Method and system for detecting fracture source in optical fiber production process

Abstract

The invention discloses a method and system for detecting a fracture source in an optical fiber production process, electronic equipment and a storage medium, and relates to the technical field of image detection. The method comprises the steps of obtaining a visible light image of a to-be-detected optical fiber; extracting a plurality of contour features of the surface of the to-be-detected optical fiber based on the visible light image, and determining a plurality of contour abnormal points of the to-be-detected optical fiber by combining the contour features; receiving temperature data of the to-be-detected optical fiber, and generating a temperature gradient map of the to-be-detected optical fiber based on the temperature data; temperature abnormal points in the temperature gradient map are determined, and the relative distance between each temperature abnormal point and each contour abnormal point is calculated; and screening out a target contour abnormal point and a target temperature abnormal point of which the relative distance is smaller than a distance threshold, and determining a fracture source of the to-be-detected optical fiber based on the positions of the target contour abnormal point and the target temperature abnormal point. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of fracture source detection in the optical fiber production process is achieved.

Description

Technical Field

[0001] The present application relates to the field of image detection technology, and in particular to a method, system, electronic equipment and storage medium for detecting a break source in an optical fiber production process. Background Art

[0002] With the rapid development of optical fiber communication technology, optical fiber, as the core component of modern communication networks, has a quality that is directly related to the performance and reliability of communication systems. During the optical fiber production process, due to the influence of factors such as materials, processes and environment, tiny defects or potential sources of breakage may occur. If these problems are not discovered and resolved in time, they will seriously affect the service life and transmission quality of the optical fiber.

[0003] At present, the existing method for detecting the break source in the optical fiber production process uses a high-resolution camera to capture the image of the optical fiber surface and analyze the abnormal features to determine the potential break source. However, in practical applications, the abnormal surface profile of the optical fiber may not cause the actual break risk of the optical fiber, and the internal defects of the optical fiber may not have obvious contour features on the surface. In this case, relying solely on a single image detection is prone to missed detection or misjudgment, resulting in low accuracy in the detection of break sources in the optical fiber production process. Summary of the invention

[0004] The present application provides a method, system, electronic device and storage medium for detecting a break source in an optical fiber production process, which has the effect of improving the accuracy of detecting a break source in an optical fiber production process.

[0005] In a first aspect, the present application provides a method for detecting a break source in an optical fiber production process, comprising: Acquire a visible light image of the optical fiber to be inspected; Extracting a plurality of contour features of the surface of the optical fiber to be detected based on the visible light image, and determining a plurality of contour abnormal points of the optical fiber to be detected in combination with each of the contour features; Receiving temperature data of the optical fiber to be detected, and generating a temperature gradient map of the optical fiber to be detected based on the temperature data; Determine a plurality of temperature anomaly points in the temperature gradient map, and calculate the relative distance between each of the temperature anomaly points and each of the contour anomaly points; Among the temperature anomaly points and the contour anomaly points, the target contour anomaly points and the target temperature anomaly points corresponding to the relative distances being less than the distance threshold are screened out, and based on the positions of the target contour anomaly points and the target temperature anomaly points, the break source of the optical fiber to be detected is determined.

[0006] In a second aspect of the present application, a system for detecting a break source in an optical fiber production process is provided, the system comprising: An image acquisition module, used to acquire a visible light image of the optical fiber to be detected; An image processing module, used to extract a plurality of contour features of the surface of the optical fiber to be detected based on the visible light image, and determine a plurality of contour abnormal points of the optical fiber to be detected in combination with each of the contour features; A temperature gradient map determining module, configured to receive temperature data of the optical fiber to be detected, and generate a temperature gradient map of the optical fiber to be detected based on the temperature data; A fracture source determination module is used to determine multiple temperature anomaly points in the temperature gradient map, and calculate the relative distance between each of the temperature anomaly points and each of the contour anomaly points; among each of the temperature anomaly points and each of the contour anomaly points, screen out the target contour anomaly points and target temperature anomaly points corresponding to the relative distance being less than a distance threshold, and determine the fracture source of the optical fiber to be detected based on the positions of the target contour anomaly points and the target temperature anomaly points.

[0007] In a third aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the program can implement a method for detecting a break source in an optical fiber production process when loaded and executed by the processor.

[0008] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements a method for detecting a break source in an optical fiber production process.

[0009] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By adopting the above technical solution, combined with the two methods of image contour extraction and temperature distribution generation, the detection results of shape anomalies and temperature anomalies are obtained respectively. Then the distance relationship between the two anomalies is calculated to screen out highly correlated target anomaly points. This multi-angle verification method makes the final determined fracture source position associated with both the defects in the shape structure and the abnormal characteristics of the temperature distribution. Through contour analysis and temperature feature analysis, it avoids analyzing the fracture source from the contour alone while ignoring the fracture inside the optical fiber, and realizes multi-dimensional analysis of the fracture of the optical fiber during the production process, thereby improving the accuracy of fracture source detection during the optical fiber production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flow chart of a method for detecting a break source in an optical fiber production process provided by an embodiment of the present application; Figure 2 It is a structural schematic diagram of a detection system for a break source in an optical fiber production process provided by an embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0011] Description of reference numerals: 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0012] In order to enable technicians in this field to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0013] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0014] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0015] The present application embodiment provides a method for detecting a break source in an optical fiber production process. In one embodiment, please refer to Figure 1 , Figure 1 The present invention provides a flow chart of a method for detecting the breakage source in the optical fiber production process provided by an embodiment of the present invention. The method can be implemented by a computer program, which can be integrated in an application or run as an independent tool application. The method can also be implemented by a single-chip microcomputer or run in a detection system for the breakage source in the optical fiber production process based on a von Neumann system. Specifically, the method can include the following steps: Step 101: Obtain a visible light image of the optical fiber to be detected; extract multiple contour features of the surface of the optical fiber to be detected based on the visible light image, and determine multiple contour abnormal points of the optical fiber to be detected in combination with each contour feature.

