. The target melt zone image includes bright lines formed within the drawn optical fiber due to the penetration of preset illumination light, and different target melt zone images are acquired at different times within a specified time period; Inputting the target melt zone image sequence into a pre-trained monitoring model, so that the monitoring model determines target melt zone characteristics based on the target melt zone image sequence, and estimates the transmission loss value of the single crystal optical fiber drawn within the specified time period based on the target melt zone characteristics, wherein the target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the change in the diameter of the drawn optical fiber, and the change in the shape of the melt zone; If it is determined that the transmission loss value meets the preset alarm condition, an alarm message is generated and the alarm message is uploaded so as to execute the task according to the alarm message.

[0008] Optionally, the monitoring model includes: a convolution module; Inputting the target melt zone image sequence into a pre-trained monitoring model to determine the target melt zone characteristics based on the target melt zone image sequence through the monitoring model, specifically including: Inputting the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract image convolution features corresponding to the target melt zone image from the target melt zone image; The target melting zone features are determined according to the image convolution features corresponding to each target melting zone image.

[0009] Optionally, the monitoring model includes: a sequence feature extraction module; According to the image convolution features corresponding to each target melt zone image, the target melt zone features are determined, specifically including: According to the order of acquisition time of each target melt zone image, the image convolution features corresponding to each target melt zone image are sequentially input into the sequence feature extraction module, so that the sequence feature extraction module performs several rounds of processing based on the image convolution features corresponding to each target melt zone image to obtain the sequence features corresponding to the target melt zone image sequence as the target melt zone features; wherein, For each round of processing, the image convolution features of the target melt zone image input to the sequence feature extraction module in the round of processing are determined as the target image convolution features, and the candidate target melt zone features obtained after the previous round of processing are obtained, and the target image convolution features are fused with the candidate target melt zone features to obtain the initial candidate target melt zone features in the current round of processing. The initial candidate target melt zone features are screened through a preset forgetting function to obtain the candidate target melt zone features after the current round of processing, until it is determined that the preset termination condition is met. At this time, the sequence features corresponding to the target melt zone image sequence are obtained as the target melt zone features.

[0010] Optionally, training a monitoring model includes: Acquiring an initial monitoring model, a sample melt zone image sequence, and an actual transmission loss value of a single crystal optical fiber drawn within a time period corresponding to the sample melt zone image sequence; Inputting the sample melt zone image sequence into the initial monitoring model, so as to determine the sample melt zone characteristics based on the sample melt zone image sequence through the initial monitoring model, and estimating the transmission loss value of the single crystal optical fiber drawn within a time period corresponding to the sample melt zone image sequence based on the sample melt zone characteristics, as the estimated transmission loss value; According to the deviation between the estimated transmission loss value and the actual transmission loss value, a target loss is determined, and the initial monitoring model is trained with the goal of minimizing the target loss to obtain a monitoring model, wherein the target loss is positively correlated with the deviation between the estimated transmission loss value and the actual transmission loss value.

[0011] Optionally, the actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence is measured and calculated by a spectrometer connected to the end of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence.

[0012] This specification provides a single crystal optical fiber loss online monitoring device, including: an acquisition module for acquiring a target melt zone image sequence, wherein each target melt zone image is an image of the melt zone of a raw material rod used to prepare a single crystal optical fiber, acquired during the process of preparing the single crystal optical fiber, and each target melt zone image includes bright lines formed within the drawn optical fiber due to the penetration of preset illumination light, and different target melt zone images are acquired at different times within a specified time period; a prediction module, configured to input the target melt zone image sequence into a pre-trained monitoring model, so as to determine target melt zone characteristics based on the target melt zone image sequence through the monitoring model, and to estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period based on the target melt zone characteristics, wherein the target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the change in the diameter of the drawn optical fiber, and the change in the shape of the melt zone; The execution module is used to generate alarm information when it is determined that the transmission loss value meets the preset alarm condition, and upload the alarm information to perform the task according to the alarm information.

[0013] Optionally, the monitoring model includes: a convolution module; The prediction module is specifically used to input the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract the image convolution feature corresponding to the target melt zone image from the target melt zone image; and determine the target melt zone feature based on the image convolution feature corresponding to each target melt zone image.

