Tow bundling detection method based on image feature analysis

Abstract

The invention discloses a tow bundling detection method based on image feature analysis, and relates to the technical field of tow quality detection, and the method comprises the following steps: S1, building a detection device and fixing a tow; s2, setting a weight; s3, a weight releasing and falling process is formed; s4, the falling time is obtained, and the falling time of the weight is obtained according to the time difference between the starting time and the ending time; s5, calculating a bundling parameter; and S6, determining a bundling result, and completing the detection. Quantitative evaluation of the bundling property is achieved through the equivalent friction coefficient, the detection error is smaller than or equal to 1%, and compared with a traditional manual visual method and a tension method, the precision is improved by 5-20 times; meanwhile, the detection time of a single sample is shorter than or equal to 30 seconds, the efficiency is improved by 50% compared with that of traditional equipment, automation of the whole process of'detection-calculation-analysis-export 'is achieved, and manual intervention is reduced; the device also supports altitude compensation and dust environment protection, and can stably work in laboratories and workshops.

Description

Technical Field

[0001] This invention relates to the field of filament quality inspection technology, specifically a method for detecting filament bundle aggregation based on image feature analysis. Background Technology

[0002] The bundle cohesion of fiber products is a key indicator of fiber product quality, directly affecting the stability of subsequent textile and composite material molding processes, as well as the mechanical properties of the final product. Existing fiber bundle cohesion testing technologies have the following main shortcomings:

[0003] Subjective testing methods: Traditional manual unwinding methods rely on operators to rotate the filament bundle and observe the unwinding status, judging the bundle cohesion level based on experience, with an error of ≥20%, and cannot provide quantitative data support;

[0004] Poor equipment adaptability: Existing mechanized traction unwinding equipment (such as the glass fiber filament bundle detection device disclosed in patent CN202321056789.1) is mostly designed for single material bundles. Changing the detection material requires overall adjustment of the equipment structure, which is complicated to operate.

[0005] Low accuracy and efficiency: Equipment using tensile testing methods (such as patent CN202210834567.9) has a detection error of ≥5% and a single sample testing time of more than 1 minute, which is difficult to meet the batch testing needs of the production line;

[0006] Weak environmental adaptability: Existing equipment does not take into account the effect of altitude on gravitational acceleration, which can easily lead to data deviation when testing in high-altitude areas, and it lacks protection against dust and electromagnetic interference. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method for detecting the bundle bundle aggregation based on image feature analysis, thereby solving the problems mentioned in the background section.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] In a first aspect, the present invention provides a method for detecting the bundle bundle properties based on image feature analysis, comprising the following steps:

[0010] S1. Installation of the detection device and fixing of the wire bundle: Install the detection device at the detection station and fix the wire bundle to be tested on the detection device;

[0011] S2. Setting of weights: A weight release mechanism is set above the wire bundle, and the weights are installed in the weight release mechanism so that the weights are located inside the upper end of the wire bundle. The weights are stationary before release.

[0012] S3. Weight release and falling process: Control the weight release mechanism so that the weight enters the inside of the filament bundle from the top of the filament bundle under the action of gravity and moves downward along the axial direction of the filament bundle. During the falling process, the weight comes into contact with and rubs against the internal structure of the filament bundle, thereby forming a resistance effect related to the bundle bundle state.

[0013] S4. Acquisition of falling time: A detection unit is set up on the falling path of the weight to detect the passing status of the weight. When the weight starts to fall, the start time is recorded. When the weight reaches the end position, the end time is recorded. The falling time of the weight inside the wire bundle is obtained according to the time difference between the start time and the end time.

[0014] S5. Calculation of bundle properties parameters: Based on the obtained weight falling time, combined with the weight's mass, falling distance, and gravitational acceleration, calculate the bundle properties parameters corresponding to the bundle bundle state.

[0015] S6. Bundling result determination: The bundle bundle is compared with the pre-set determination criteria, and the bundle bundle bundle level is determined according to the comparison result to complete the detection of the bundle bundle bundle bundle state.

