Silk-covered wire weaving density automatic control method

Abstract

The invention is applicable to the technical field of silk-covered wire weaving, and particularly relates to a silk-covered wire weaving density automatic control method, which comprises the following steps of: acquiring region characteristics, weaving pitches and weaving densities of a plurality of regions of a to-be-woven cable in real time; determining a first area according to each knitting pitch and each knitting density; obtaining a second region according to each region feature; under the condition that it is determined that the whole to-be-braided cable is abnormal according to the first region and the second region, the braiding pitch of each region of the to-be-braided cable is adjusted; under the condition that it is determined that the to-be-braided cable is not overall abnormal according to the first area and the second area, a third area behind the second area in the braiding direction of the to-be-braided cable is determined according to the first area; and the knitting pitch of the second area and the third area is adjusted. Therefore, the silk-covered wire weaving density automatic control method provided by the invention can prevent the problem that the silk-covered wire is evolved into global defects due to local weaving problems in the silk-covered wire weaving process.

Description

Technical Field

[0001] This application belongs to the field of silk-covered yarn braiding technology, and in particular relates to an automatic control method for the braiding density of silk-covered yarn. Background Technology

[0002] Wire braiding is a process that uses precision machinery to interweave threads (such as nylon, glass fiber, or metal wire) at specific angles and pitches onto the surface of a conductor (such as copper or aluminum wire) to form a braided layer that provides insulation, protection, or enhanced performance.

[0003] In existing wire braiding processes, the axial movement of the conductor, the rotation of the braiding head, and the wire feeding system form a dynamic balance. Uneven wire winding in one area directly alters the wire distribution in subsequent areas. Furthermore, existing technologies typically address anomalies in isolated areas, neglecting inter-regional interactions, making it easier for local anomalies to spread into global risks. Therefore, existing wire braiding techniques suffer from the problem that local problems can easily evolve into global defects. Summary of the Invention

[0004] This application provides an automatic control method for the braiding density of silk-covered yarn, which can prevent problems caused by local braiding issues from evolving into global defects during the braiding process.

[0005] In a first aspect, embodiments of this application provide an automatic control method for the braiding density of covered yarn, including: The system acquires in real time the regional characteristics, weaving pitch, and weaving density of multiple areas of the cable to be braided; wherein the regional characteristics include tension and weaving angle, and the weaving pitch is the ratio of axial movement distance to the number of interlacing cycles; A first region is determined based on each braiding pitch and each braiding density; wherein, the multiple regions of the cable to be braided include the first region and multiple other regions, and the first region is the abnormal region of the cable to be braided that currently needs adjustment; A second region is obtained based on the characteristics of each of the aforementioned regions; wherein, the second region is an abnormal region in which the cable to be braided may need to be adjusted in the future; If the overall abnormality of the cable to be braided is determined based on the first region and the second region, the braiding pitch of each region of the cable to be braided is adjusted. If it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, a third region is determined from the first region that is after the second region along the braiding direction of the cable to be braided. Adjust the weaving pitch of the second region and the third region.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The automatic control method for braiding density of silk-covered wire provided in this application acquires the regional characteristics, braiding pitch, and braiding density of multiple regions of the cable to be braided in real time; determines a first region based on each braiding pitch and density; obtains a second region based on the characteristics of each region; adjusts the braiding pitch of each region of the cable to be braided if the overall cable is determined to be abnormal based on the first and second regions; and determines a third region following the second region along the braiding direction of the cable to be braided based on the first region if the overall cable is not abnormal based on the first and second regions, and adjusts the braiding pitch of the second and third regions. Therefore, the automatic control method for braiding density of silk-covered wire provided in this application can not only accurately locate the abnormal regions that need adjustment, but also predict regions that may need adjustment in the future by analyzing regional characteristics, and further determine the regions that may be affected later. This realizes the transformation from passively solving problems to actively preventing problems, which is conducive to improving the level of cable braiding quality control. Different adjustment strategies are adopted according to whether the overall cable is abnormal. When the overall cable is abnormal, a comprehensive adjustment is performed; when it is not abnormal, a targeted local adjustment is performed. This allows for reasonable allocation of resources according to the actual situation, avoiding over-adjustment or under-adjustment, which is conducive to improving production efficiency and product quality, while reducing production costs.

[0007] In one possible implementation of the first aspect, obtaining the second region based on each of the region features includes: Based on the regional characteristics of the first region, candidate regions are determined from among the other regions; For each candidate region, the probability that the candidate region is the second region is obtained based on the regional characteristics of the first region and the regional characteristics of the candidate region. The second region is determined based on the probability.

[0008] In one possible implementation of the first aspect, the step of obtaining the probability that the candidate region is the second region based on the regional features of the first region and the regional features of the candidate region for each candidate region includes: A region feature pair is obtained based on the region features of the first region and the region features of each of the candidate regions; wherein, the region feature pair is used to reflect the association relationship between the first region and each of the candidate regions; The probability that each candidate region is the second region is obtained based on the region features.