[0014] Optionally, the monitoring model includes: a sequence feature extraction module; The prediction module is specifically configured to, according to the order of acquisition time of each target melt zone image, sequentially input the image convolution features corresponding to each target melt zone image into the sequence feature extraction module, so that the sequence feature extraction module performs several rounds of processing based on the image convolution features corresponding to each target melt zone image, so as to obtain sequence features corresponding to the target melt zone image sequence as target melt zone features; wherein, for each round of processing, the image convolution features of the target melt zone image input to the sequence feature extraction module in the round of processing are determined as target image convolution features, and candidate target melt zone features obtained after the previous round of processing are obtained, the target image convolution features are fused with the candidate target melt zone features to obtain initial candidate target melt zone features in the current round of processing, and the initial candidate target melt zone features are screened by a preset forgetting function to obtain candidate target melt zone features after the current round of processing, until a preset termination condition is determined to be satisfied, at which point the sequence features corresponding to the target melt zone image sequence are obtained as target melt zone features.

[0015] This specification provides a computer-readable storage medium, wherein the storage medium stores a computer program. When the computer program is executed by a processor, the above-mentioned single-crystal optical fiber loss online monitoring method is implemented.

[0016] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned single-crystal optical fiber loss online monitoring method is implemented.

[0017] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects: In the single crystal optical fiber loss online monitoring method provided in this specification, a target melt zone image sequence is obtained, wherein the target melt zone image sequence includes each target melt zone image of a raw material rod used to prepare the single crystal optical fiber, which is collected during the process of preparing the single crystal optical fiber. The target melt zone image includes bright lines formed inside the drawn optical fiber due to the penetration of preset illumination light. Different target melt zone images are collected at different times within a specified time period. The target melt zone image sequence is input into a pre-trained monitoring model to determine the target melt zone characteristics based on the target melt zone image sequence through the monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period based on the target melt zone characteristics. The target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the diameter change of the drawn optical fiber, and the shape change of the melt zone. If it is determined that the transmission loss value meets the preset alarm condition, an alarm message is generated and the alarm message is uploaded so that the task is executed according to the alarm message.

[0018] It can be seen from the above method that, in the process of preparing single crystal optical fiber, a target melt zone image sequence of the single crystal optical fiber drawn within a specified time period can be collected by a preset image acquisition device, and then the quality of the single crystal optical fiber being drawn can be monitored online according to the acquired target melt zone image sequence through a preset monitoring model, and when an abnormality is detected in the drawn single crystal optical fiber, an alarm message can be generated, so that the equipment used to draw the single crystal optical fiber can make production adjustments in time according to the alarm message, thereby improving the yield of the single crystal optical fiber and reducing the cost of preparing the single crystal optical fiber. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The exemplary embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation of this specification. In the drawings: Figure 1 A schematic diagram of the LHPG system principle provided in this specification; Figure 2 A schematic diagram of a flow chart of a method for online monitoring of single-crystal optical fiber loss provided in this specification; Figure 3 A schematic diagram of a target melting zone image provided in this specification; Figure 4 A schematic diagram of the process of determining the transmission loss value of a single crystal optical fiber provided in this specification; Figure 5 A schematic diagram of the measurement process of the actual transmission loss value provided in this manual; Figure 6 This is a schematic diagram of a single-crystal optical fiber loss online monitoring device provided in this specification; Figure 7 This manual provides a corresponding Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of this specification more clear, the following will clearly and completely describe the technical solutions of this specification in conjunction with the specific embodiments of this specification and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this specification.

[0021] At present, the commonly used methods for preparing single crystal optical fibers include the guided mode method, micro-pull-down method, and laser heated pedestal growth (LHPG). Among them, the LHPG method is particularly good at preparing ultra-fine single crystal optical fibers with diameters below 100 microns, while other methods such as micro-pull-down method (μ-PD) and guided mode method (EFG) are usually more used to prepare optical fibers with diameters ranging from 0.5 to 2 mm. This makes the LHPG method widely used in national defense and civilian fields such as high-energy lasers, high-temperature sensing, radiation detection, and information communications. Figure 1 shown.

[0022] Figure 1 This is a schematic diagram of the LHPG system principle provided in this manual.