[0016] Among them, the visible light image refers to a digital image collected in the visible light band containing the surface feature information of the optical fiber to be detected. In the embodiment of the present application, the visible light image can be understood as a digital image of the surface and near-surface area of ​​the optical fiber to be detected, which is obtained by taking a high-resolution industrial camera installed on the optical fiber production line under controlled light source illumination. The visible light image is used to extract detailed information such as the contour structure, defects, cracks, etc. of the optical fiber surface and near-surface layer, so as to realize the detection of the optical fiber surface contour features and the location of contour abnormal points.

[0017] Profile features refer to a set of parameters that describe the geometric structure and surface properties of the optical fiber surface profile segment. In the embodiment of the present application, the profile features can be understood as a set of parameters such as the curvature, width and grayscale value of each profile segment extracted after image processing. These parameters are used to quantitatively represent the surface profile shape, boundary range and surface reflection characteristics of the corresponding profile segment area.

[0018] A contour abnormal point refers to an area on the optical fiber surface where contour features are abnormal. In the embodiment of the present application, a contour abnormal point can be understood as an area corresponding to a surface contour segment with abnormal surface contour shape, range or smoothness, which is determined by image processing and feature parameter analysis.

[0019] Specifically, in order to accurately identify the fine defects on the surface of the optical fiber, it is first necessary to obtain a visible light image of the surface area of ​​the optical fiber to be detected taken by a high-resolution industrial camera. The visible light image contains rich optical fiber surface feature information and is an important data source for detecting surface contour anomalies. After obtaining the original visible light image, denoising is required to reduce the influence of image noise points and obtain a clear preprocessed image. Then, the edge detection operator is used to extract the overall edge contour information of the preprocessed image, and it is divided into multiple contour segments at equal intervals according to the optical fiber axis. For each contour segment, its curvature parameter, width parameter and gray value parameter are extracted and calculated. These three parameters constitute the contour feature that describes the surface characteristics of the contour segment. Among them, the curvature reflects the degree of bending of the contour, the width represents the contour range, and the gray value represents the surface reflection performance. Finally, the characteristic parameters of the contour segment are compared with the set normal threshold range to determine whether there is an obvious abnormality. If the characteristic parameter deviation is too large, the contour segment can be determined as a contour abnormal point, which is likely to indicate that there are defects such as tiny cracks or undulations in the surface layer, thereby causing a risk of breakage. By combining image processing with parameter calculation, the contour features of the optical fiber surface can be effectively extracted, and the contour abnormal point areas with structural defects can be accurately identified, providing a basis for the subsequent detection and positioning of the break source, thereby improving the effect of optical fiber break source identification.

[0020] Based on the above embodiment, as an optional embodiment, in step 101: extracting multiple contour features of the surface of the optical fiber to be detected based on the visible light image, this step may also include the following steps: Step 201: De-noising the visible light image to obtain a pre-processed image.

[0021] De-noising refers to the process of using digital image processing methods to process images containing noise to suppress or eliminate noise. In the embodiment of the present application, de-noising can be understood as performing various types of filtering, smoothing and other processing on the collected original visible light image to eliminate or reduce the influence of random noise points such as Gaussian noise and salt and pepper noise, and generate a pre-processed image with clearer and more coherent visual effects.

[0022] The pre-processed image refers to an image that has been preliminarily processed. In the embodiment of the present application, the pre-processed image can be understood as an image obtained by performing denoising and other processing on the collected original visible light image.

[0023] Specifically, after obtaining the original visible light image of the optical fiber to be tested, denoising is required to improve the image quality. Since the original image is inevitably mixed with random noises such as Gaussian noise and salt and pepper noise during the shooting and transmission process, these noise points will interfere with the subsequent edge extraction and contour analysis. In order to eliminate the influence of noise, algorithms such as median filtering and Gaussian filtering can be used to smooth the original image, suppress noise points, and obtain a clearer image. Methods such as wavelet transform can also be used to selectively enhance the edge contour contrast of the image. After denoising, a preprocessed image with better coherence and less noise can be obtained. The preprocessed image lays the foundation for the subsequent accurate extraction of contour information by retaining key contour structure features. Compared with the original image, it can better support the identification of key details such as surface microcracks and undulations, avoid introducing misjudgments, and improve the accuracy of analysis.

[0024] Step 202: extracting the edge contour of the preprocessed image; dividing the edge contour into a plurality of contour segments according to the axial direction of the optical fiber to be detected.

[0025] The edge contour refers to the contour line reflecting the boundary of the object in the image. In the embodiment of the present application, the edge contour can be understood as a set of contour lines of the optical fiber surface boundary extracted from the pre-processed visible light image by an edge detection operator.