[0009] In one possible implementation of the first aspect, before adjusting the braiding pitch of each region of the cable to be braided in the event that the overall cable to be braided is abnormal based on the first region and the second region, the method further includes: A time series model is established based on the weaving density of the first region and the second region; wherein, the time series model is used to predict the weaving density of multiple regions of the cable to be woven at future times; The weaving density in each of the other regions following the second region is predicted based on the time series model, and it is determined whether the overall cable to be woven is abnormal.

[0010] In one possible implementation of the first aspect, adjusting the braiding pitch of each region of the cable to be braided when an overall abnormality is determined based on the first region and the second region includes: When the overall anomaly of the cable to be braided is determined based on the first region and the second region, the optimal pitch combination is obtained by using a genetic algorithm with the goal of minimizing the deviation of each braiding density and minimizing the adjustment amount of each braiding pitch. The braiding pitch of each region of the cable to be braided is adjusted according to the optimal pitch combination.

[0011] In one possible implementation of the first aspect, determining a third region following the second region along the braiding direction of the cable to be braided, based on the first region and the second region, if it is determined that the cable to be braided is not an overall anomaly, includes: If it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, calculate the conditional probability that each of the other regions after the second region is abnormal under the condition that the first region is a braiding abnormality. The third region is determined based on the conditional probabilities described.

[0012] In one possible implementation of the first aspect, adjusting the weaving pitch of the second region and the third region includes: A transition section is defined after the second region; A transition section is determined before and after the third region; When the distance between the second region and the third region is greater than a preset threshold, the weaving pitch of the second region and the third region is gradually adjusted according to each transition segment.

[0013] In one possible implementation of the first aspect, the method further includes: If the distance between the first region and the second region is less than or equal to the preset threshold, the transition segment after the first region and the transition segment before the second region are merged, and the weaving pitch is adjusted according to the merged transition segment.

[0014] In one possible implementation of the first aspect, an automatic control device for the braiding density of covered yarn is applied, the automatic control device for the braiding density of covered yarn includes a control device and a covered yarn braiding machine, the covered yarn braiding machine includes a feeding zone, an interlacing zone and a winding zone, and the method further includes: Obtain a surface image of the cable to be braided; The texture uniformity is obtained by calculating the texture variance based on the surface image. The rotation speed of the interlacing zone is adjusted according to the texture uniformity.

[0015] In one possible implementation of the first aspect, the real-time acquisition of regional characteristics, braiding pitch, and braiding density of multiple regions of the cable to be braided includes: The wire feeding speed of the wire feeding area is obtained in real time; Each braiding pitch is calculated based on the wire feeding speed and the adjusted rotational speed of the interlacing zone; Each of the described weaving densities is determined based on the described weaving pitch and the described surface image.

[0016] Secondly, embodiments of this application provide an automatic control device for the braiding density of covered yarn, comprising: The acquisition module is used to acquire in real time the regional characteristics, weaving pitch, and weaving density of multiple areas of the cable to be braided; wherein, the regional characteristics include tension and weaving angle, and the weaving pitch is the ratio of axial movement distance to the number of interlacing cycles; The first region module is used to determine a first region based on each braiding pitch and each braiding density; wherein, the multiple regions of the cable to be braided include the first region and multiple other regions, and the first region is the abnormal region of the cable to be braided that currently needs to be adjusted; The second region module is used to obtain a second region based on the characteristics of each region; wherein, the second region is an abnormal region that may need to be adjusted in the future for the cable to be braided; An overall anomaly module is used to adjust the braiding pitch of each region of the cable to be braided when an overall anomaly is determined to be in the cable to be braided based on the first region and the second region. The third region module is used to determine, based on the first region, a third region following the second region along the braiding direction of the cable to be braided, if it is determined from the first region and the second region that the cable to be braided is not an overall abnormality. An adjustment module is used to adjust the weaving pitch of the second region and the third region.

[0017] Thirdly, embodiments of this application provide an automatic control device for the braiding density of covered yarn, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method as described in any one of the first aspects above.

[0018] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0019] Fifthly, embodiments of this application provide a computer program product that, when run on an automatic control device for the braiding density of covered yarn, causes the automatic control device for the braiding density of covered yarn to execute the method described in any one of the first aspects above.

[0020] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating an embodiment of the automatic control method for braiding density of covered yarn provided in this application; Figure 2 This is a schematic diagram illustrating the implementation process of steps S300, S320, S400, S500 and S600 in the automatic control method for braiding density of covered yarn provided in an embodiment of this application. Figure 3 This is a schematic diagram of the implementation process of step S100 in the automatic control method for braiding density of covered yarn provided in an embodiment of this application; Figure 4 This is a schematic diagram of the braiding density and braiding pitch in an embodiment of the automatic control method for braiding density of covered yarn provided in this application; Figure 5 This is a schematic diagram of tension and weaving angle in an automatic control method for the braiding density of covered yarn provided in an embodiment of this application; Figure 6This is a schematic diagram illustrating the effect of braiding pitch adjustment in an embodiment of the automatic control method for braiding density of covered yarn provided in this application; Figure 7 This is a schematic diagram of the automatic control device for braiding density of silk-covered yarn provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the automatic control device for the braiding density of the silk-covered yarn provided in the embodiments of this application. Detailed Implementation

[0023] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0024] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0025] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0026] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0027] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0029] In related technologies, the axial movement of the conductor, the rotation of the braiding head, and the yarn feeding system form a dynamic balance. Uneven yarn winding in a certain area will directly alter the yarn distribution in subsequent areas. Furthermore, existing technologies typically address anomalies in isolated areas, neglecting the interactions between regions, making it easier for local anomalies to spread into global risks. Therefore, existing yarn braiding techniques suffer from the problem that local problems can easily evolve into global defects.