[0023] Combine Figure 1As can be seen, during the LHPG system's single-crystal fiber production process, a laser beam is transmitted through a carefully designed optical path and focused onto a raw material rod at the bottom of the growth system. The high laser energy melts the material at the top of the raw material rod, forming a molten zone. Simultaneously, a motor-driven system pulls the fiber upward at a constant speed, while another motor feeds the raw material rod upward at a corresponding speed. As the raw material rod is continuously fed and the fiber is pulled upward, the material in the molten zone gradually solidifies, forming a single-crystal fiber. By precisely controlling the laser power, feed rate, and pulling speed, the diameter and quality of the single-crystal fiber can be effectively controlled.

[0024] However, during the single-crystal fiber production process, the transmission loss of the grown single-crystal fibers can vary due to factors such as environmental vibration, laser power fluctuations, and raw material uniformity (e.g., contamination of a localized area within the raw material rod). Since the transmission loss of a single-crystal fiber can only be measured after the entire fiber is drawn and removed from the LHPG system, if a produced single-crystal fiber is determined to be substandard, it is often necessary to re-produce the single-crystal fiber. Therefore, real-time monitoring of the quality of the produced single-crystal fiber during the production process is particularly important.

[0025] Currently, the most commonly used online monitoring technology for single-crystal optical fibers involves using a pre-set image acquisition device to capture images of the melt zone during the LHPG system's single-crystal fiber production process and measure the diameter of the newly drawn single-crystal fiber. If the diameter of the newly drawn single-crystal fiber is detected to be increasing, the drawing speed of the upper motor can be appropriately increased to reduce the fiber diameter. This monitoring and control method can keep fluctuations in the diameter of the produced single-crystal fiber within a certain range, thereby improving the quality of the produced single-crystal fiber.

[0026] However, single-crystal fiber diameter fluctuation is only one aspect affecting fiber quality; the ultimate determinant of fiber quality lies in its transmission loss. For example, in actual production, the raw material rod may be contaminated with tiny impurities, which can cause a small section of the drawn single-crystal fiber to have abnormally high transmission loss. Even if the diameter fluctuation of the entire fiber meets manufacturing requirements, the high loss of this small section will increase the transmission loss of the entire fiber, affecting its performance.

[0027] Therefore, if the transmission loss of single-crystal optical fibers during the drawing process can be monitored in real time, prompt action can be taken when an anomaly is detected, such as stopping the drawing process, cutting off the problematic fiber section and raw material rod, and then continuing the drawing process. This not only ensures the quality of the entire optical fiber but also effectively reduces the cost increase caused by quality issues.

[0028] The technical solutions provided by the various embodiments of this specification are described in detail below with reference to the accompanying drawings.

[0029] Figure 2 This is a flow chart of a method for online monitoring of single-crystal optical fiber loss provided in this specification, comprising the following steps: S201: Acquire a target melt zone image sequence, wherein the target melt zone image sequence includes each target melt zone image of a raw material rod melt zone for preparing a single crystal optical fiber, which is acquired during the process of preparing the single crystal optical fiber. The target melt zone image includes bright lines formed inside the drawn optical fiber due to the penetration of preset illumination light, and different target melt zone images are acquired at different times within a specified time period.

[0030] In this specification, the business platform can obtain a target melt zone image sequence, and through a preset monitoring model, predict the transmission loss value of the single crystal optical fiber corresponding to each target melt zone image contained in the target melt zone image sequence based on the obtained target melt zone image sequence, and then generate a control instruction for controlling the preparation of the single crystal optical fiber according to the predicted transmission loss value, so as to control the stopping of the preparation of the single crystal optical fiber according to the control instruction.

[0031] The target melting zone image sequence includes various target melting zone images, and different target melting zone images are acquired by a preset image acquisition device at different times within a specified time period.

[0032] For example, the designated time period for acquiring each target melt zone image in each target melt zone image sequence can be set to 30 seconds. During this time, an image is acquired every 3 seconds using the preset image acquisition device, ultimately obtaining 10 target melt zone images to form the target melt zone image sequence. This acquisition method ensures that each target melt zone image in the target melt zone image sequence is evenly distributed in time, providing a continuous image sequence with temporal resolution for subsequent analysis.

[0033] The above-mentioned image acquisition device can be a hardware device that can capture and record images, such as: a sensor, a digital camera, a camera, an industrial camera and other electronic components.

[0034] The above-mentioned target melting zone image includes bright lines formed inside the drawn optical fiber due to the penetration of the preset illumination light, as shown in FIG. Figure 3 shown.