[0016] To further optimize this technical solution, in step S1, the detection device includes an aluminum alloy fixed frame, an upper pneumatic clamping device, and a lower fixed support platform; the wire bundle to be detected is set vertically within the aluminum alloy fixed frame, so that the upper end of the wire bundle is fixed by the upper pneumatic clamping device and the lower end is supported by the lower fixed support platform, so that the wire bundle is in a stable vertical state, which is used to ensure that the position of the wire bundle does not change during the detection process.

[0017] To further optimize this technical solution, the detection device also includes an auxiliary guide assembly, which consists of three sets of guide rings evenly distributed along the vertical direction, used to constrain the axis of the wire bundle and stabilize the weight falling channel.

[0018] To further optimize this technical solution, in step S1, fixing the filament bundle to be tested onto the testing device includes:

[0019] Take a sample of the filament bundle to be tested and arrange the sample along the vertical guide groove of the aluminum alloy fixed frame so that it forms a vertical tension state between the upper pneumatic clamping device and the lower fixed support platform.

[0020] The position of the filament bundle is calibrated by three sets of guide rings to keep the filament bundle axis coaxial with the frame reference axis and meet the fixed falling distance between the upper pneumatic clamping device and the lower fixed support platform; then the upper pneumatic clamping device is activated to clamp the filament bundle under clamping pressure to complete the fixation.

[0021] To further optimize this technical solution, in step S2, a through hole is provided in the center of the weight, and the weight is installed in the weight release mechanism through the through hole;

[0022] An NFC tag is affixed to the outer surface of the weight, which is automatically identified by an NFC reader, and the weight's specifications and mass parameters are read.

[0023] To further optimize this technical solution, in step S4, the detection unit uses two sets of D-TOF infrared ranging sensors arranged symmetrically above and below. The upper sensor is fixed to the upper part of the aluminum alloy fixed frame and aligned with the initial release position of the weight, while the lower sensor is fixed to the lower part of the aluminum alloy fixed frame and aligned with the end position of the weight on the lower fixed support platform.

[0024] At the start of the detection, the starting point of the timing is triggered when the upper sensor detects the weight release blocking signal, and the ending point of the timing is triggered when the lower sensor detects the weight reaching the ending position blocking signal.

[0025] To further optimize this technical solution, in step S4, anomaly detection is performed on the falling time of the obtained weight inside the wire bundle:

[0026] The system monitors whether the falling time is within the reasonable range of 0.1-10s. If it exceeds the range, it is judged as an abnormality, and the weight release mechanism is reset and "data invalid" is displayed.

[0027] If three consecutive abnormal detections are detected, the sensor self-diagnostic program will be automatically activated to check for sensor optical path obstruction or wiring faults.

[0028] To further optimize this technical solution, in step S5, when calculating the bundle aggregation parameters corresponding to the bundle aggregation state, an equivalent friction calculation model is constructed, and the equivalent friction coefficient is obtained through model calculation, which is used to characterize the degree of resistance of the internal structure of the bundle to the falling process of the weight.

[0029] To further optimize this technical solution, the equivalent friction calculation model also includes altitude compensation processing:

[0030] The system automatically adjusts the value of gravitational acceleration g after the user inputs the altitude, with an adjustment accuracy of ≤0.1m / s². After g is updated, the equivalent friction calculation model calculates the equivalent friction coefficient.

[0031] To further optimize this technical solution, in step S6, the equivalent friction coefficient is input into the clustering level judgment logic. The judgment rules are as follows: when μ≥0.8, it is judged as “excellent”; when 0.6≤μ<0.8, it is judged as “good”; when 0.4≤μ<0.6, it is judged as “medium”; and when μ<0.4, it is judged as “poor”.

[0032] In a second aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein the computer program instructions, when executed by the processor, implement the steps of a filament bundle bundle detection method based on image feature analysis as described in the first aspect of the present invention.

