Yarn tension detection method, system and equipment and storage medium

Abstract

The invention relates to a yarn tension detection method, system and device and a storage medium, and belongs to the technical field of yarn detection.The method comprises the steps that a preset visual detection module is controlled to collect a yarn image with each yarn in a preset detection area; the preset tension detector is controlled to move, and each yarn in the preset detection area is detected to obtain a yarn tension value; carrying out fusion processing on the yarn tension value and the yarn image, analyzing to obtain a quality inspection result of each yarn, and outputting the yarn image after fusion processing and the corresponding quality inspection result so as to enable a detector to know a one-to-one correspondence relationship between the yarn and the quality inspection result; wherein the quality inspection result at least comprises a judgment result about whether the tension value is abnormal or not. The yarn batch detection device has the advantages that in the yarn batch detection process, one-to-one correspondence of yarn and detection results is achieved, and the screening efficiency and accuracy of defective products are improved.

Description

Technical Field

[0001] The present application relates to the technical field of yarn detection, and in particular to a yarn tension detection method, system, device and storage medium. Background Art

[0002] In the textile industry, yarn tension control is a key factor in ensuring product quality. With advances in automation technology, traditional yarn tension detection methods have been gradually replaced by electronic sensors and computer vision. The application of these technologies not only improves detection accuracy but also significantly enhances production efficiency.

[0003] However, when using the above-mentioned tension tester to test batches of densely arranged yarns, it is difficult to accurately match the tension value of each yarn with the yarn one by one. In particular, when the yarn tension value does not meet the standard, the tester needs to manually find the yarn with the substandard tension value. This method is not only inefficient but also prone to errors, and therefore needs to be improved. Summary of the Invention

[0004] In order to achieve a one-to-one correspondence between yarns and test results during batch yarn testing and improve screening efficiency and accuracy, the present application provides a yarn tension detection method, system, device and storage medium.

[0005] In a first aspect, the present application provides a yarn tension detection method, comprising: Controlling a preset visual inspection module to collect a yarn image of each yarn within a preset inspection area; Controlling the preset tension detector to move and respectively detect each yarn in the preset detection area to obtain the yarn tension value; The yarn tension value is fused with the yarn image, and the quality inspection result of each yarn is obtained by analysis. The fused yarn image and its corresponding quality inspection result are output so that the inspector can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes a determination result of whether the tension value is abnormal.

[0006] By adopting the above technical solution, in the process of using a tension tester to test multiple yarns, a visual inspection module is added to obtain yarn images, so as to more intuitively help the testers know the yarns and their corresponding tension quality inspection results, and achieve a one-to-one correspondence between the yarns and the quality inspection results, so as to help quickly locate the position of the yarn with abnormal tension values.

[0007] Optionally, the method further includes: Based on the yarn images captured by the visual inspection module, each yarn characteristic is analyzed, and a detection point is selected for each yarn, wherein the detection point is a portion of the yarn that is in contact with the tension detector so that the tension detector can detect and obtain a tension value; wherein the yarn characteristics include at least the physical structure of the yarn; A moving path is planned and generated according to all the detection points of the yarn, and the tension detector is controlled to move based on the moving path. Whenever the yarn moves to any detection point, the tension detector is controlled to obtain a tension value.

[0008] By adopting the above technical solution, the physical structure of the yarn (such as yarn thickness, color, and texture) is analyzed, and based on the yarn characteristics, detection points (such as joints, knots, etc.) are selected for each yarn to ensure that the aforementioned positions are positions that can reflect the overall quality status of the yarn, and ultimately achieve efficient detection of the yarn by the tension tester.

[0009] Optionally, the analyzing the characteristics of each yarn based on the yarn image collected by the visual inspection module and selecting a detection point for each yarn includes: During the movement of the tension detector, the dynamic behavior of each yarn is analyzed based on the yarn image collected in real time by the preset visual detection module; wherein the dynamic behavior at least includes the vibration behavior of the yarn; Obtain the vibration frequency of each yarn and perform time series analysis to predict the estimated vibration frequency at different locations of each yarn; Analyze the characteristics of each yarn and, in combination with the estimated vibration frequency of each yarn, comprehensively determine the detection point of each yarn; and satisfy: the vibration effect of the yarn at the detection point meets the preset requirements; The method further comprises: The received yarn tension values ​​and the vibration frequencies of the corresponding detection points are subjected to vibration compensation processing on the yarn tension values ​​corresponding to each detection point using a preset vibration compensation algorithm.