[0026] The axial direction of the optical fiber to be detected refers to the longitudinal centerline direction of the optical fiber to be detected. In the embodiment of the present application, it can be understood as the axial centerline direction of the optical fiber to be detected along the manufacturing and transmission directions.

[0027] The contour segment refers to the local contour areas obtained by dividing the overall edge contour of the optical fiber along the axial direction. In the embodiment of the present application, the contour segment can be understood as the contour-shaped line segments obtained by dividing the edge line representing the overall contour of the optical fiber surface along the axial direction of the optical fiber at a certain interval and at equal distances.

[0028] Specifically, in order to accurately obtain the key information of the optical fiber surface shape, it is first necessary to extract the edge contour of the pre-processed image after denoising. Here, edge detection operators such as Sobel and Canny can be used to process the pre-processed image, detect the light intensity change edge in the image, and obtain the edge contour information representing the optical fiber surface contour. The edge contour retains the main features of the optical fiber surface shape and structure, and is an important basis for judging the detection of surface defects. However, as a whole, it cannot reflect the axial changes and cannot support the identification of defects in local areas of the surface. Therefore, it is necessary to consider the axial characteristics of the optical fiber, and divide the extracted overall edge contour according to the axial direction of the optical fiber to obtain multiple contour segments. The contour segment can provide contour information of the local area of ​​the optical fiber surface along the axial direction, which is helpful for the subsequent identification of local surface defects. After edge contour extraction and axial segmentation, a contour segment sequence reflecting the structural characteristics of the optical fiber surface contour can be obtained, which lays the foundation for the subsequent accurate judgment of surface quality defects and improves the detection effect.

[0029] Step 203: For each contour segment, based on the pixel points of the contour segment, extract the curvature, width and grayscale value of the contour segment; and use the curvature, width and grayscale value of each contour segment as the contour feature of the optical fiber surface to be detected.

[0030] The curvature refers to a parameter describing the degree of tortuosity of the shape of the contour segment. In the embodiment of the present application, it can be understood as the curvature of the curve calculated based on the pixel point fitting of the contour segment. It reflects whether the surface contour of the contour segment area has abnormal distortion.

[0031] Width refers to a parameter representing the surface range covered by a contour segment. In the embodiment of the present application, it can be understood as the number of pixels contained in the contour segment or the distance between two boundary pixels. It reflects the lateral range size of the surface area corresponding to the contour segment.

[0032] Gray value refers to the image gray characteristic parameter describing the surface quality of the contour segment. In the embodiment of the present application, it can be understood as the gray value distribution or average gray value of all pixels of the contour segment. It reflects the smoothness and reflection degree of the surface area of ​​the contour segment.

[0033] Specifically, after obtaining each contour segment reflecting the optical fiber surface profile information, it is necessary to further extract parameters describing the characteristics of each contour segment as contour features. For each contour segment, all pixel points contained in the contour segment can be statistically analyzed, and the curvature parameter, width parameter and grayscale distribution parameter of the contour segment can be calculated based on these pixel points. Among them, the curvature parameter refers to the tortuosity and bending degree of the contour segment; the width parameter indicates the size of the surface range covered by the contour segment; and the grayscale value parameter reflects the smoothness and reflectivity of the surface area corresponding to the contour segment. These three parameters can quantitatively describe the geometric shape, surface range and optical characteristics of the contour segment. By using the curvature, width and grayscale value of each extracted contour segment as the characteristic parameters of the optical fiber surface profile, a quantitative description of each local area of ​​the surface can be obtained, providing a basis for subsequent judgment of surface defects.

[0034] Based on the above embodiment, as an optional embodiment, in step 101: combining various profile features to determine multiple profile abnormal points of the optical fiber to be detected, this step may also include the following steps: Step 204: For each contour segment, calculate a first difference between the curvature and the curvature threshold, a second difference between the width and the width threshold, and a third difference between the gray value and the gray value threshold.

[0035] Specifically, after obtaining the curvature, width and gray value parameters representing the surface profile characteristics of the optical fiber, it is necessary to further calculate the difference between these parameters and the corresponding threshold value as a basis for judging whether the profile is normal. Because when there are defects on the optical fiber surface, the profile curvature, width and gray value of its local area will be obviously abnormal. Therefore, for each contour segment, the difference between its curvature value and the normal curvature threshold, the difference between the width value and the normal width threshold, and the difference between the gray value and the normal gray threshold are calculated. When the difference between these three characteristic parameters of the contour segment is greater than the preset threshold, it means that there are surface defects in the local area corresponding to the contour segment, which may be abnormal conditions such as cracks, undulating deformation or nozzle residue. By comparing the difference between the contour feature parameters and the threshold value, it can be judged whether there are local defects on the optical fiber surface, and the accurate evaluation and defect identification of the optical fiber surface quality can be achieved. Therefore, by calculating the difference between the contour feature parameters and the corresponding threshold value, it can be quantitatively judged whether the contour features such as curvature, width and gray scale of the optical fiber surface are normal, thereby effectively identifying surface defects and realizing automatic detection of optical fiber quality.

[0036] Step 205: performing weighted summation on the first difference, the second difference and the third difference to obtain an abnormality score of the contour segment; if the abnormality score is greater than or equal to the standard score, the area corresponding to the contour segment is regarded as a contour abnormal point.