[0035] Figure 3 This is a schematic diagram of the target melting zone image provided in this specification.

[0036] exist Figure 3In the figure, the image on the left is the target melt zone image corresponding to the single crystal fiber with a higher transmission loss value, and the image on the right is the target melt zone image corresponding to the single crystal fiber with a lower transmission loss value. After the pre-set camera and lighting, a bright line can be observed in the center of the single crystal fiber due to the penetration of the illumination fiber. This bright line is formed by the illumination light transmitted by the fiber. Since the illumination light itself is uniform, the unevenness of the light spot on the bright line in the center of the single crystal fiber can reflect the transmission loss of the single crystal fiber. There will be obvious differences in the brightness and distribution of the light spot in the area with higher loss and the area with lower loss, that is, in the area with higher loss, the brightness and distribution of the light spot will be different. Figure 3 The white lines in the single-crystal fiber region in the left image are intermittent, while Figure 3 The white lines in the single-crystal fiber region in the left image are uniform and continuous. Therefore, the monitoring model can be used to identify the target melt zone image and estimate the transmission loss of the corresponding single-crystal fiber.

[0037] In this specification, the execution entity for implementing the method for online monitoring of single-crystal optical fiber loss can refer to a designated device set up in a business platform by a server or Internet of Things (IoT) device, or it can refer to a terminal device such as a desktop computer or laptop computer installed with a control application of the LHPG system. For ease of description, the following only uses the terminal device as an example of the execution entity to illustrate the method for online monitoring of single-crystal optical fiber loss provided in this specification.

[0038] S202: Inputting the target melt zone image sequence into a pre-trained monitoring model, so as to determine the target melt zone characteristics according to the target melt zone image sequence through the monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period based on the target melt zone characteristics, wherein the target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the diameter change of the drawn optical fiber, and the shape change of the melt zone.

[0039] In this specification, after obtaining the target melt zone image sequence, the terminal device can input the obtained target melt zone image sequence into a pre-trained monitoring model to determine the target melt zone characteristics based on the target melt zone image sequence through the preset monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within a specified time period based on the target melt zone characteristics.

[0040] The target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the diameter change of the drawn optical fiber, and the shape change of the melt zone.

[0041] Specifically, the above-mentioned monitoring module may be provided with a convolutional neural network (CNN). In this case, the terminal device may input the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract the image convolution features corresponding to the target melt zone image from the target melt zone image, and determine the target melt zone features based on the image convolution features corresponding to each target melt zone image. Then, based on the target melt zone features, the transmission loss value of the single crystal optical fiber drawn within a specified time period may be estimated.

[0042] In actual application scenarios, in order to extract the dynamic features of each target melt zone image in the target melt zone image sequence that change over time, the above-mentioned monitoring model may also be provided with a sequence feature extraction module. At this time, the terminal device may determine the image convolution features corresponding to each target melt zone image through the convolution module of the monitoring model, and then input the image convolution features corresponding to each target melt zone image into the sequence feature extraction module in sequence according to the order of acquisition time of each target melt zone image, so as to perform several rounds of processing according to the image convolution features corresponding to each target melt zone image through the sequence feature extraction module to obtain the sequence features corresponding to the target melt zone image sequence as the target melt zone features.

[0043] Among them, for each round of processing, according to the order of the acquisition time of each target melt zone image, a target melt zone image is selected from each target melt zone image as the image to be processed in this round of processing, and the image convolution feature of the image to be processed in this round of processing is input into the sequence feature extraction module as the target image convolution feature in this round of processing, specifically as follows Figure 4 shown.

[0044] Figure 4 This is a schematic diagram of the process of determining the transmission loss value of the single crystal optical fiber provided in this specification.

[0045] Combine Figure 4 It can be seen that for each round of processing, the terminal device can determine the image convolution features of the target melt zone image input into the sequence feature extraction module in the round of processing as the target image convolution features, and obtain the candidate target melt zone features obtained after the previous round of processing, so that the target image convolution features can be fused with the candidate target melt zone features to obtain the initial candidate target melt zone features in the current round of processing, and the initial candidate target melt zone features are screened by a preset forgetting function to obtain the candidate target melt zone features after the current round of processing, until it is determined that the preset termination condition is met. At this time, the sequence features corresponding to the target melt zone image sequence are obtained as the target melt zone features.