[0010] By adopting the above technical solution, during the detection process, the yarn is easily affected by the movement of the tension detector or other factors in the environment and vibrates, and the yarn vibration will affect the accuracy of the yarn tension detection. For this reason, when selecting the detection points, this application not only considers the yarn characteristics, but also comprehensively considers the factor of yarn vibration to select the yarn parts that are affected by vibration and meet the preset requirements (such as being less affected by vibration) as the detection points, and then further uses the vibration compensation algorithm to further compensate the yarn tension value for vibration to improve the error caused by vibration.

[0011] Optionally, obtaining the vibration frequency of each yarn and performing time series analysis to predict the estimated vibration frequency at different locations of each yarn includes: Obtaining the yarn vibration frequency at different parts of each yarn when the tension detector is located at different positions; The correlation between the vibration frequency of each yarn and the distance between the tension detector and each yarn is analyzed, and the estimated vibration frequency of different parts of each yarn is predicted based on the correlation. The estimated vibration frequency refers to the vibration frequency of different parts of the yarn to which the estimated vibration frequency belongs when the tension detector moves to contact the yarn to which the estimated vibration frequency belongs.

[0012] By adopting the above-mentioned technical solution, the vibration frequency of the yarn is likely to change with the different distances from the tension detector. For this reason, this application proposes to predict the vibration frequency of the corresponding yarn when the tension detector moves to contact the yarn based on this distance relationship, thereby improving the accuracy of determining the vibration frequency of the yarn and the accuracy of selecting the detection point.

[0013] Optionally, obtaining the yarn vibration frequency at different parts of each yarn when the tension detector is located at different positions includes: Based on the pre-built vibration simulation model, the tension detector is used as the vibration source, and based on the yarn characteristics, the yarn vibration frequency of different parts of each yarn is obtained when the tension detector is located in different positions.

[0014] By adopting the above technical solution, this application uses a pre-built vibration simulation model to simulate the vibration of the yarn based on the characteristics of each yarn (such as the physical structural characteristics of the yarn) with the tension detector as the vibration source, thereby improving the calculation efficiency and accuracy of the yarn vibration frequency.

[0015] Optionally, the analyzing each yarn characteristic includes: Based on the yarn image and a preset yarn material library, yarn characteristics corresponding to the yarn in the yarn image are matched and obtained from the yarn material library; wherein the yarn material library pre-stores a plurality of different types of yarns and their corresponding images and physical structure characteristics.

[0016] Optionally, the method further includes: Whenever a quality inspection result is determined, determining an abnormal quality inspection result and a corresponding abnormal yarn, and determining an abnormal position of the abnormal yarn; Whenever a detection point is selected for each yarn, based on the yarn characteristics of each yarn, the abnormal position of the yarn with the same yarn characteristics in the historical period is determined, and according to the determined abnormal position, a detection point is added to the corresponding yarn.

[0017] By adopting the above technical solution, locations where quality inspection anomalies are prone to occur in historical periods are used as detection points. That is, each yarn can correspond to multiple detection points to achieve comprehensive and accurate detection of the corresponding yarn quality.

[0018] In a second aspect, the present application provides a yarn tension detection system, comprising: An image acquisition module, used for controlling a preset visual inspection module to acquire a yarn image of each yarn within a preset inspection area; The tension detection module is used to control the movement of the preset tension detector and detect each yarn in the preset detection area to obtain the yarn tension value; A quality inspection and analysis module is used to fuse the yarn tension value with the yarn image, analyze and obtain the quality inspection result of each yarn, and output the fused yarn image and its corresponding quality inspection result so that the inspector can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes the determination result of whether the tension value is abnormal.