[0030] To address the aforementioned issues, this application provides an automatic control method for the braiding density of silk-covered wire. This method involves acquiring the regional characteristics, braiding pitch, and braiding density of multiple regions of the cable to be braided in real time; determining a first region based on each braiding pitch and density; obtaining a second region based on the characteristics of each region; adjusting the braiding pitch of each region if the overall cable is determined to be abnormal based on the first and second regions; and determining a third region following the second region along the braiding direction of the cable based on the first region if the overall cable is not abnormal based on the first and second regions; and adjusting the braiding pitch of the second and third regions. Therefore, the automatic control method for the braiding density of silk-covered wire provided by this application not only accurately locates the abnormal regions that currently need adjustment but also predicts regions that may need adjustment in the future by analyzing regional characteristics, and further determines regions that may be affected subsequently. This achieves a shift from passively solving problems to proactively preventing them, which is beneficial for improving the level of cable braiding quality control. Different adjustment strategies are adopted depending on whether the overall cable is abnormal. When there is an overall anomaly, comprehensive adjustments are made; when there is no overall anomaly, targeted local adjustments are made. This allows for the rational allocation of resources based on the actual situation, avoiding over-adjustment or under-adjustment, which is conducive to improving production efficiency and product quality, while reducing production costs.

[0031] The automatic control method for braiding density of covered yarn provided in this application embodiment can be applied to an automatic control device for braiding density of covered yarn. In this case, the automatic control device for braiding density of covered yarn is the executing entity of the automatic control method for braiding density of covered yarn provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of automatic control device for braiding density of covered yarn.

[0032] For example, the automatic control equipment for the braiding density of silk-covered yarn includes a control device and a silk-covered yarn braiding machine. The control device can be an industrial computer, a programmable logic controller, an embedded control system, a distributed control system, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a laptop computer, a handheld computing device, etc., but is not limited to these.

[0033] To better understand the automatic control method for braiding density of covered yarn provided in the embodiments of this application, the specific implementation process of the automatic control method for braiding density of covered yarn provided in the embodiments of this application will be described by way of example below.

[0034] Figure 1 This illustration shows a schematic flowchart of an automatic control method for the braiding density of covered yarn provided in an embodiment of this application. The automatic control method for the braiding density of covered yarn includes: S100 acquires in real-time the regional characteristics, braiding pitch, and braiding density of multiple areas of the cable to be braided. Among them, the regional characteristics include tension and braiding angle, and the braiding pitch is the ratio of axial movement distance to the number of interlacing cycles.

[0035] For example, the cable to be braided can be divided into multiple regions along the braiding direction based on a preset fixed length.

[0036] For example, a multi-sensor array can be used to collect real-time data on tension, weaving angle, axial movement distance, and interlacing count in different areas of the cable to be braided. Tension is measured using a piezoelectric sensor, and the weaving angle is obtained through a dual-axis gyroscope combined with machine vision calibration. Figure 5 As shown; the weaving pitch is calculated by recording the axial movement distance (e.g., 20mm) and the number of interlacing cycles (e.g., 4 times) using an encoder (e.g., 20mm / 4=5mm). Figure 4 As shown; weaving density is the number of interlacing points per unit length, which can be obtained by capturing the weaving trajectory with a high-speed camera and statistically analyzing it using image processing algorithms (such as OpenCV contour detection), as shown. Figure 4 As shown.

[0037] S200, determine the first region based on each braiding pitch and each braiding density. The multiple regions of the cable to be braided include the first region and multiple other regions. The first region is the abnormal region of the cable to be braided that currently requires adjustment.

[0038] For example, the weaving pitch of each region can be compared with a corresponding preset range, or the weaving density of each region can be compared with a corresponding preset range, and the region whose weaving pitch or weaving density exceeds the corresponding preset range can be determined as the first region. For example, the preset range of weaving pitch is 4-6 mm, and the preset range of weaving density is 2-4 times / cm.

[0039] S300, based on the characteristics of each area, a second area is obtained. This second area represents the abnormal areas where the cable to be braided may require future adjustments.

[0040] For example, an LSTM neural network model can be built using historical weaving data (including regional characteristics, weaving pitch, and weaving density of multiple regions during the real-time weaving process of the cable). This LSTM neural network model can then be used to perform time-series predictions of the regional features. The input consists of regional features (including tension and weaving angle) from 20 historical time steps, and the output is a predicted value for the future (e.g., 3 time steps). If the predicted value exceeds a preset range, it is marked as a second region.

[0041] S400, if an overall abnormality is determined in the cable to be braided based on the first and second regions, the braiding pitch of each region of the cable to be braided is adjusted.