[0037] Among them, the anomaly score refers to a quantitative indicator indicating the degree of defects in the contour segment. In the embodiment of the present application, the anomaly score can be understood as a comprehensive anomaly score of the contour segment obtained by weighted summation of the curvature difference, width difference and grayscale difference. The anomaly score can quantitatively evaluate the severity of defects in the optical fiber surface area corresponding to the contour segment by comparing the difference between the curvature, width and grayscale of the contour segment and the normal threshold.

[0038] The standard score refers to the threshold score for judging whether the profile segment is abnormal. In the embodiment of the present application, the standard score can be understood as the threshold score determined in advance based on the normal condition of the optical fiber surface. The standard score provides the parameter range and value of the normal optical fiber surface profile, which is an empirical value and represents the normal level when the optical fiber surface is defect-free.

[0039] Specifically, in order to accurately identify the contour abnormal area with defects on the optical fiber surface, after obtaining the curvature difference, width difference and grayscale difference, a comprehensive judgment is required. Here, the weighted sum of the curvature difference, width difference and grayscale difference of the contour segment can be calculated by the weighted summation method as the abnormal score of the contour segment. Giving different weights to the three characteristic parameters can reasonably describe their importance. Then, the abnormal score is compared with the standard score corresponding to the normal optical fiber surface determined in advance. If the abnormal score is greater than or equal to the standard score, it can be judged that the optical fiber surface area corresponding to the contour segment has defects and is determined as a contour abnormal point. By combining multiple characteristic parameters and comparing them with the standard threshold, it is possible to accurately identify whether there are defects in the local area of ​​the optical fiber surface and find the contour abnormal point. Finally, all contour abnormal areas are output to complete the detection and positioning of optical fiber surface defects. Through this process, the multi-features of contour curvature, width and grayscale can be effectively utilized to intelligently identify the defective areas of optical fiber surface quality and realize automatic detection and evaluation.

[0040] Based on the above embodiment, as an optional embodiment, in step 205: performing weighted summation on the first difference, the second difference and the third difference to obtain the abnormality score of the contour segment, this step may also include the following steps: Step 215: Obtain the historical number of unqualified products of the optical fiber to be tested, where the historical number of unqualified products includes the number of unqualified products in curvature, the number of unqualified products in width, and the number of unqualified products in gray value.

[0041] Among them, the number of historical defective products refers to the statistical number of quality defective products that appeared in the past production process of the product line. In the embodiment of the present application, the number of historical defective products can be understood as the statistical data of the number of defective products in the quality inspection results of the optical fiber products produced by the product line in the past extracted from the quality traceability database. The number of historical defective products can be subdivided into three categories: the number of curvature defectives, the number of width defectives, and the number of grayscale defectives, which represent the statistics of historical failures of three defect types.

[0042] Specifically, in order to be able to intelligently optimize the current quality inspection model based on the historical quality data of optical fiber products, it is necessary to first obtain the number of defective products that occurred in the historical production process of the product line. Extract the optical fiber quality inspection database of the product line's past production from the quality traceability system, statistically analyze the historical inspection results, obtain the number of defective products in the product line due to curvature, width and grayscale abnormalities, and form three statistical parameters: the number of curvature defectives, the number of width defectives and the number of grayscale defectives. Obtaining the statistical data of these historical defective product numbers can analyze the common types of quality problems in the product line and the frequency of occurrence of different quality problems. This can provide data support for the subsequent optimization of the quality inspection model, so that the inspection model can be optimized for the common defects of the product. Therefore, collecting the historical quality inspection statistical data of the product line can provide a basis for the customization of the current inspection model, realize the construction of a targeted inspection model, and improve the accuracy of optical fiber quality inspection.

[0043] Step 225: The ratio of the number of unqualified curvature products to the number of historical unqualified products is used as the weight corresponding to the first difference; the ratio of the number of unqualified width products to the number of historical unqualified products is used as the weight corresponding to the second difference; the ratio of the number of unqualified gray value products to the number of historical unqualified products is used as the weight corresponding to the third difference.

[0044] Specifically, in order to optimize the quality inspection model according to the characteristics of the current product line, it is necessary to optimize the weights of the inspection parameters based on historical data.

[0045] Specifically, first calculate the ratio of the number of unqualified curvature products to the total number of unqualified products in history as the weight of the curvature difference, calculate the ratio of the number of unqualified width products to the total number of unqualified products in history as the weight of the width difference, and calculate the ratio of the number of unqualified grayscale products to the total number of unqualified grayscale products in history as the weight of the grayscale difference. In this way, according to the distribution of unqualified quality indicators in history, the weight coefficients of each parameter in the current detection model can be reasonably set, so that when comprehensively judging the quality, the weights of different parameters are consistent with the actual quality status of the current product line.

[0046] Step 235: Based on the weights corresponding to the first difference, the second difference and the third difference, weighted sum is performed on the first difference, the second difference and the third difference to obtain an abnormality score of the contour segment.

[0047] Specifically, after setting the detection parameter weights based on historical statistics, these weights need to be applied to calculate the anomaly score of the contour segment. The weights corresponding to the determined curvature difference, width difference, and grayscale difference are used to perform a weighted sum to obtain the anomaly score of the contour segment. In this way, the difference can be weightedly combined according to the importance of each parameter in the quality inspection of the current product line. The detection parameter weights based on historical statistics can make the anomaly score more consistent with the actual quality of the current product line.

[0048] Step 102: Receive temperature data of the optical fiber to be tested, and generate a temperature gradient map of the optical fiber to be tested based on the temperature data.