[0033] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of a filament bundle bundle detection method based on image feature analysis as described in the first aspect of the present invention.

[0034] Compared with existing technologies, this invention provides a method for detecting the bundle bundle properties based on image feature analysis, which has the following advantages:

[0035] This image feature analysis-based method for detecting filament bundle aggregation achieves quantitative assessment of aggregation through the equivalent friction coefficient, with a detection error of ≤1%. Compared to traditional manual visual inspection and tensile testing, it improves accuracy by 5-20 times. At the same time, the detection time for a single sample is ≤30 seconds, which is 50% more efficient than traditional equipment. It also automates the entire process of "detection-calculation-analysis-export," reducing manual intervention. Furthermore, it supports altitude compensation and dust environment protection, and can work stably in laboratories and workshops. Attached Figure Description

[0036] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 This is a schematic flowchart of a method for detecting the bundle bundle properties based on image feature analysis proposed in this invention. Detailed Implementation

[0038] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0039] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0040] Secondly, the term "an embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places throughout this specification does not necessarily refer to the same embodiment, nor is it a single embodiment or an embodiment selectively excluded from other embodiments.

[0041] Example 1:

[0042] Reference Figure 1 This is the first embodiment of the present invention, which provides a method for detecting the bundle bundle properties based on image feature analysis, characterized by comprising the following steps:

[0043] S1. Installation of the testing device and fixing of the wire bundle: Install the testing device at the testing station and fix the wire bundle to be tested on the testing device.

[0044] The testing device includes an aluminum alloy fixed frame, an upper pneumatic clamping device, and a lower fixed support platform. In this embodiment, the aluminum alloy fixed frame has a height of 550-650mm, a width of 180-220mm, and a depth of 130-170mm, with vertical guide grooves on the inner side. It is made of 6061-T6 aluminum alloy, and the frame structure is optimized through finite element analysis to ensure that the deformation during testing is ≤0.1mm, avoiding the impact of vibration on testing accuracy. The upper pneumatic clamping device uses a dual-cylinder drive to ensure uniform clamping force distribution and has a width adjustment mechanism to accommodate wire bundles with widths of 10-50mm. The clamping pressure is adjusted to 5.8±0.2kPa, and the clamping surface is coated with a Shore hardness tester. A 60±5HA silicone buffer layer; a 5mm thick silicone pad is laid on the surface of the lower fixed support platform, and the vertical distance between it and the upper pneumatic clamping device is fixed at 300mm±0.5mm, with a coaxiality tolerance of ≤±0.2mm. It is rigidly connected to the aluminum alloy fixed frame by bolts; at the same time, an auxiliary guide assembly is installed, which consists of 3 sets of guide rings evenly distributed in the vertical direction, with a guide ring spacing of 100mm±0.1mm, a center deviation along the X-axis of ≤±0.2mm, and an inner diameter of the guide ring that is 2mm±0.1mm larger than the maximum width of the wire bundle. It is used to constrain the wire bundle axis and stabilize the weight falling channel.

[0045] Fixing the filament bundle to be tested onto the testing device includes:

[0046] Take a sample of the filament bundle to be tested and cut it into a length of 350 mm. Arrange the sample along the vertical guide groove of the aluminum alloy fixed frame to form a vertical tension state between the upper pneumatic clamping device and the lower fixed support platform. The position of the filament bundle is calibrated by three sets of guide rings to keep the filament bundle axis coaxial with the frame reference axis and to ensure that the fixed drop distance between the upper pneumatic clamping device and the lower fixed support platform is 300 mm (i.e., 0.3 m). Then, the upper pneumatic clamping device is activated to clamp the filament bundle at a clamping pressure of 5.8 ± 0.2 kPa. The silicone buffer layer is used to achieve uniform distribution of clamping force and avoid damage to the filament bundle, while ensuring that the filament bundle does not slip relative to each other during the test.