[0046] In the above content, there are multiple methods for the terminal device to fuse the target image convolution features with the candidate target melt zone features to obtain the initial candidate target melt zone features in this round of processing. For example, according to the preset weights corresponding to the target image convolution features and the weights of the candidate target melt zone features, the target image convolution features and the candidate target melt zone features are weightedly fused.

[0047] In the above content, the preset forgetting function can be an activation function, and the output value is between 0 and 1. A value close to 0 indicates "forgetting" and a value close to 1 indicates "retaining". The process of screening the initial candidate target melt zone features through the preset forgetting function can refer to the following formula:

[0048]

[0049] In the above formula, is the activation value after the forgetting function, is the weight corresponding to the preset target image convolution feature, is the weight of the preset candidate target melt zone feature, is the convolution feature of the target image, is the candidate target melting zone feature, is the bias parameter, These are the characteristics of the candidate target melt zone after this round of processing.

[0050] Furthermore, after obtaining the sequence features corresponding to the target melt zone image sequence as the target melt zone features, the terminal device can input the target melt zone features into the decoding layer of the monitoring model, so as to obtain the target melt zone feature activation value corresponding to the target melt zone image sequence according to the input target melt zone features through the decoding layer of the monitoring model, and then the transmission loss value of the single crystal optical fiber drawn within the specified time period can be estimated according to the target melt zone feature activation value corresponding to the target melt zone image sequence.

[0051] The above-mentioned monitoring model can be deployed to the terminal device only after training. The training method of the monitoring model here can be that the terminal device obtains the initial monitoring model, the sample melt zone image sequence and the actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence, and inputs the pre-acquired sample melt zone image sequence into the initial monitoring model, so that the initial monitoring model can determine the sample melt zone characteristics according to the sample melt zone image sequence, and estimate the transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence according to the sample melt zone characteristics as the estimated transmission loss value, and then the target loss can be determined according to the deviation between the estimated transmission loss value and the actual transmission loss value, and the initial monitoring model is trained with the goal of minimizing the target loss to obtain the monitoring model.

[0052] The target loss is positively correlated with the deviation between the estimated transmission loss value and the actual transmission loss value.

[0053] The actual transmission loss value of the single crystal optical fiber drawn during the time period corresponding to the sample melt zone image sequence obtained by the terminal device can be measured and calculated by a spectrometer connected to the end of the single crystal optical fiber drawn during the time period corresponding to the sample melt zone image sequence, specifically as follows: Figure 5 shown.

[0054] Figure 5 This is a schematic diagram of the measurement process of the actual transmission loss values ​​provided in this manual.

[0055] Combine Figure 5 It can be seen that in the process of drawing a single crystal optical fiber, a section of seed optical fiber can be left at the end of the single crystal optical fiber being drawn, and the end face of the seed optical fiber can be polished to prepare a standard connector and connected to a preset spectrometer, so that the spectral data of the single crystal optical fiber being drawn can be measured by the spectrometer, and then the actual transmission loss value of the single crystal optical fiber drawn in the corresponding time period of the sample melt zone image sequence can be obtained by calculating the change value between the spectral data of the single crystal optical fiber drawn before the corresponding time period of the sample melt zone image sequence and the spectral data of the single crystal optical fiber drawn after the corresponding time period of the sample melt zone image sequence.

[0056] In actual application scenarios, since the spectral data of the single-crystal optical fiber being drawn obtained by the spectrometer often fluctuates (that is, the spectral data is not a curve that continuously decreases or increases, but a curve that fluctuates up and down), the actual transmission loss value of the single-crystal optical fiber drawn during the corresponding time period of the sample melt zone image sequence measured by the spectrometer may have a certain error.

[0057] Based on this, the method for the above-mentioned terminal device to obtain the actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence can also be to cut off the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence to obtain the single crystal optical fiber finally drawn within the time period corresponding to the sample melt zone image sequence as the sample single crystal optical fiber, so that a laser can be input at one end of the sample single crystal optical fiber and the laser power of the laser output by the sample single crystal optical fiber can be measured at the other end of the sample single crystal optical fiber. Then, a specified length of the sample single crystal optical fiber can be cut off at the other end of the sample single crystal optical fiber, and the laser power of the laser output from the other end of the remaining sample single crystal optical fiber can be measured again. The transmission loss value of the cut portion of the sample single crystal optical fiber is determined based on the difference in laser power obtained from the two measurements, and the above operation is repeated until the transmission loss value of the entire sample single crystal optical fiber is determined as the actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence.