[0049] The temperature data refers to a set of data describing the temperature of each position of the optical fiber during the manufacturing and transmission process. In the embodiment of the present application, the temperature data can be understood as a set of discrete data representing the temperature values ​​of each detection point along the optical fiber axis, which is obtained by collecting through a distributed optical fiber temperature sensing system.

[0050] The temperature gradient map refers to a continuous temperature field image that reflects the temperature distribution on the optical fiber surface. In the embodiment of the present application, the temperature gradient map can be understood as a two-dimensional image that describes the temperature gradient change on the optical fiber surface, generated based on the collected discrete temperature data through image processing and temperature field reconstruction methods.

[0051] Specifically, in order to realize the thermal anomaly identification of optical fiber surface defects, the first step is to obtain the temperature data during the optical fiber manufacturing and transmission process. The temperature values ​​at different positions are collected to obtain a set of discrete temperature data describing the temperature distribution of the optical fiber. Considering that the temperature field on the optical fiber surface is continuously distributed, only discrete temperature data points cannot reflect the overall distribution of the optical fiber surface temperature. Therefore, it is necessary to use image reconstruction methods such as cubic spline interpolation and wavelet analysis based on these discrete temperature sample data to generate a temperature gradient map reflecting the continuous temperature gradient distribution on the optical fiber surface. As a two-dimensional temperature field distribution image, the temperature gradient map can intuitively represent the temperature change on the optical fiber surface in the form of color mapping, clearly reflect the hot spots and thermal anomaly areas on the optical fiber surface, and realize the visual expression of the optical fiber thermal distribution. Therefore, receiving discrete temperature data and generating a continuous temperature gradient map can realize the effective detection of the temperature distribution on the optical fiber surface, provide image support for the subsequent identification of temperature anomaly areas, and improve the early warning effect of optical fiber breakage hazards.

[0052] Based on the above embodiment, as an optional embodiment, in step 102: generating a temperature gradient map of the optical fiber to be tested based on the temperature data, this step may also include the following steps: Step 301: Determine the optical fiber temperatures corresponding to a plurality of temperature sampling points in the temperature data; and calculate the temperature difference between adjacent temperature sampling points based on the temperatures of the optical fibers.

[0053] Specifically, in order to analyze the temperature changes of the optical fiber at different positions, it is necessary to calculate the temperature difference based on the collected temperature data. According to the temperature data obtained by the distributed optical fiber temperature sensing system, the corresponding optical fiber temperature value at each temperature sampling point on the optical fiber axis is determined. Then, the temperature difference between adjacent temperature sampling points is calculated to reflect the temperature change value of the optical fiber between adjacent axial positions. By calculating the temperature difference between the axial positions, the temperature gradient change of the optical fiber along the axial direction can be analyzed, and the thermal change points where the temperature in the local area of ​​the optical fiber rises or falls rapidly can be reflected. Finally, according to the distribution of the temperature difference, the thermal anomalies on the surface of the optical fiber can be found, and the early warning detection of potential optical fiber breakage hazards can be realized.

[0054] Step 302: Determine a plurality of temperature gradients according to each temperature difference and the distance between adjacent temperature sampling points corresponding to each temperature difference.

[0055] The temperature gradient refers to a parameter that describes the degree of temperature change. In the embodiment of the present application, the temperature gradient can be understood as the ratio of the temperature difference between adjacent temperature sampling points on the optical fiber axis to the distance between them. The temperature gradient reflects the temperature change value per unit distance, indicating the severity and change trend of the temperature change in the local area of ​​the optical fiber.

[0056] Specifically, in order to better analyze the temperature change trend between different positions of the optical fiber, it is necessary to calculate the temperature gradient based on the temperature difference. Based on the temperature difference between a set of axial temperature sampling points that have been obtained, and considering the spatial distance of the temperature sampling points corresponding to each temperature difference, the corresponding temperature gradient is calculated by dividing the difference by the distance. In this way, the temperature gradient can reflect the rate of change of temperature within a unit distance, and more accurately describe the severity of the temperature change between different axial positions of the optical fiber. After obtaining the temperature gradient distribution at each axial position of the optical fiber, the temperature change trend of the local area on the surface of the optical fiber can be clarified, and the thermal change points with abnormal temperature gradients can be found. Therefore, calculating the temperature gradient representing the severity of the temperature change helps to more accurately analyze the thermal distribution of the optical fiber and realize the detection and early warning of defects on the optical fiber surface.

[0057] Step 303: Map each temperature gradient onto a preset two-dimensional plane to generate a temperature gradient map of the optical fiber to be tested.

[0058] Specifically, in order to reflect the thermal distribution on the optical fiber surface, it is necessary to map the calculated temperature gradient to generate a temperature gradient map. The temperature gradient values ​​calculated at different axial positions of the optical fiber are mapped to a pre-set two-dimensional coordinate plane, where the horizontal axis represents the axial position of the optical fiber and the vertical axis represents the temperature gradient value. Different colors are used to correspond to different temperature gradient values, and the distribution change curve of the axial temperature gradient of the optical fiber is drawn. In this way, a continuous two-dimensional temperature gradient distribution map can be formed, which clearly reflects the temperature changes in different areas of the optical fiber surface. Through the visual expression of the temperature gradient map, the optical fiber thermal distribution can be intuitively observed, local thermal anomalies can be found, and rapid warning and positioning of optical fiber surface defects and breakage risks can be achieved.