[0047] S2. Setting of weights: A weight release mechanism is set above the wire bundle, and the weights are installed in the weight release mechanism so that the weights are located inside the upper end of the wire bundle. The weights are stationary before release.

[0048] Based on the material and stiffness / flexibility of the wire bundle to be tested, stainless steel weights are selected, including at least three weight specifications: 10g, 20g, and 50g. Each weight has a diameter of 8mm ± 0.1mm and a length of 20mm ± 0.1mm, with a surface roughness of Ra 1.6μm after sandblasting, and a through hole with a diameter of 2mm ± 0.05mm is provided in the center. The selected weights are installed on a pneumatic weight release mechanism, which temporarily fixes the weights with thin steel wires. The initial fixing position is set 10mm ± 0.2mm below the upper pneumatic clamping device, and the release response time is ≤ 0.1s to ensure that no additional impact is introduced at the moment of weight release and to maintain the consistency of the falling trajectory. An NFC tag is affixed to the outer surface of the weight, and the weight specifications and mass parameters are automatically identified by an NFC reader.

[0049] S3. Weight release and falling process: Control the weight release mechanism so that the weight enters the interior of the filament bundle from the top under the action of gravity and moves downward along the axial direction of the filament bundle. During the falling process, the weight comes into contact with and rubs against the internal structure of the filament bundle, thereby forming a resistance effect related to the bundle bundle state.

[0050] S4. Acquisition of falling time: A detection unit is set up on the falling path of the weight to detect the passing status of the weight. When the weight starts to fall, the start time is recorded, and when the weight reaches the end position, the end time is recorded. The falling time of the weight inside the wire bundle is obtained based on the time difference between the start time and the end time.

[0051] The detection unit employs two sets of symmetrically arranged D-TOF infrared ranging sensors. The sensors emit 940nm modulated infrared light, have a response time ≤0.1ms, and a ranging accuracy of ±0.5mm. They can filter out ambient light interference such as natural light and workshop lighting, ensuring stable detection even in workshop environments with dust concentrations ≤10mg / m³. The upper sensor is fixed to the upper part of the aluminum alloy frame and aligned with the initial release position of the weight, while the lower sensor is fixed to the lower part of the frame and aligned with the end position of the weight, located 10mm±0.2mm above the lower support platform. This ensures that the vertical coaxiality deviation between the two sensors is ≤±0.3mm.

[0052] At the start of the detection, the starting point of the timing is triggered when the upper sensor detects the weight release blocking signal, and the ending point of the timing is triggered when the lower sensor detects the weight reaching the ending position blocking signal.

[0053] Anomaly detection was performed on the falling time of the obtained weights inside the filament bundle:

[0054] The system monitors whether the falling time is within the reasonable range of 0.1-10s. If it exceeds the range, it is judged as an abnormality, and the weight release mechanism is reset and "data invalid" is displayed.

[0055] If three consecutive abnormal detections are detected, the sensor self-diagnostic program will be automatically activated to check for sensor optical path obstruction or wiring faults.

[0056] For example, when t < 0.1s (extremely loose filament) or t > 10s (weight stuck), the buzzer sounds an alarm and prompts "Reinstall sample" on the touch screen; if the sensor fails to detect a signal for 5 consecutive times, the sensor power is automatically cut off and a "sensor malfunction" message is displayed.

[0057] S5. Calculation of Bundle Parameters: Based on the obtained weight falling time, combined with the weight's mass, falling distance, and gravitational acceleration, calculate the bundle bundle parameters corresponding to the bundle bundle state.

[0058] When calculating the bundle aggregation parameters corresponding to the bundle aggregation state, an equivalent friction calculation model is constructed. The equivalent friction coefficient is obtained through the model calculation and is used to characterize the degree of resistance of the internal structure of the bundle to the falling process of the weight.

[0059] The equivalent friction calculation model is shown below:

[0060]

[0061] in,

[0062] It is the equivalent friction coefficient;

[0063] Material correction factor (e.g., carbon fiber) 1.2, Spandex 0.9, Polyester 1.1);

[0064] The measured fall time obtained in step S4;

[0065] The acceleration due to gravity is set to 9.8 m / s² by default and altitude compensation adjustment is supported.