[0042] For example, if the proportion of the first region is greater than the corresponding preset threshold or the number of the second region (i.e., the predicted abnormal region) is greater than the corresponding preset threshold, then the overall cable to be braided is determined to be abnormal, and the braiding pitch of each region of the cable to be braided is adjusted according to the braiding pitch and braiding density of the first region and the regional characteristics of the second region.

[0043] S500, if it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, a third region is determined from the first region after the second region along the braiding direction of the cable to be braided.

[0044] For example, if it is determined that the cable to be braided is not abnormal as a whole based on the first region and the second region, a tension transmission model (such as a BP neural network, which predicts the tension change of multiple regions after inputting the tension of a certain region) can be established by using historical braiding data (including the regional characteristics, braiding pitch and braiding density of multiple regions of the cable in real time during the historical braiding process) to confirm whether the tension of the region after the second region is affected by the tension of the first region, and the region with the largest change in tension is determined as the third region.

[0045] S600, adjust the weaving pitch of the second and third zones.

[0046] For example, a PID control algorithm can be used to adjust the weaving pitch of the second and third regions. For instance, assuming a target pitch of 5mm, a current second region pitch of 7.2mm, and a third region pitch of 5.1mm, the PID parameter K... p =0.8, K i =0.1, K d =0.05. Adjustment amount ΔP=K p e+K i ∫edt+K d de / dt, where e = target weaving pitch - current weaving pitch, the adjustment amount ΔP in the second region is -2.2mm, and the adjustment amount ΔP in the third region is -0.1mm, to achieve a smooth transition.

[0047] In one possible implementation, please refer to Figure 2 S300, based on the characteristics of each region, the second region is obtained, including: S310, determine candidate regions among multiple other regions based on the regional characteristics of the first region.

[0048] For example, candidate regions can be obtained by filtering regions whose distance from the boundary of the first region is less than a preset distance threshold based on the regional characteristics of the first region. Alternatively, a tension transmission model established using historical weaving data can be used to input the tension of the first region, obtain the tension change of multiple regions after the first region, and determine the regions whose tension change is greater than a preset threshold as candidate regions.

[0049] S320, for each candidate region, the probability that the candidate region is the second region is obtained based on the regional characteristics of the first region and the regional characteristics of the candidate region.

[0050] For example, the regional features of the first region and the candidate region can be combined, and a Gaussian Mixture Model (GMM) can be used to estimate the probability density of the feature space. Then, Bayes' theorem can be used to calculate the posterior probability that the candidate region belongs to the second region. For example, P(second region) Feature) = P(Feature) Second Zone) P(second region) / P(feature), where the prior probability P(second region) is set as the mean probability that the candidate region is a historical anomaly region, and the likelihood function P(feature) is... The second region was obtained by fitting with GMM.

[0051] S330, determine the second region based on probability.

[0052] For example, the region with the highest probability can be directly identified as the second region. Alternatively, an adaptive threshold filtering strategy can be used, dynamically adjusting the probability threshold based on production conditions. When the cable is under high tension, the probability threshold is set to 0.6; when under low tension, it is set to 0.4, and the region with a probability greater than the probability threshold is identified as the second region.

[0053] Through the above steps S310 to S330, accurate anomaly prediction under different working conditions is achieved through GMM and adaptive threshold; probabilistic prediction identifies potential anomaly areas in advance, which helps to reduce downtime for adjustment and improve efficiency; by accurately adjusting the braiding pitch of the second and third regions, it helps to reduce cable tension fluctuation rate and braiding angle deviation and improve product qualification rate.

[0054] Optionally, please refer to Figure 2 S320, for each candidate region, the probability that the candidate region is the second region is obtained based on the regional characteristics of the first region and the regional characteristics of the candidate region, including: S321, obtain regional feature pairs based on the regional features of the first region and the regional features of each candidate region. These regional feature pairs reflect the correlation between the first region and each candidate region.

[0055] For example, feature vectors for the first region and each candidate region can be obtained based on the regional features of the first region and the regional features of each candidate region. A multidimensional correlation matrix can be constructed based on the feature vectors of the first region and the candidate regions. For example, the tension difference between the tension 45N in the first region and the tension 42N in candidate region A is 3N, and the offset between the angles 38° and 35° is 3°. The regional feature pairs are: [3N, 3... Alternatively, the Pearson correlation coefficient can be used to quantify the linear correlation between feature vectors, while the Spearman rank correlation can capture nonlinear relationships to obtain regional feature pairs.

[0056] S322, based on the regional characteristics, obtain the probability that each candidate region is the second region.

[0057] For example, the XGBoost gradient boosting tree model can be used for probability prediction. The input is a feature pair matrix, and the output is the probability that a candidate region belongs to the second region. The model's decision can be interpreted using the SHAP value; for example, a tension difference > 2N contributes +0.15 to the probability, and an angle offset > 2° contributes +0.12.

[0058] Through the above steps S321 to S322, the XGBoost model captures the feature interaction effect by quantifying the multidimensional correlation between regions through the feature pair matrix. This is beneficial for improving the accuracy of probability prediction and adapting to complex working conditions such as tension fluctuations and angle deviations. The probability prediction results are helpful for accurately calculating the pitch adjustment amount in the second region, which helps to reduce the cable tension fluctuation rate and braiding angle deviation, as well as improve the product qualification rate.