[0058] S203: If it is determined that the transmission loss value meets the preset alarm condition, an alarm message is generated and the alarm message is uploaded so as to execute a task according to the alarm message.

[0059] In this specification, if the terminal device determines that the transmission loss value of the single crystal optical fiber drawn within a specified time period corresponding to the estimated target melting zone image sequence output by the monitoring model meets the preset alarm condition, an alarm message can be generated, and then the alarm message can be uploaded so that the control system used to control the drawing of the single crystal optical fiber can generate a control instruction based on the alarm information, and control the equipment used to draw the single crystal optical fiber to stop drawing and cut off the problematic optical fiber part and raw material rod through the control instruction, and then continue to draw the single crystal optical fiber.

[0060] Among them, the above-mentioned alarm conditions can be set according to actual needs. For example, if it is determined that the transmission loss value of the single crystal optical fiber drawn within a specified time period corresponding to the estimated target melting zone image sequence output by the monitoring model is higher than the preset threshold, it can be determined that the preset alarm conditions are met.

[0061] For another example: if it is determined that the difference between the transmission loss value of the single crystal optical fiber drawn within a specified time period corresponding to the estimated target melt zone image sequence output by the monitoring model and the historical transmission loss value output by the monitoring model exceeds a preset threshold, it can be determined that the preset alarm condition is met.

[0062] It should be noted that in actual application scenarios, the terminal device can also obtain the target melt zone video data, and input the target melt zone video data into a preset monitoring model, so that the monitoring model can predict the transmission loss value of the single crystal optical fiber corresponding to the target melt zone video data based on each frame of the target melt zone image contained in the target melt zone video data, and then generate an alarm information based on the predicted transmission loss value.

[0063] It can be seen from the above method that, in the process of preparing single crystal optical fiber, the terminal device can collect the target melting zone image sequence of the single crystal optical fiber drawn within a specified time period through the preset image acquisition device, and then use the preset monitoring model to perform online monitoring of the quality of the single crystal optical fiber being drawn based on the acquired target melting zone image sequence, and can generate an alarm message when it is detected that the drawn single crystal optical fiber has an abnormality, so that the equipment used to draw the single crystal optical fiber can make production adjustments in time according to the alarm information, thereby improving the yield of the single crystal optical fiber and reducing the cost of preparing the single crystal optical fiber.

[0064] The above is one or more implementations of the single crystal optical fiber loss online monitoring method of this specification. Based on the same idea, this specification also provides a corresponding single crystal optical fiber loss online monitoring device, such as Figure 6 shown.

[0065] Figure 6 A schematic diagram of a single-crystal optical fiber loss online monitoring device provided in this specification includes: An acquisition module 601 is configured to acquire a target melt zone image sequence, wherein each target melt zone image is an image of the melt zone of a raw material rod used to prepare a single crystal optical fiber, acquired during the process of preparing the single crystal optical fiber. The target melt zone image includes bright lines formed within the drawn optical fiber due to the penetration of preset illumination light. Different target melt zone images are acquired at different times within a specified time period. A prediction module 602 is configured to input the target melt zone image sequence into a pre-trained monitoring model, so that the monitoring model can determine target melt zone characteristics based on the target melt zone image sequence, and estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period based on the target melt zone characteristics. The target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the change in the diameter of the drawn optical fiber, and the change in the shape of the melt zone. The execution module 603 is configured to generate an alarm message when it is determined that the transmission loss value meets a preset alarm condition, and upload the alarm message so as to execute a task according to the alarm message.

[0066] Optionally, the monitoring model includes: a convolution module; The prediction module 602 is specifically used to input the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract the image convolution feature corresponding to the target melt zone image from the target melt zone image; and determine the target melt zone feature according to the image convolution feature corresponding to each target melt zone image.