[0059] Step 103: determining a plurality of temperature anomaly points in the temperature gradient map, and calculating the relative distance between each temperature anomaly point and each contour anomaly point.

[0060] Among them, the temperature abnormal point refers to the point in the temperature gradient diagram that reflects the abnormal temperature change. In the embodiment of the present application, the temperature abnormal point can be understood as the point where the temperature gradient value suddenly changes in a local area or is significantly higher than the surrounding point. The temperature abnormal point reflects the abnormal thermal distribution of the local area of ​​the optical fiber surface where the temperature rises or drops rapidly.

[0061] Specifically, in order to realize the correlation analysis between temperature distribution and surface shape, it is necessary to determine the temperature anomaly points in the temperature gradient map and calculate the distance to the surface contour anomaly points. First, in the temperature gradient map, multiple temperature anomaly points with thermal anomalies are determined based on the abnormal changes in the temperature gradient value. Then, the contour anomaly points that have been obtained are selected as corresponding references, and the relative distance between each temperature anomaly point and each contour anomaly point in the axial direction of the optical fiber is calculated. In this way, a set of distance matrices between temperature anomaly points and contour anomaly points can be obtained. By calculating the distance relationship between temperature anomaly points and surface contour anomaly points, correlation analysis can be performed to determine whether the temperature anomaly corresponds to a surface shape defect, thereby realizing cross-validation of the detection results.

[0062] Based on the above embodiment, as an optional embodiment, in step 103: ... This step may also include the following steps: Step 401: determine whether there is an image area in the temperature gradient map where the temperature gradient is greater than or equal to the gradient threshold; if there is an image area where the temperature gradient is greater than or equal to the gradient threshold, take the position corresponding to the image area as a temperature anomaly point.

[0063] Specifically, in order to accurately locate the temperature anomaly area in the temperature gradient distribution map, a threshold judgment is required. First, a temperature gradient threshold is preset, that is, to determine whether there is an image area with a temperature gradient greater than or equal to the gradient threshold. Then, the temperature gradient values ​​of each position in the temperature gradient map are judged in turn. If it is found that the temperature gradient of a certain position exceeds the preset gradient threshold, the image area is marked as having a temperature anomaly. Finally, all image areas that are judged to have a temperature gradient exceeding the threshold are determined as temperature anomaly points. In this way, by comparing with the threshold, normal temperature change areas can be effectively filtered out, and points with thermal anomalies on the optical fiber surface can be accurately identified as temperature anomaly points.

[0064] Step 104: Filter out target profile abnormal points and target temperature abnormal points whose relative distances are less than the distance threshold from each temperature abnormal point and each profile abnormal point, and determine the break source of the optical fiber to be detected based on the positions of the target profile abnormal points and the target temperature abnormal points.

[0065] Among them, the fracture source refers to the position on the surface of the optical fiber that is prone to fracture during the production process. In the embodiment of the present application, the fracture source can be understood as the point where there are defects on the optical fiber surface and abnormal temperature distribution, which is determined by correlation analysis of temperature abnormal points and contour abnormal points. The fracture source reflects the area where the double abnormalities of optical fiber surface quality and thermal distribution converge. These areas are very prone to crack propagation and cause optical fiber breakage due to structural defects and stress abnormalities.

[0066] Specifically, in order to accurately locate the hidden dangers of optical fiber breakage, it is necessary to determine the final breakage source through the correlation analysis of temperature anomaly points and contour anomaly points. First, the relative distance between the temperature anomaly points and the contour anomaly points is calculated, and then a distance threshold is set. Among all the abnormal point combinations, the target combination with a relative distance less than the distance threshold is screened out, which indicates that there is a high correlation between the two anomalies. Finally, among these highly correlated target combinations, the closest pair of temperature anomaly points and contour anomaly points are selected. The axial position of the pair of abnormal points is determined as the breakage source. In this way, through the correlation analysis and screening of the two detection results, the accuracy of the breakage source positioning can be improved, and the key area most likely to cause a break can be found. Determining the breakage source can provide early warning of the hidden dangers of optical fiber breakage, realize early detection of minor defects in optical fiber, and avoid breakage failures during use.

[0067] Based on the above embodiment, as an optional embodiment, in step 104: determining the break source of the optical fiber to be detected based on the positions of the target profile abnormal point and the target temperature abnormal point, this step may also include the following steps: Step 501: Based on the positions of the target profile abnormal point and the target temperature abnormal point, determine the overlapping area of ​​the target profile abnormal point and the target temperature abnormal point on the optical fiber to be detected; and use the overlapping area as the fracture source of the optical fiber to be detected.

[0068] The overlapping area refers to the area where the contour anomaly point and the temperature anomaly point overlap in the axial direction of the optical fiber. In the embodiment of the present application, the overlapping area can be understood as the part where the axial coordinate ranges corresponding to the contour anomaly point and the temperature anomaly point intersect. The overlapping area is associated with both shape defects and thermal anomalies, reflecting the characteristics of dual anomalies in structure and temperature in the local area of ​​the optical fiber surface.