[0019] In a third aspect, the present application provides a yarn tension detection device, comprising a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute any of the methods described in the first aspect.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, characterized in that it stores a computer program that can be loaded by a processor and execute any method described in the first aspect.

[0021] In summary, this application includes at least one of the following beneficial technical effects: 1. This application utilizes a tension tester to continuously and synchronously test multiple yarns, and uses a visual inspection module to obtain yarn images. This allows inspectors to more intuitively identify the yarns and their corresponding tension quality inspection results, achieving a one-to-one correspondence between yarns and quality inspection results, thereby helping inspectors quickly locate yarns with abnormal tension values. 2. Furthermore, by analyzing the physical structure of the yarn (such as yarn thickness, color, and texture), and based on the yarn characteristics, the detection points are selected for each yarn to ensure that the aforementioned positions are positions that can reflect the overall quality of the yarn, and ultimately achieve efficient and comprehensive detection of the yarn by the tension tester. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a flow chart of a yarn tension detection method disclosed in an embodiment of the present application.

[0024] Figure 2 This is a structural block diagram of a yarn tension detection system disclosed in an embodiment of the present application.

[0025] Description of reference numerals: 201, image acquisition module; 202, tension detection module; 203, quality inspection and analysis module. DETAILED DESCRIPTION

[0026] The following is combined with Figure 1-2 This application is described in further detail.

[0027] The embodiment of the present application discloses a yarn tension detection method, the execution subject of which is a yarn tension detection system. The yarn tension detection system is communicatively connected to a tension detector and a visual detection module. The visual detection module can be specifically a camera. The visual detection module is used to capture yarn images, and the tension detector is used to detect the yarn tension value. Figure 1 The specific process of yarn tension detection by visual inspection module and tension detector in conjunction with yarn tension detection system is specifically explained.

[0028] S101, controlling a preset visual inspection module to collect a yarn image of each yarn within a preset inspection area.

[0029] In practice, multiple yarns to be measured are pre-tensioned and placed within a predetermined detection area, such that adjacent yarns are spaced apart. For example, a visual inspection module (e.g., a high-definition camera) can be pre-installed above the predetermined detection area. The visual inspection module captures an image of the predetermined detection area, which includes all yarns placed within the predetermined detection area, thereby obtaining a yarn image. In other embodiments, the number of yarns contained in a single yarn image is less than the total number of yarns placed within the predetermined detection area.

[0030] S102, controlling the preset tension detector to move and respectively detect each yarn in the preset detection area to obtain the yarn tension value.

[0031] In implementation, the tension detector is pre-slidably connected near the preset detection area so that the preset detection area is located on the moving path of the tension detector, and it is ensured that when the tension detector moves into the preset detection area, the detection end of the tension detector can detect the yarn tension value in the preset detection area.

[0032] S103: The yarn tension value is fused with the yarn image, and the quality inspection result of each yarn is obtained by analysis. The fused yarn image and its corresponding quality inspection result are output so that the inspector can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes the determination result of whether the tension value is abnormal.

[0033] In practice, the yarn tension detection system is used to determine the actual position of each yarn in the yarn image within a preset detection area based on the arrangement of all yarns in the yarn image. Then, based on the actual position of each yarn, the system determines the order in which the tension detector detects the yarn tension values ​​for each yarn. For example, if the tension detector moves from one side of the preset detection area (such as side A) to the other side (side B), the tension detection order of all yarns in the preset detection area is from side A to side B. Based on this order, a one-to-one correspondence is established between the yarn tension values ​​successively detected by the tension detector during movement and the yarns in the yarn image, and the yarn tension value corresponding to each yarn is marked in the yarn image, thereby achieving the fusion processing of the yarn tension value and the yarn image.

[0034] Furthermore, the yarn tension detection system is also used to compare each yarn tension value with a preset yarn tension range. If the yarn tension value exceeds the preset yarn tension range, the yarn tension value is determined as an abnormal tension value and the corresponding yarn is determined as an abnormal yarn, so as to obtain the quality inspection result of each yarn. Accordingly, based on the quality inspection results of all yarns, the quality inspection results can be imaged in the yarn image, such as marking the abnormal yarn with a specific color in the yarn image to distinguish abnormal yarn from non-abnormal yarn.