[0059] In one possible implementation, please refer to Figure 2 S400, before adjusting the braiding pitch of each region of the cable to be braided in the event that an overall abnormality is determined based on the first region and the second region, the method further includes: S401, Establish a time series model based on the weaving density of the first and second regions. The time series model is used to predict the weaving density of multiple regions of the cable to be woven at future times.

[0060] For example, a sliding window feature (such as the mean and standard deviation of the density over the past 5 time steps) can be constructed based on the weaving density of the first and second regions, aligned by timestamps. For instance, the feature vector of region A at time t3 is [0.865, 0.015]. An LSTM (Long Short-Term Memory) network is used to handle time series dependencies, with an input dimension of (time step size × number of features). The model structure consists of a 2-layer LSTM (64 units per layer) + a fully connected layer (outputting the density prediction). Training is performed using data from the past 1000 time steps. A validation set AUC of 0.91 indicates that the model can capture density change trends, thus obtaining a time series model. Based on the density sequences of regions A and B (such as X...),... A =[0.85,0.88,0.87,0.89],X B =[0.90,0.92,0.91,0.93]) to obtain the feature vectors of region A and region B. Input the feature vectors of region A and region B into the time series model to predict the weaving density of other regions at time t5 (e.g., the weaving density of region C is 0.94).

[0061] S402, based on the time series model, predict the braiding density of each other region after the second region, and determine whether the overall cable to be braided is abnormal.

[0062] For example, the predicted braiding density output by the time series model (e.g., 0.94 for region C at time t5) can be compared with a preset threshold (e.g., 0.95). If the predicted braiding density of multiple other regions (e.g., >20%) is lower than the preset threshold, then the overall cable to be braided is determined to be abnormal.

[0063] Through the above steps S401 to S402, the LSTM model can predict density trends in advance, which is beneficial for early warning; after identifying the overall anomaly, adjusting the braiding pitch is beneficial for improving cable density uniformity and process stability.

[0064] In one possible implementation, please refer to Figure 2 S400, if an overall anomaly is determined in the cable to be braided based on the first and second regions, the braiding pitch of each region of the cable to be braided is adjusted, including: S410, when the overall anomaly of the cable to be braided is determined based on the first region and the second region, the optimal pitch combination is obtained by using a genetic algorithm with the goal of minimizing the deviation of each braiding density and minimizing the adjustment amount of each braiding pitch.

[0065] For example, the mean square error (MSE) between the predicted knit density and the target knit density can be calculated, the absolute values ​​of the adjustments for each knit pitch can be taken and summed, and the objective function can be determined, for example, f(x) = w1. MSE+w2 ∑∣Δp i |, where w1 and w2 are weights (e.g., w1=0.6, w2=0.4), Δp i Let be the adjustment amount for the weaving pitch in the i-th region. An initial population randomly generates a set of weaving pitch adjustments (e.g., ±10% of the baseline pitch value). Through genetic operations such as selection, crossover, and mutation, the weaving pitch adjustment is limited to ±15%. If it exceeds this range, a penalty term is added to the objective function, such as f(x) = w1. MSE+w2 ∑∣Δp i |+w3 max(0, |Δp) i The optimal pitch combination is obtained through iterative genetic algorithm (∣−0.15).

[0066] S420 adjusts the braiding pitch of each area of ​​the cable to be braided according to the optimal pitch combination.

[0067] For example, the braiding pitch of each region of the cable to be braided can be adjusted according to the optimal pitch combination. The braiding density feedback is monitored online. If the braiding density is still lower than the preset threshold after multiple consecutive detections (e.g., 3 times), secondary optimization is triggered (e.g., adjusting the weight w1 to 0.8).

[0068] Through the steps S410 to S420 above, the global optimal solution is found through the genetic algorithm, which helps to reduce the braiding density deviation and the amount of braiding pitch adjustment, and helps to improve process stability and cable qualification rate.

[0069] In one possible implementation, please refer to Figure 2S500, if it is determined from the first and second regions that the cable to be braided is not an overall abnormality, a third region is determined from the first region, following the second region along the braiding direction of the cable to be braided, including: S510, if it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, calculate the conditional probability that there is an abnormality in each of the other regions after the second region under the condition that the braiding is abnormal in the first region.

[0070] For example, a Bayesian network can be constructed based on historical braiding data (including regional characteristics, braiding pitch, and braiding density of multiple regions of historical braided cables). Nodes represent regional features, and edges represent causal relationships between features. For instance, abnormal tension in region A leads to angular shift in region B. The conditional (posterior) probability is calculated using Bayes' theorem. For example, P(D i |T1)=P(T1|D i ) P(D i ) / P(T1), where P(T1|D i P(D) represents the conditional probability of an abnormal tension in region A when region B has an abnormal density. This probability is obtained through statistical analysis of historical weaving data (e.g., 0.7). i Let P(D) be the prior probability of density anomaly in region B (e.g., 0.2), and P(T1) be the marginal probability of tension anomaly in region A (e.g., 0.3). Substituting these values, we get P(D). i |T1)=0.7×0.2 / 0.3≈0.47.