[0069] Specifically, in order to improve the accuracy of the fracture risk positioning, it is necessary to determine the overlapping area of ​​the target abnormal points, and first determine the position coordinates of the target contour abnormal points and the target temperature abnormal points in the axial direction of the optical fiber. Then determine whether there is an overlapping area in the axial direction between the two sets of abnormal points, and then determine the overlapping area where both the contour abnormality and the temperature abnormality are displayed as the fracture source of the optical fiber. Because the overlapping area reflects both the shape structure defects and the thermal distribution abnormality, the fracture risk is the greatest. By accurately locating the fracture source through the overlapping area, the surface shape and temperature factors are comprehensively considered, and the early warning of the risk of optical fiber fracture can be reliably achieved.

[0070] Reference Figure 2 , a detection system for a breakage source in an optical fiber production process provided by an embodiment of the present application, the system comprises: an image acquisition module, an image processing module, a temperature gradient map determination module, and a breakage source determination module, wherein: An image acquisition module, used to acquire a visible light image of the optical fiber to be detected; An image processing module is used to extract multiple contour features of the surface of the optical fiber to be detected based on the visible light image, and determine multiple contour abnormal points of the optical fiber to be detected in combination with each contour feature; A temperature gradient map determination module is used to receive temperature data of the optical fiber to be detected and generate a temperature gradient map of the optical fiber to be detected based on the temperature data; The fracture source determination module is used to determine multiple temperature anomaly points in the temperature gradient map and calculate the relative distance between each temperature anomaly point and each contour anomaly point; among each temperature anomaly point and each contour anomaly point, the target contour anomaly point and the target temperature anomaly point whose relative distance is less than the distance threshold are screened out, and based on the positions of the target contour anomaly point and the target temperature anomaly point, the fracture source of the optical fiber to be detected is determined.

[0071] On the basis of the above embodiment, the image processing module is also used to perform denoising on the visible light image to obtain a preprocessed image; extract the edge contour of the preprocessed image; divide the edge contour into multiple contour segments according to the axial direction of the optical fiber to be detected; for each contour segment, based on the pixel points of the contour segment, extract the curvature, width and grayscale value of the contour segment; use the curvature, width and grayscale value of each contour segment as the contour feature of the surface of the optical fiber to be detected.

[0072] Based on the above embodiment, the image processing module is also used to calculate, for each contour segment, a first difference between the curvature and the curvature threshold, a second difference between the width and the width threshold, and a third difference between the grayscale value and the grayscale value threshold; perform weighted summation on the first difference, the second difference, and the third difference to obtain an abnormality score for the contour segment; if the abnormality score is greater than or equal to the standard score, the area corresponding to the contour segment is used as a contour abnormality point.

[0073] On the basis of the above embodiment, the image processing module is also used to obtain the historical number of defective products of the optical fiber to be inspected, the historical number of defective products includes the number of curvature defective products, the number of width defective products and the number of gray value defective products; the ratio of the number of curvature defective products to the historical number of defective products is used as the weight corresponding to the first difference; the ratio of the number of width defective products to the historical number of defective products is used as the weight corresponding to the second difference; the ratio of the number of gray value defective products to the historical number of defective products is used as the weight corresponding to the third difference; based on the weights corresponding to the first difference, the second difference and the third difference, the first difference, the second difference and the third difference are weightedly summed to obtain the abnormality score of the contour segment.

[0074] On the basis of the above-mentioned embodiment, the temperature gradient map determination module is also used to determine the optical fiber temperature corresponding to multiple temperature sampling points in the temperature data; based on each optical fiber temperature, calculate the temperature difference between adjacent temperature sampling points; determine multiple temperature gradients according to each temperature difference and the distance between adjacent temperature sampling points corresponding to each temperature difference; map each temperature gradient on a preset two-dimensional plane to generate a temperature gradient map of the optical fiber to be tested.

[0075] Based on the above embodiment, the fracture source determination module is further used to determine whether there is an image area in the temperature gradient map where the temperature gradient is greater than or equal to the gradient threshold; if there is an image area where the temperature gradient is greater than or equal to the gradient threshold, the position corresponding to the image area is taken as the temperature anomaly point.

[0076] Based on the above embodiment, the fracture source determination module is also used to determine the overlapping area of ​​the target contour abnormal point and the target temperature abnormal point on the optical fiber to be detected based on the positions of the target contour abnormal point and the target temperature abnormal point; and use the overlapping area as the fracture source of the optical fiber to be detected.

[0077] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0078] The present application also discloses an electronic device. Figure 3 , Figure 3 The electronic device 300 may include: at least one processor 301 , at least one network interface 304 , a user interface 303 , a memory 305 , and at least one communication bus 302 .

[0079] The communication bus 302 is used to realize the connection and communication between these components.

[0080] The user interface 303 may include a display interface and a camera interface. Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0081] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0082] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface diagrams and applications, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0083] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally also be at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 The memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a method for detecting a break source in an optical fiber production process.

[0084] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call the application program stored in the memory 305 for a method for detecting the source of breakage in the optical fiber production process. When executed by one or more processors 301, the electronic device 300 executes one or more methods in the above-mentioned embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simple description, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described order of actions, because according to the present application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0085] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0087] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0088] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0089] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0090] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present disclosure.

[0091] This application is intended to cover any variation, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art not recorded in the present disclosure. The description and examples are to be regarded as exemplary only.