[0035] Optionally, the yarn tension detection method further includes the following steps: S1011, based on the yarn image captured by the visual inspection module, analyzing each yarn characteristic and selecting a detection point for each yarn, wherein the detection point is a portion of the yarn that contacts the tension detector so that the tension detector can detect and obtain a tension value; wherein the yarn characteristic includes at least the physical structure of the yarn; S1012: Plan and generate a movement path according to the detection points of all yarns, and control the movement of the tension detector based on the movement path. Whenever the tension detector moves to any detection point, the tension detector is controlled to obtain a tension value.

[0036] S1011 specifically includes the following sub-steps: S10111, while the tension detector is moving, analyzing the dynamic behavior of each yarn based on the yarn image captured in real time by a preset visual inspection module; wherein the dynamic behavior includes at least yarn vibration behavior; S10112, obtaining the vibration frequency of each yarn and performing time series analysis to predict the estimated vibration frequency at different locations of each yarn; S10113, based on the yarn image and a preset yarn material library, the yarn characteristics corresponding to the yarn in the yarn image are matched from the yarn material library; wherein the yarn material library pre-stores a variety of different types of yarns and their corresponding images and physical structure characteristics; and combined with the estimated vibration frequency of each yarn, the detection point of each yarn is comprehensively determined; and it satisfies: the yarn at the detection point is affected by vibration in accordance with the preset requirements.

[0037] Among them, S10112 specifically includes the following: Based on a pre-built vibration simulation model, the tension detector is used as the vibration source. Based on the yarn characteristics, the yarn vibration frequency at different locations of each yarn is simulated and analyzed when the tension detector is in different positions. The correlation between the vibration frequency of each yarn and the distance between the tension detector and each yarn is analyzed, and the estimated vibration frequency of different parts of each yarn is predicted based on the correlation. The estimated vibration frequency refers to the vibration frequency of different parts of the yarn to which the estimated vibration frequency belongs when the tension detector moves to contact the yarn to which the estimated vibration frequency belongs.

[0038] The yarn tension detection method further comprises the following steps: The received yarn tension value and the vibration frequency of the corresponding detection point are subjected to vibration compensation processing on the yarn tension value corresponding to each detection point through a preset vibration compensation algorithm.

[0039] In practice, the visual inspection module can specifically be a high-resolution, high-speed camera. As the tension detector moves toward and within the preset detection zone, the yarn tension detection system continuously captures multiple frames of yarn images of the yarn within the preset detection zone. The yarn tension detection system analyzes the differences between the multiple frames of yarn images to determine the dynamic behavior of each yarn (e.g., vibration, stretching, twisting, etc.). For example, the yarn tension detection system can use optical flow to identify the specific dynamic behavior of each yarn. For example, when the direction of the optical flow vector changes randomly, the yarn is considered to be vibrating; when the optical flow vector increases uniformly along the yarn direction, the yarn is considered to be pulling; and when the optical flow vector exhibits nonlinear changes, the yarn is considered to be twisting.

[0040] If dynamic behavior occurs, the tension detection system can further determine whether the dynamic behavior is abnormal, that is, whether the parameters corresponding to the dynamic behavior exceed the preset parameter range. For example, if stretching occurs, the stretching length can be determined based on the change in yarn length in multiple frames of yarn images. If the stretching length exceeds the preset range, a warning message can be output for the inspection personnel to be notified. Furthermore, if twisting occurs, the twisting angle at the same position of the yarn in multiple frames of yarn images can be used to determine whether the twisting angle exceeds the preset range. In this case, the twisting behavior is considered abnormal and a warning message is issued.

[0041] When there is a yarn that vibrates (hereinafter referred to as the target yarn), the yarn tension detection system will first capture the target yarn image from the yarn image, and then find the yarn characteristics corresponding to the target yarn image from the preset yarn material library. The yarn characteristics specifically include the physical structural characteristics of the yarn, such as yarn material, thickness, thickness, twist, texture structure (such as knotting, joints), etc.; then analyze whether the yarn characteristics of the yarn segment of the target yarn in the preset detection area remain uniform in the length direction. If inconsistent, such as the existence of different texture structures in the length direction, or the inclusion of different yarn materials or uneven thickness in the length direction; then the yarn segment of the target yarn in the preset detection area is segmented to form several sub-yarn segments to ensure that the yarn characteristics at different yarn positions in the same sub-yarn segment are the same.