[0067] Optionally, the monitoring model includes: a sequence feature extraction module; The prediction module 602 is specifically configured to, according to the order of acquisition time of each target melt zone image, sequentially input the image convolution features corresponding to each target melt zone image into the sequence feature extraction module, so that the sequence feature extraction module performs several rounds of processing based on the image convolution features corresponding to each target melt zone image, so as to obtain sequence features corresponding to the target melt zone image sequence as target melt zone features; wherein, for each round of processing, the image convolution features of the target melt zone images input to the sequence feature extraction module in the round of processing are determined as target image convolution features, and candidate target melt zone features obtained after the previous round of processing are obtained, the target image convolution features are fused with the candidate target melt zone features to obtain initial candidate target melt zone features in the current round of processing, and the initial candidate target melt zone features are screened using a preset forgetting function to obtain candidate target melt zone features after the current round of processing, until a preset termination condition is determined to be satisfied, at which point the sequence features corresponding to the target melt zone image sequence are obtained as target melt zone features.

[0068] Optionally, the apparatus further includes: a training module 604; The training module 604 is specifically used to obtain an initial monitoring model, a sample melt zone image sequence, and an actual transmission loss value of a single crystal optical fiber drawn within a time period corresponding to the sample melt zone image sequence; input the sample melt zone image sequence into the initial monitoring model, so as to determine the sample melt zone characteristics according to the sample melt zone image sequence through the initial monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within a time period corresponding to the sample melt zone image sequence according to the sample melt zone characteristics, as an estimated transmission loss value; determine a target loss according to a deviation between the estimated transmission loss value and the actual transmission loss value, and train the initial monitoring model with the goal of minimizing the target loss to obtain a monitoring model, wherein the target loss is positively correlated with the deviation between the estimated transmission loss value and the actual transmission loss value.

[0069] Optionally, the actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence is measured and calculated by a spectrometer connected to the end of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence.

[0070] This specification also provides a computer-readable storage medium, which stores a computer program that can be used to execute the above Figure 1 A single-crystal optical fiber loss online monitoring method is provided.

[0071] This manual also provides Figure 7 The one shown corresponds to Figure 1 Schematic diagram of the electronic equipment. Figure 7As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1 Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. In other words, the execution body of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0072] Improvements to a technology can be clearly categorized as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with technological advancements, many process flow improvements can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using physical hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can integrate a digital system onto a PLD by programming it themselves, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly performed using software called a "logic compiler." This is similar to the software compilers used during program development. Before compilation, the original code must be written in a specific programming language, called a hardware description language (HDL). There are many types of HDL, including ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages ​​and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0073] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the memory control logic. Those skilled in the art will also appreciate that, in addition to implementing the controller purely in computer-readable program code, the controller can also be implemented in the form of logic gates, switches, an application-specific integrated circuit, a programmable logic controller, an embedded microcontroller, etc. by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the means for implementing the various functions included therein can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can be considered both a software module implementing the method and a structure within the hardware component.

[0074] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0075] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0076] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Thus, this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0077] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0081] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0082] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0084] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0086] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0087] The foregoing is merely an example of the present invention and is not intended to limit the present invention. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for online monitoring of single crystal optical fiber loss, characterized in that: include: Acquire a target melting zone image sequence, wherein each target melting zone image is an image of a melting zone of a raw material rod for preparing a single crystal optical fiber collected during the process of preparing the single crystal optical fiber, wherein the target melting zone image contains bright lines formed inside the drawn optical fiber due to the penetration of a preset illumination light, and different target melting zone images are collected at different times within a specified time period; Inputting the target melt zone image sequence into a pre-trained monitoring model, so as to determine the target melt zone characteristics according to the target melt zone image sequence through the monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period according to the target melt zone characteristics, wherein the target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the diameter change of the drawn optical fiber, and the shape change of the melt zone; If it is determined that the transmission loss value meets the preset alarm condition, an alarm message is generated and the alarm message is uploaded so as to perform a task according to the alarm message.

2. The method according to claim 1, characterized in that The monitoring model includes: a convolution module; Inputting the target melt zone image sequence into a pre-trained monitoring model, so as to determine the target melt zone characteristics according to the target melt zone image sequence through the monitoring model, specifically including: Inputting the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract image convolution features corresponding to the target melt zone image from the target melt zone image; The target melting zone features are determined according to the image convolution features corresponding to each target melting zone image.