Claims

1. A method for detecting a break source in an optical fiber production process, characterized in that: include: Acquire a visible light image of the optical fiber to be inspected; Extracting a plurality of contour features of the surface of the optical fiber to be detected based on the visible light image, and determining a plurality of contour abnormal points of the optical fiber to be detected in combination with each of the contour features; Receiving temperature data of the optical fiber to be detected, and generating a temperature gradient map of the optical fiber to be detected based on the temperature data; Determine a plurality of temperature anomaly points in the temperature gradient map, and calculate the relative distance between each of the temperature anomaly points and each of the contour anomaly points; Among the temperature anomaly points and the contour anomaly points, the target contour anomaly points and the target temperature anomaly points corresponding to the relative distances being less than the distance threshold are screened out, and based on the positions of the target contour anomaly points and the target temperature anomaly points, the break source of the optical fiber to be detected is determined.

2. The method for detecting breakage sources in the optical fiber production process according to claim 1, characterized in that: The step of extracting a plurality of contour features of the surface of the optical fiber to be detected based on the visible light image comprises: Performing denoising processing on the visible light image to obtain a preprocessed image; Extracting edge contours of the preprocessed image; Dividing the edge contour into a plurality of contour segments according to the axial direction of the optical fiber to be detected; For each of the contour segments, based on the pixel points of the contour segment, extract the curvature, width and grayscale value of the contour segment; The curvature, width and gray value of each contour segment are used as contour features of the surface of the optical fiber to be detected.

3. The method for detecting breakage sources in the optical fiber production process according to claim 2, characterized in that: The step of combining the profile features to determine a plurality of profile abnormal points of the optical fiber to be detected comprises: For each of the contour segments, calculating a first difference between the curvature and a curvature threshold, a second difference between the width and a width threshold, and a third difference between the grayscale value and a grayscale value threshold; Performing weighted summation on the first difference, the second difference, and the third difference to obtain an abnormality score of the contour segment; If the abnormal score is greater than or equal to the standard score, the area corresponding to the contour segment is used as the contour abnormal point.

4. The method for detecting breakage sources in the optical fiber production process according to claim 3, characterized in that: The step of performing weighted summation on the first difference, the second difference, and the third difference to obtain an abnormality score of the contour segment includes: Obtaining the number of historical unqualified products of the optical fiber to be tested, wherein the number of historical unqualified products includes the number of unqualified products in curvature, the number of unqualified products in width, and the number of unqualified products in gray value; The ratio of the number of curvature failures to the number of historical failures is used as the weight corresponding to the first difference; The ratio of the number of defective width products to the number of defective products in history is used as the weight corresponding to the second difference; The ratio of the number of unqualified gray values ​​to the number of historical unqualified products is used as the weight corresponding to the third difference; Based on the weights corresponding to the first difference, the second difference and the third difference, a weighted sum is performed on the first difference, the second difference and the third difference to obtain an abnormality score of the contour segment.

5. The method for detecting breakage sources in the optical fiber production process according to claim 1, characterized in that: The step of generating a temperature gradient diagram of the optical fiber to be detected based on the temperature data comprises: Determine the optical fiber temperature corresponding to a plurality of temperature sampling points in the temperature data; Based on the temperature of each optical fiber, calculating the temperature difference between adjacent temperature sampling points; Determining a plurality of temperature gradients according to each of the temperature differences and the distances between adjacent temperature sampling points corresponding to each of the temperature differences; Each of the temperature gradients is mapped onto a preset two-dimensional plane to generate a temperature gradient map of the optical fiber to be detected.

6. The method for detecting breakage sources in the optical fiber production process according to claim 5, characterized in that: The determining of a plurality of temperature anomaly points in the temperature gradient map comprises: Determine whether there is an image area in the temperature gradient map where the temperature gradient is greater than or equal to the gradient threshold; If there is an image region where the temperature gradient is greater than or equal to the gradient threshold, the position corresponding to the image region is taken as the temperature anomaly point.

7. The method for detecting breakage sources in the optical fiber production process according to claim 1, characterized in that: The step of determining the break source of the optical fiber to be detected based on the positions of the target profile abnormal point and the target temperature abnormal point comprises: Based on the positions of the target profile abnormal point and the target temperature abnormal point, determining an overlapping area between the target profile abnormal point and the target temperature abnormal point on the optical fiber to be detected; The overlapping area is used as the break source of the optical fiber to be detected.

8. A system for detecting breakage sources during optical fiber production, characterized in that: The system comprises: An image acquisition module, used to acquire a visible light image of the optical fiber to be detected; An image processing module, used to extract a plurality of contour features of the surface of the optical fiber to be detected based on the visible light image, and determine a plurality of contour abnormal points of the optical fiber to be detected in combination with each of the contour features; A temperature gradient map determining module, configured to receive temperature data of the optical fiber to be detected, and generate a temperature gradient map of the optical fiber to be detected based on the temperature data; A fracture source determination module is used to determine multiple temperature anomaly points in the temperature gradient map, and calculate the relative distance between each of the temperature anomaly points and each of the contour anomaly points; among each of the temperature anomaly points and each of the contour anomaly points, screen out the target contour anomaly points and target temperature anomaly points corresponding to the relative distance being less than a distance threshold, and determine the fracture source of the optical fiber to be detected based on the positions of the target contour anomaly points and the target temperature anomaly points.

9. An electronic device, characterized in that: The device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method for detecting a break source in an optical fiber production process as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method for detecting a break source in an optical fiber production process according to any one of claims 1 to 7 is executed.

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