[0042] Then, the yarn characteristics corresponding to each sub-yarn segment of the target yarn and the initial distance value between each sub-yarn segment and the tension detector (that is, the initial distance between the tension detector and the yarn before the tension detector moves) are sequentially used as input parameters and input into the pre-built vibration simulation model, so as to simulate and output the yarn vibration frequency of each sub-yarn segment of the target yarn at different positions in the length direction in turn through the vibration simulation model; the vibration simulation model is a simulation model obtained after learning and training convergence based on vibration test data of the historical period, which includes a yarn physical model corresponding to each yarn in the yarn material library. By using the tension detector as the vibration source, the vibration conditions of different positions in the length direction of the yarn are simulated when the tension detector moves towards the yarn at a specific initial distance from the yarn, and the vibration conditions are characterized by the vibration frequency.

[0043] By adjusting the initial distance value, the set of yarn vibration frequencies at different positions in the length direction of the sub-yarn segment is determined when the tension detector is at different positions from the sub-yarn segment. Then, by analyzing the change of the yarn vibration frequency at different positions of the sub-yarn segment with the initial distance value, and based on the association model, it is predicted that when the initial distance value is 0 (that is, when the tension detector moves to the position used to detect the yarn tension value of the sub-yarn segment), the vibration frequency at different positions of the sub-yarn segment (that is, the estimated vibration frequency) is obtained. According to the prediction result, the position in the sub-yarn segment with the smallest estimated vibration frequency is selected as the detection point of the sub-yarn segment. If a certain yarn has multiple sub-yarn segments in the preset detection area, then the yarn will be determined to obtain multiple detection points, and the detection points correspond to the sub-yarn segments one by one. The detection points are the specific positions of the yarn used by the subsequent tension detector to perform tension detection on the yarn.

[0044] After determining the detection points of all sub-yarn segments contained in all yarns within the preset detection area, the movement path of the tension detector will be generated based on the specific locations of all detection points. A tension detector with multiple detection points can be selected so that the tension detector can synchronously detect all detection points of the target yarn containing multiple detection points; finally, the yarn tension detection system is used to control the movement of the tension detector along the movement path to ensure that the tension detector can detect the corresponding yarn tension value at all detection points during the movement process. After obtaining the yarn tension value, the preset vibration compensation algorithm can be used to correct the yarn tension value of the detection point based on the estimated vibration frequency corresponding to each detection point to reduce the error caused by vibration.

[0045] Optionally, the yarn tension detection method further includes the following steps: Whenever a quality inspection result is determined, determining an abnormal quality inspection result and a corresponding abnormal yarn, and determining an abnormal position of the abnormal yarn; Whenever a detection point is selected for each yarn, based on the yarn characteristics of each yarn, the abnormal position of the yarn with the same yarn characteristics in the historical period is determined, and according to the determined abnormal position, a detection point is added to the corresponding yarn.

[0046] During implementation, on the basis of using the aforementioned technical solution to determine the detection points for each yarn in the preset detection area one by one, it is also possible to further find yarns with the same yarn characteristics and abnormal positions from the historical period according to the yarn characteristics of the yarn, and analyze whether the abnormal position has preset regular characteristics. For example, if the abnormal position is a yarn knot / joint, if so, the position in the current yarn with the same preset regular characteristics as the abnormal position will be used as a detection point, so as to add a new detection point.

[0047] The present application also discloses a yarn tension detection system. Figure 2 ,include: An image acquisition module 201 is used to control a preset visual inspection module to acquire a yarn image of each yarn within a preset inspection area; The tension detection module 202 is used to control the movement of a preset tension detector and detect each yarn in a preset detection area to obtain a yarn tension value; The quality inspection and analysis module 203 is used to fuse the yarn tension value with the yarn image, analyze and obtain the quality inspection result of each yarn, and output the fused yarn image and its corresponding quality inspection result so that the inspection personnel can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes the determination result of whether the tension value is abnormal.