3. The method according to claim 2, characterized in that The monitoring model includes: a sequence feature extraction module; According to the image convolution features corresponding to each target melt zone image, the target melt zone features are determined, specifically including: According to the sequence of acquisition time of each target melt zone image, the image convolution features corresponding to each target melt zone image are sequentially input into the sequence feature extraction module, so that the sequence feature extraction module performs several rounds of processing according to the image convolution features corresponding to each target melt zone image, so as to obtain the sequence features corresponding to the target melt zone image sequence as the target melt zone features; wherein, For each round of processing, the image convolution features of the target melt zone image input to the sequence feature extraction module in the round of processing are determined as the target image convolution features, and the candidate target melt zone features obtained after the previous round of processing are obtained, and the target image convolution features are fused with the candidate target melt zone features to obtain the initial candidate target melt zone features in the current round of processing, and the initial candidate target melt zone features are screened by a preset forgetting function to obtain the candidate target melt zone features after the current round of processing, until it is determined that the preset termination condition is met, at which time, the sequence features corresponding to the target melt zone image sequence are obtained as the target melt zone features.

4. The method according to claim 1, characterized in that Training monitoring models, including: Acquire an initial monitoring model, a sample melt zone image sequence, and an actual transmission loss value of a single crystal optical fiber drawn within a time period corresponding to the sample melt zone image sequence; Inputting the sample melt zone image sequence into the initial monitoring model, so as to determine the sample melt zone characteristics according to the sample melt zone image sequence through the initial monitoring model, and estimating the transmission loss value of the single crystal optical fiber drawn in the time period corresponding to the sample melt zone image sequence according to the sample melt zone characteristics as the estimated transmission loss value; According to the deviation between the estimated transmission loss value and the actual transmission loss value, a target loss is determined, and the initial monitoring model is trained with the goal of minimizing the target loss to obtain a monitoring model, wherein the target loss is positively correlated with the deviation between the estimated transmission loss value and the actual transmission loss value.

5. The method according to claim 4, characterized in that The actual transmission loss value of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence is measured and calculated by a spectrometer connected to the end of the single crystal optical fiber drawn within the time period corresponding to the sample melt zone image sequence.

6. A single crystal optical fiber loss online monitoring device, characterized in that: include: An acquisition module, used to acquire a target melting zone image sequence, wherein each target melting zone image is an image of a melting zone of a raw material rod for preparing a single crystal optical fiber acquired during the process of preparing the single crystal optical fiber, wherein the target melting zone image includes bright lines formed inside the drawn optical fiber due to the penetration of a preset illumination light, and different target melting zone images are acquired at different times within a specified time period; A prediction module, used for inputting the target melt zone image sequence into a pre-trained monitoring model, so as to determine the target melt zone characteristics according to the target melt zone image sequence through the monitoring model, and estimate the transmission loss value of the single crystal optical fiber drawn within the specified time period according to the target melt zone characteristics, wherein the target melt zone characteristics are used to reflect the uniformity of the bright lines contained in the target melt zone image, the diameter change of the drawn optical fiber, and the shape change of the melt zone; The execution module is used to generate alarm information when it is determined that the transmission loss value meets the preset alarm condition, and upload the alarm information to perform tasks according to the alarm information.

7. The device according to claim 6, characterized in that The monitoring model includes: a convolution module; The prediction module is specifically used to input the target melt zone image sequence into a pre-trained monitoring model, so as to perform convolution processing on each target melt zone image in the target melt zone image sequence through the convolution module, so as to extract the image convolution feature corresponding to the target melt zone image from the target melt zone image; and determine the target melt zone feature according to the image convolution feature corresponding to each target melt zone image.

8. The device according to claim 7, characterized in that The monitoring model includes: a sequence feature extraction module; The prediction module is specifically used to input the image convolution features corresponding to each target melt zone image into the sequence feature extraction module in sequence according to the sequence of acquisition time of each target melt zone image, so that the sequence feature extraction module performs several rounds of processing according to the image convolution features corresponding to each target melt zone image, so as to obtain the sequence features corresponding to the target melt zone image sequence as the target melt zone features; wherein, for each round of processing, the image convolution features of the target melt zone image input into the sequence feature extraction module in the round of processing are determined as the target image convolution features, and the candidate target melt zone features obtained after the previous round of processing are obtained, the target image convolution features are fused with the candidate target melt zone features to obtain the initial candidate target melt zone features in the current round of processing, and the initial candidate target melt zone features are screened by a preset forgetting function to obtain the candidate target melt zone features after the current round of processing, until it is determined that the preset termination condition is met, at which time, the sequence features corresponding to the target melt zone image sequence are obtained as the target melt zone features.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 5 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 5 is implemented.

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