[0071] S520, determine the third region based on each conditional probability.

[0072] For example, all candidate regions after the second region can be arranged in descending order of conditional probability, and the region with the highest conditional probability can be selected as the third region. If there are multiple regions with the highest conditional probability, then the region that is most similar to the tension fluctuation of the first region can be rotated from among them.

[0073] Through the steps S510 to S520 above, high-risk areas are identified in advance using the conditional probability model, which is beneficial for accurately predicting the propagation of anomalies; by accurately locating the third area, unnecessary pitch adjustment and equipment energy consumption can be reduced; and by clearly identifying the third area, the standard deviation of cable braiding density and braiding angle deviation can be reduced, which is beneficial for improving the product qualification rate.

[0074] In one possible implementation, please refer to Figure 2 S600, adjust the weaving pitch of the second and third zones, including: S610, a transition section is defined after the second region.

[0075] For example, the initial pitch difference between the second and third regions (Δp=|p2-p3|) and the weaving speed can be determined by L trans1 =k Δp v determines the transition section length, where k is an empirical coefficient (usually taken as 0.8~1.2, depending on the rigidity of the cable material). For example, if the pitch of the second region p2 = 2.0 mm, the target pitch of the third region p3 = 2.5 mm, the braiding speed v = 50 mm / s, and k = 1.0, then the transition section length L trans1 =1.0×0.5×50=25mm. Extend L after the end point of the second region (coordinate x2). trans1 The distance, marked as the starting point x of the transition segment. trans1_start =x2, endpoint x trans1_end =x2+L trans1 .

[0076] S620 defines a transition section before and after the third region.

[0077] For example, the transition section before the third region can be determined based on the weaving pitch difference between the third and second regions. For instance, L trans2_front =k Δp v, where Δp = |p2−p3|, if Δp = 0.5mm, v = 50mm / s, k = 1.0, then L trans2_front =25mm. The transition section after the third region is determined based on the difference in weaving pitch between the third region and subsequent regions (if any).

[0078] S630, when the distance between the second and third regions is greater than a preset threshold, the weaving pitch of the second and third regions is gradually adjusted according to each transition segment.

[0079] For example, when the distance between the second and third regions exceeds a preset threshold, the weaving pitch of the second and third regions can be gradually adjusted according to each transition segment. Specifically, within the transition segment before the second or third region, the weaving pitch of the region preceding the second or third region is gradually adjusted to the (adjusted) weaving pitch of the second or third region. Similarly, within the transition segment after the second or third region, the (adjusted) weaving pitch of the second or third region is gradually adjusted to the weaving pitch of the region following the second or third region. This allows for a smooth transition from the adjusted weaving pitch in the second region to the adjusted weaving pitch in the third region. The adjustment amount of the weaving pitch within the second and third regions can be smoothly adjusted using a PID control algorithm.

[0080] Through steps S610 to S630, the gradual adjustment helps to achieve a uniform stress distribution in the cable and reduces the standard deviation of the bending radius. The dynamic transition section length and segmented adjustment strategy make the solution highly adaptable, which helps to improve process coverage, increase adjustment success rate, and reduce material waste.

[0081] In one possible implementation, please refer to Figure 2 The methods also include: S640, when the distance between the first region and the second region is less than or equal to a preset threshold, merge the transition segment after the first region and the transition segment before the second region, and adjust the weaving pitch according to the merged transition segment.

[0082] For example, when the distance between the first region and the second region is less than or equal to a preset threshold, the transition segment after the first region and the transition segment before the second region are merged, and the braiding pitch is adjusted according to the merged transition segment. Within the merged transition segment, the braiding pitch is linearly adjusted from the (adjusted) braiding pitch in the first region to the (adjusted) braiding pitch in the second region. The cable's braiding pitch is adjusted according to the merged transition segment, smoothly transitioning from the adjusted braiding pitch in the first region to the adjusted braiding pitch in the second region. The adjustment amount of the braiding pitch in the first and second regions can be smoothly adjusted using a PID control algorithm, with the adjustment effect as shown... Figure 6 As shown.

[0083] It is understandable that when the distance between the second and third regions is less than or equal to a preset threshold, the transition segment after the second region and the transition segment before the third region are also merged, and the weaving pitch is adjusted according to the merged transition segment.

[0084] After merging the transition sections through the above step S640, it is beneficial to reduce the standard deviation of the cable bending radius, improve adjustment efficiency, reduce the amount of parameter calculation, and increase the adjustment success rate. It is suitable for high-precision, multi-area cable braiding scenarios.

[0085] In one possible implementation, please refer to Figure 3 This method is applied to an automatic control device for the braiding density of covered yarn. The device includes a control unit and a yarn braiding machine. The yarn braiding machine includes a feeding zone, an interlacing zone, and a winding zone. The method also includes: S001, Obtain the surface image of the cable to be braided.

[0086] For example, an industrial-grade line scan camera equipped with a coaxial light source can be used to vertically photograph the surface of the cable at the exit of the braiding area of ​​the braiding machine. The camera transmits the surface image to the control device in real time through a gigabit Ethernet interface. The sampling frequency is synchronized with the braiding speed (e.g., when the cable moves at a speed of 2 m / s, it is sampled twice per millimeter).