[0048] Optionally, the system further includes a detection point determination module for analyzing the characteristics of each yarn based on the yarn image captured by the visual detection module, and selecting a detection point for each yarn, wherein the detection point is a portion of the yarn that is in contact with the tension detector so that the tension detector can detect and obtain a tension value; wherein the yarn characteristics include at least the physical structure of the yarn; According to the detection points of all yarns, a moving path is planned and generated, and the movement of the tension detector is controlled based on the moving path. Whenever it moves to any detection point, the tension detector is controlled to obtain the tension value.

[0049] Optionally, the detection point determination module is also used to analyze the dynamic behavior of each yarn based on the yarn image collected in real time by the preset visual detection module during the movement of the tension tester; wherein the dynamic behavior at least includes the vibration behavior of the yarn; it is also used to obtain the vibration frequency of each yarn for time series analysis, and predict the estimated vibration frequency at different parts of each yarn; it is also used to analyze the characteristics of each yarn, and combine the estimated vibration frequency of each yarn to comprehensively determine the detection point of each yarn; and it meets the following requirements: the yarn at the detection point is affected by vibration in accordance with the preset requirements.

[0050] It also includes a vibration compensation module for performing vibration compensation processing on the yarn tension value corresponding to each detection point through a preset vibration compensation algorithm based on the received yarn tension value and the vibration frequency of the corresponding detection point.

[0051] Optionally, the detection point determination module is also used to obtain the yarn vibration frequency of different parts of each yarn when the tension detector is located at different positions; it is also used to analyze the correlation between the vibration frequency of each yarn and the distance between the tension detector and each yarn, and predict the estimated vibration frequency of different parts of each yarn based on the correlation. The estimated vibration frequency refers to the vibration frequency of different parts of the yarn to which the estimated vibration frequency belongs when the tension detector moves to contact the yarn to which the estimated vibration frequency belongs.

[0052] Optionally, the detection point determination module is also used to simulate and analyze the yarn vibration frequency of different parts of each yarn when the tension detector is located at different positions based on a pre-built vibration simulation model, with the tension detector as the vibration source, and based on the yarn characteristics.

[0053] Optionally, the detection point determination module is also used to match the yarn characteristics corresponding to the yarn in the yarn image from the yarn material library based on the yarn image and a preset yarn material library; wherein the yarn material library pre-stores a variety of different types of yarns and their corresponding images and physical structure characteristics.

[0054] Optionally, the detection point determination module is also used to determine the abnormal quality inspection results and the corresponding abnormal yarns, and determine the abnormal positions of the abnormal yarns whenever a quality inspection result is determined; it is also used to determine the abnormal positions of yarns with the same yarn characteristics in historical periods based on the yarn characteristics of each yarn whenever a detection point is selected for each yarn, and add detection points to the corresponding yarns according to the determined abnormal positions.

[0055] An embodiment of the present application further discloses a yarn tension detection device, which includes a memory and a processor. The memory stores a computer program that can be loaded by the processor and execute the yarn tension detection method as described above.

[0056] An embodiment of the present application also discloses a computer-readable storage medium, which stores a computer program that can be loaded by a processor and execute the yarn tension detection method as described above. The computer-readable storage medium includes, for example: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0057] It should be noted that, in this document, relational terms such as first and second, etc. are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0058] The above embodiments are intended only to illustrate the technical solutions of this application and are not intended to limit the scope of protection of this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on these embodiments, all other embodiments obtained by persons of ordinary skill in the art without inventive effort are also within the scope of protection to be protected by this application.

Claims

1. A yarn tension detection method, characterized in that: include: Controlling a preset visual inspection module to collect a yarn image of each yarn within a preset inspection area; Controlling the preset tension detector to move and respectively detect each yarn in the preset detection area to obtain the yarn tension value; The yarn tension value is fused with the yarn image, and the quality inspection result of each yarn is obtained by analysis. The fused yarn image and its corresponding quality inspection result are output so that the inspector can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes a determination result of whether the tension value is abnormal.