[0066] The mass of the weights selected in step S3 (unit: kg, which can be automatically read by NFC or determined according to the weight specifications). To fix the falling distance, we take 0.3m (corresponding to a fixed spacing of 300mm).

[0067] The equivalent friction calculation model also includes altitude compensation processing.

[0068] The system automatically adjusts the value of gravitational acceleration g after the user inputs the altitude, with an adjustment accuracy of ≤0.1m / s². After g is updated, the equivalent friction calculation model calculates the equivalent friction coefficient to ensure that the calculation caliber remains consistent under different geographical conditions.

[0069] In this embodiment, a comparison table of friction coefficients of filaments made of different materials is provided, as shown in Table 1.

[0070] Table 1

[0071] Silk tow material Stainless steel models Static equivalent coefficient of friction (μ) Dynamic equivalent friction coefficient (μ) cotton fiber bundles 303 (Friction Roller) 0.48~0.62 0.35~0.49 Polyester tow (PET) 304 0.32~0.39 0.28~0.35 Polyamide 6 (PA6) tow 316 0.29~0.47 0.20~0.23 UHMWPE fiber fabric 304 (steel back) 0.12~0.18 0.08~0.15 Para-aramid pulp tow 316L 0.38~0.52 0.30~0.41 <![CDATA[PBO fiber bundle (La 3+ modified)]]> 304 0.35~0.47 0.30~0.41 Basalt fiber bundles (BF) 304 0.48~0.58 0.42~0.53 Prepreg carbon fiber bundles (0° angle) 316L 0.42~0.58 0.35~0.48 Stainless steel fiber (6μm diameter) 304 (self-friing) 0.70~0.77 0.44~0.50

[0072] S6. Bundling result determination: The bundle bundle is compared with the pre-set determination criteria, and the bundle bundle bundle level is determined according to the comparison result to complete the detection of the bundle bundle bundle bundle state.

[0073] The equivalent friction coefficient is input into the clustering level judgment logic. The judgment rules are as follows: when μ≥0.8, it is judged as "excellent", when 0.6≤μ<0.8, it is judged as "good", when 0.4≤μ<0.6, it is judged as "medium", and when μ<0.4, it is judged as "poor".

[0074] After the judgment is completed, the falling time t, friction coefficient μ and concentration level are displayed in real time on the 2.4-inch TFT-LCD touch screen, and the corresponding four colors are displayed as blue, green, yellow and red. When the level is "poor", an alarm is triggered by a buzzer with an alarm frequency of 1kHz and a duration of 2s.

[0075] The system stores each test result, with the storage module supporting the cyclic storage of no less than 1,000 sets of data. Each set of data includes at least the test time, weight specifications, drop time, equivalent friction coefficient, and grade information, and supports export via USB interface.

[0076] For example, test data is stored in the format of "sample number, test time (year-month-day hour:minute:second), weight specification (g), drop time (ms), equivalent friction coefficient, grade", and can be exported as a CSV file via USB interface, which can be opened and analyzed directly with Excel.

[0077] In practical application of the methods described above, the following points should be noted:

[0078] Equipment assembly requirements: The tightening torque of the connecting bolts of each component should be 2-3 N·m. The optical path calibration of the sensor requires the assistance of a laser collimator to ensure that the coaxiality deviation is ≤ ±0.3 mm.

[0079] Routine maintenance: Calibrate the k-value once a month with a standard wire bundle, clean the sensor probe quarterly (wipe with a lint-free cloth dampened with alcohol), and check the lubrication of the lead screw mechanism annually (add lithium-based grease).

[0080] Applicable environment: operating temperature -10℃~40℃, relative humidity ≤80%, free from corrosive gases and strong electromagnetic interference (e.g., away from welding machines, frequency converters and other equipment).