[0087] S002, calculate the texture variance based on the surface image to obtain the texture uniformity.

[0088] For example, the CLAHE algorithm can be used to enhance the local contrast of the surface image and eliminate the effects of uneven illumination. Then, the surface image is divided into 10×10 pixel sub-blocks, and the gray-level variance of each sub-block is calculated to obtain the overall texture uniformity U=1−σ. block / σ max , where σ block σ is the mean variance of the sub-blocks. max For the theoretical maximum variance (such as σ in pure black and white textures) max =128).

[0089] S003, adjust the rotation speed of the interlacing zone according to the texture uniformity.

[0090] For example, a fuzzy PID control algorithm can be used to adjust the rotation speed of the interlacing zone by taking the deviation of the texture uniformity from the preset target texture uniformity (such as 0.9) and the rate of change of the deviation as input.

[0091] Through the above steps S001 to S003, the entire process delay from image acquisition to speed adjustment is reduced, supporting high-speed braiding, which is beneficial to improving the density control accuracy of cables and reducing the standard deviation of cable uniformity; the fuzzy PID algorithm adapts to working condition fluctuations and maintains control stability when tension changes and speed fluctuations occur, which is beneficial to improving the detection rate of cable surface defects, reducing scrap rate and saving raw material costs.

[0092] In one possible implementation, please refer to Figure 3 S100, acquires in real time the regional characteristics, braiding pitch, and braiding density of multiple areas of the cable to be braided, including: S110, real-time acquisition of the wire feeding speed in the wire feeding area.

[0093] For example, a high-precision encoder or laser velocimeter can be installed in the cable feeding area to directly measure the cable movement speed. The encoder acquires pulse signals in real time through a PLC or dedicated controller and converts them to obtain the cable feeding speed in the feeding area; the laser velocimeter transmits the cable feeding speed in the feeding area to the control device through an RS485 interface.

[0094] S120 calculates each braiding pitch based on the wire feeding speed and the adjusted rotation speed of the interlacing zone.

[0095] For example, the braiding pitch can be calculated based on the wire feed speed and the adjusted rotational speed of the interlacing zone. For instance, the braiding pitch p = ×1000 (mm / section), where, N carrier The number of crossover carriers (e.g., 4 groups) is represented by v, which is in m / s.

[0096] S130, determine each weaving density based on each weaving pitch and surface image.

[0097] For example, the actual braiding density can be extracted through surface image processing: convert the surface image into a black and white binary image, extract the cable skeleton, and calculate the number of intersections N per unit length. cross Actual density ρ actual =N cross / L, where L is the region length (e.g., 10 mm). The theoretical density ρ is calculated based on the pitch p. theory =1 / p, introducing a correction coefficient α=ρ actual / ρ theory If α does not belong to [0.95, 1.05], then the weaving pitch adjustment is triggered.

[0098] Through the above steps S110 to S130, the high-precision encoder and filtering algorithm help reduce speed error; the speed-rotation speed linkage calculation improves the accuracy of weaving pitch control; and image processing technology enables real-time density verification.

[0099] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] Corresponding to the automatic control method for braiding density of covered yarn described in the above embodiments, this application also provides an automatic control device for braiding density of covered yarn, the various modules of which can realize the various steps of the automatic control method for braiding density of covered yarn. Figure 7 A structural block diagram of the automatic control device for braiding density of covered yarn provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0101] Reference Figure 7 The device includes: The acquisition module is used to acquire in real time the regional characteristics, weaving pitch, and weaving density of multiple areas of the cable to be braided; wherein, the regional characteristics include tension and weaving angle, and the weaving pitch is the ratio of axial movement distance to the number of interlacing cycles; The first region module is used to determine a first region based on each braiding pitch and each braiding density; wherein, the multiple regions of the cable to be braided include the first region and multiple other regions, and the first region is the abnormal region of the cable to be braided that currently needs to be adjusted; The second region module is used to obtain a second region based on the characteristics of each region; wherein, the second region is an abnormal region that may need to be adjusted in the future for the cable to be braided; An overall anomaly module is used to adjust the braiding pitch of each region of the cable to be braided when an overall anomaly is determined to be in the cable to be braided based on the first region and the second region. The third region module is used to determine, based on the first region, a third region following the second region along the braiding direction of the cable to be braided, if it is determined from the first region and the second region that the cable to be braided is not an overall abnormality. An adjustment module is used to adjust the weaving pitch of the second region and the third region.

[0102] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0104] This application also provides an automatic control device for the braiding density of covered yarn. Figure 8 This is a schematic diagram of the structure of an automatic control device for the braiding density of covered yarn provided in an embodiment of this application. Figure 8 As shown, the automatic control device 8 for the braiding density of the silk-covered yarn in this embodiment includes: at least one processor 80 ( Figure 8 Only one is shown in the image), at least one memory 81 ( Figure 8 (Only one is shown in the image) and a computer program 82 stored in the at least one memory 81 and executable on the at least one processor 80. When the processor 80 executes the computer program 82, it causes the automatic control device 8 for the yarn braiding density to perform the steps in any of the above embodiments of the automatic control method for yarn braiding density, or causes the automatic control device 8 for the yarn braiding density to perform the functions of each module / unit in the above embodiments of the apparatus.