2. The yarn tension detection method according to claim 1, characterized in that: The method further comprises: Based on the yarn images captured by the visual inspection module, each yarn characteristic is analyzed, and a detection point is selected for each yarn, wherein the detection point is a portion of the yarn that is in contact with the tension detector so that the tension detector can detect and obtain a tension value; wherein the yarn characteristics include at least the physical structure of the yarn; A moving path is planned and generated according to all the detection points of the yarn, and the tension detector is controlled to move based on the moving path. Whenever the yarn moves to any detection point, the tension detector is controlled to obtain a tension value.

3. The yarn tension detection method according to claim 2, characterized in that: The method of analyzing the characteristics of each yarn based on the yarn image collected by the visual inspection module and selecting an inspection point for each yarn includes: During the movement of the tension detector, the dynamic behavior of each yarn is analyzed based on the yarn image collected in real time by the preset visual detection module; wherein the dynamic behavior at least includes the vibration behavior of the yarn; Obtain the vibration frequency of each yarn and perform time series analysis to predict the estimated vibration frequency at different locations of each yarn; Analyze the characteristics of each yarn and, in combination with the estimated vibration frequency of each yarn, comprehensively determine the detection point of each yarn; and satisfy: the vibration effect of the yarn at the detection point meets the preset requirements; The method further comprises: The received yarn tension values ​​and the vibration frequencies of the corresponding detection points are subjected to vibration compensation processing on the yarn tension values ​​corresponding to each detection point using a preset vibration compensation algorithm.

4. The yarn tension detection method according to claim 3, characterized in that: The obtaining of the vibration frequency of each yarn and performing time series analysis to predict the estimated vibration frequency at different locations of each yarn includes: Obtaining the yarn vibration frequency at different parts of each yarn when the tension detector is located at different positions; The correlation between the vibration frequency of each yarn and the distance between the tension detector and each yarn is analyzed, and the estimated vibration frequency of different parts of each yarn is predicted based on the correlation. The estimated vibration frequency refers to the vibration frequency of different parts of the yarn to which the estimated vibration frequency belongs when the tension detector moves to contact the yarn to which the estimated vibration frequency belongs.

5. The yarn tension detection method according to claim 4, characterized in that: The method of obtaining the yarn vibration frequency at different positions of each yarn when the tension detector is located at different positions includes: Based on the pre-built vibration simulation model, the tension detector is used as the vibration source, and based on the yarn characteristics, the yarn vibration frequency of different parts of each yarn is obtained when the tension detector is located in different positions.

6. The yarn tension detection method according to claim 3, characterized in that: The analysis of each yarn characteristic includes: Based on the yarn image and a preset yarn material library, yarn characteristics corresponding to the yarn in the yarn image are matched and obtained from the yarn material library; wherein the yarn material library pre-stores a plurality of different types of yarns and their corresponding images and physical structure characteristics.

7. The yarn tension detection method according to claim 1, characterized in that: The method further comprises: Whenever a quality inspection result is determined, determining an abnormal quality inspection result and a corresponding abnormal yarn, and determining an abnormal position of the abnormal yarn; Whenever a detection point is selected for each yarn, based on the yarn characteristics of each yarn, the abnormal position of the yarn with the same yarn characteristics in the historical period is determined, and according to the determined abnormal position, a detection point is added to the corresponding yarn.

8. A yarn tension detection system, characterized in that: include, An image acquisition module (201) is used to control a preset visual detection module to acquire a yarn image of each yarn within a preset detection area; A tension detection module (202) is used to control the movement of a preset tension detector and to detect each yarn in a preset detection area to obtain a yarn tension value; The quality inspection analysis module (203) is used to fuse the yarn tension value with the yarn image, analyze and obtain the quality inspection result of each yarn, and output the fused yarn image and its corresponding quality inspection result, so that the inspection personnel can know the one-to-one correspondence between the yarn and the quality inspection result; wherein the quality inspection result at least includes the determination result of whether the tension value is abnormal.

9. A yarn tension detection device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 7.

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