[0081] Example 2:

[0082] 12K carbon fiber tow detection was performed based on the method described in Example 1.

[0083] Equipment debugging:

[0084] Enter the altitude value (e.g., Beijing's altitude is 31.2m, g=9.801m / s²).

[0085] Selecting a 50g weight, the NFC reader automatically recognizes m=0.05kg;

[0086] Calibrate using 12K carbon fiber standard filament bundle, measure t=1.4s, calculate k=0.000875 and store it.

[0087] Sample testing:

[0088] Cut a 350mm long 12K carbon fiber sample and place it vertically between the upper pneumatic clamping device and the lower fixed support platform. The position is then calibrated using a guide ring.

[0089] When the test is started, the weight release mechanism releases the thin steel wire, and the weight falls freely along the inside of the wire bundle.

[0090] The upper and lower sensors sequentially capture the weight signals, record t=1.42s, calculate μ=0.000875×(1.42×9.801) / (0.05×0.3)=0.000875×13.917 / 0.015≈0.805, and judge the level as "excellent";

[0091] The test results are displayed on the touchscreen, the data is automatically stored, and can be exported to a computer via USB.

[0092] Result verification:

[0093] The test was repeated 5 times, and the μ values ​​were 0.805, 0.812, 0.798, 0.803 and 0.807, respectively. The mean value was 0.805 and the standard deviation was 0.005, which proved that the test repeatability was good.

[0094] Example 3:

[0095] 40D spandex tow was tested based on the method described in Example 1.

[0096] Equipment debugging:

[0097] Select a 10g weight (m=0.01kg), calibrate with a 40D spandex standard yarn (known μ=0.5), measure t=2.1s, and calculate k=0.5×0.01×0.3 / (2.1×9.8)=0.0015 / 20.58≈0.0000729.

[0098] Sample testing:

[0099] Cut a 350mm long 40D spandex sample, adjust the width of the upper pneumatic clamping device to 15mm, and set the clamping pressure to 5.8kPa;

[0100] Start the detection, record t=2.05s, calculate μ=0.0000729×(2.05×9.8) / (0.01×0.3)=0.0000729×20.09 / 0.003≈0.482, and judge the level as "medium".

[0101] Results analysis:

[0102] The spandex sample has a μ value of 0.482, which is close to the lower limit of the "medium" grade, indicating that the production process (such as adjusting the spinning speed) needs to be optimized to improve the bundle properties.

[0103] Example 4:

[0104] This embodiment also provides a computer device applicable to a method for detecting the bundle bundle density based on image feature analysis, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for detecting the bundle bundle density based on image feature analysis as proposed in the above embodiment.

[0105] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a filament bundle aggregation detection method based on image feature analysis as proposed in the above embodiments.

[0106] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0107] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0108] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0109] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0110] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0111] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for detecting the bundle bundle properties based on image feature analysis, characterized in that, Includes the following steps: S1. Installation of the detection device and fixing of the wire bundle: Install the detection device at the detection station and fix the wire bundle to be tested on the detection device; S2. Setting of weights: A weight release mechanism is set above the wire bundle, and the weights are installed in the weight release mechanism so that the weights are located inside the upper end of the wire bundle. The weights are stationary before release. S3. Weight release and falling process: Control the weight release mechanism so that the weight enters the inside of the filament bundle from the top of the filament bundle under the action of gravity and moves downward along the axial direction of the filament bundle. During the falling process, the weight comes into contact with and rubs against the internal structure of the filament bundle, thereby forming a resistance effect related to the bundle bundle state. S4. Acquisition of falling time: A detection unit is set up on the falling path of the weight to detect the passing status of the weight. When the weight starts to fall, the start time is recorded. When the weight reaches the end position, the end time is recorded. The falling time of the weight inside the wire bundle is obtained according to the time difference between the start time and the end time. S5. Calculation of bundle properties parameter: Based on the obtained weight falling time, combined with the weight's mass, falling distance, and gravitational acceleration, calculate the bundle properties parameter corresponding to the bundle bundle state, i.e., the equivalent friction coefficient μ. S6. Bundling result determination: The equivalent friction coefficient μ is compared with the pre-set judgment standard, and the bundling level of the filament bundle is determined according to the comparison result, thus completing the detection of the bundling state of the filament bundle.

2. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S1, the detection device includes an aluminum alloy fixed frame, an upper pneumatic clamping device, and a lower fixed support platform. The wire bundle to be detected is placed vertically within the aluminum alloy fixed frame, with the upper end of the wire bundle fixed by the upper pneumatic clamping device and the lower end supported by the lower fixed support platform, so that the wire bundle is in a stable vertical state to ensure that the position of the wire bundle does not change during the detection process.

3. The method for detecting the bundle bundle properties based on image feature analysis according to claim 2, characterized in that, The detection device also includes an auxiliary guide assembly, which consists of three sets of guide rings evenly distributed along the vertical direction, used to constrain the axis of the wire bundle and stabilize the weight falling channel.

4. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S1, fixing the filament bundle to be tested onto the testing device includes: Take a sample of the filament bundle to be tested and arrange the sample along the vertical guide groove of the aluminum alloy fixed frame so that it forms a vertical tension state between the upper pneumatic clamping device and the lower fixed support platform. The position of the filament bundle is calibrated by three sets of guide rings to keep the filament bundle axis coaxial with the frame reference axis and meet the fixed falling distance between the upper pneumatic clamping device and the lower fixed support platform; then the upper pneumatic clamping device is activated to clamp the filament bundle under clamping pressure to complete the fixation.

5. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S2, a through hole is provided in the center of the weight, and the weight is installed in the weight release mechanism through the through hole. An NFC tag is affixed to the outer surface of the weight, which is automatically identified by an NFC reader, and the weight's specifications and mass parameters are read.

6. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S4, the detection unit uses two sets of D-TOF infrared ranging sensors arranged symmetrically above and below. The upper sensor is fixed to the upper part of the aluminum alloy fixed frame and aligned with the initial release position of the weight, while the lower sensor is fixed to the lower part of the aluminum alloy fixed frame and aligned with the end position of the weight on the lower fixed support platform. At the start of the detection, the starting point of the timing is triggered when the upper sensor detects the weight release blocking signal, and the ending point of the timing is triggered when the lower sensor detects the weight reaching the ending position blocking signal.

7. The method for detecting the bundle bundle properties based on image feature analysis according to claim 6, characterized in that, In step S4, anomaly detection is performed on the falling time of the obtained weight inside the wire bundle: The system monitors whether the falling time is within the reasonable range of 0.1-10s. If it exceeds the range, it is judged as a detection abnormality, and the weight release mechanism is reset and "data invalid" is displayed. If three consecutive abnormal detections are detected, the sensor self-diagnostic program will be automatically activated to check for sensor optical path obstruction or wiring faults.

8. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S5, when calculating the bundle aggregation parameter corresponding to the bundle aggregation state, an equivalent friction calculation model is constructed, and the equivalent friction coefficient is obtained through model calculation, which is used to characterize the degree of resistance of the internal structure of the bundle to the falling process of the weight.

9. The method for detecting the bundle bundle properties based on image feature analysis according to claim 8, characterized in that, The equivalent friction calculation model also includes altitude compensation processing. The system automatically adjusts the value of gravitational acceleration g after the user inputs the altitude, with an adjustment accuracy of ≤0.1m / s². After g is updated, the equivalent friction calculation model calculates the equivalent friction coefficient.

10. The method for detecting the bundle bundle properties based on image feature analysis according to claim 1, characterized in that, In step S6, the equivalent friction coefficient μ is input into the clustering level judgment logic. The judgment rules are as follows: when μ≥0.8, it is judged as "excellent", when 0.6≤μ<0.8, it is judged as "good", when 0.4≤μ<0.6, it is judged as "medium", and when μ<0.4, it is judged as "poor".

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