[0105] For example, the computer program 82 may be divided into one or more modules / units, which are stored in the memory 81 and executed by the processor 80 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program 82 in the automatic control device for the braiding density of the silk-covered yarn 8.

[0106] The automatic control device 8 for the braiding density of the covered yarn includes a control unit and a covered yarn braiding machine. The control unit can be an industrial computer, a programmable logic controller, an embedded control system, a distributed control system, etc. This automatic control device for the braiding density of the covered yarn may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of the automatic control device 8 for the braiding density of silk-covered wire, and does not constitute a limitation on the automatic control device 8 for the braiding density of silk-covered wire. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0107] The processor 80 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0108] In some embodiments, the memory 81 can be an internal storage unit of the automatic control device 8 for yarn braiding density, such as a hard disk or memory. In other embodiments, the memory 81 can be an external storage device of the automatic control device 8 for yarn braiding density, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 81 can include both internal and external storage units of the automatic control device 8 for yarn braiding density. The memory 81 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0110] This application provides a computer program product that, when run on an automatic control device for the braiding density of covered yarn, enables the automatic control device for the braiding density of covered yarn to implement the steps in any of the above-described method embodiments.

[0111] If the integrated unit 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, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the automatic control device for the braiding density of the silk-covered thread, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, such as a USB flash drive, a portable hard drive, a magnetic disk, or an optical disk.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] In the embodiments provided in this application, it should be understood that the disclosed automatic control device and method for the braiding density of covered yarn can be implemented in other ways. For example, the embodiments of the automatic control device for the braiding density of covered yarn described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for automatically controlling the braiding density of silk-covered yarn, characterized in that, include: The system acquires in real time the regional characteristics, weaving pitch, and weaving density of multiple areas of the cable to be braided; wherein the regional characteristics include tension and weaving angle, and the weaving pitch is the ratio of axial movement distance to the number of interlacing cycles; A first region is determined based on each braiding pitch and each braiding density; wherein, the multiple regions of the cable to be braided include the first region and multiple other regions, and the first region is the abnormal region of the cable to be braided that currently needs adjustment; A second region is obtained based on the characteristics of each of the aforementioned regions; wherein, the second region is an abnormal region in which the cable to be braided may need to be adjusted in the future; If the overall abnormality of the cable to be braided is determined based on the first region and the second region, the braiding pitch of each region of the cable to be braided is adjusted. If it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, a third region is determined from the first region that is after the second region along the braiding direction of the cable to be braided. Adjust the weaving pitch of the second region and the third region.

2. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, The process of obtaining the second region based on the characteristics of each region includes: Based on the regional characteristics of the first region, candidate regions are determined from among the other regions; For each candidate region, the probability that the candidate region is the second region is obtained based on the regional characteristics of the first region and the regional characteristics of the candidate region. The second region is determined based on the probability.

3. The automatic control method for braiding density of covered yarn as described in claim 2, characterized in that, For each candidate region, the probability that the candidate region is the second region is obtained based on the regional features of the first region and the regional features of the candidate region, including: A region feature pair is obtained based on the region features of the first region and the region features of each of the candidate regions; wherein, the region feature pair is used to reflect the association relationship between the first region and each of the candidate regions; The probability that each candidate region is the second region is obtained based on the region features.

4. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, Before adjusting the braiding pitch of each region of the cable to be braided in the event that the overall cable to be braided is abnormal based on the first region and the second region, the method further includes: A time series model is established based on the weaving density of the first region and the second region; wherein, the time series model is used to predict the weaving density of multiple regions of the cable to be woven at future times; The weaving density in each of the other regions following the second region is predicted based on the time series model, and it is determined whether the overall cable to be woven is abnormal.

5. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, When the overall abnormality of the cable to be braided is determined based on the first region and the second region, adjusting the braiding pitch of each region of the cable to be braided includes: When the overall anomaly of the cable to be braided is determined based on the first region and the second region, the optimal pitch combination is obtained by using a genetic algorithm with the goal of minimizing the deviation of each braiding density and minimizing the adjustment amount of each braiding pitch. The braiding pitch of each region of the cable to be braided is adjusted according to the optimal pitch combination.

6. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, The step of determining a third region following the second region along the braiding direction of the cable to be braided, based on the first region and the second region, when it is determined that the cable to be braided is not an overall abnormality, includes: If it is determined from the first region and the second region that the cable to be braided is not an overall abnormality, calculate the conditional probability that each of the other regions after the second region is abnormal under the condition that the first region is a braiding abnormality. The third region is determined based on the conditional probabilities described.

7. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, The adjustment of the weaving pitch in the second region and the third region includes: A transition section is defined after the second region; A transition section is determined before and after the third region; When the distance between the second region and the third region is greater than a preset threshold, the weaving pitch of the second region and the third region is gradually adjusted according to each transition segment.

8. The automatic control method for braiding density of covered yarn as described in claim 7, characterized in that, The method further includes: If the distance between the first region and the second region is less than or equal to the preset threshold, the transition segment after the first region and the transition segment before the second region are merged, and the weaving pitch is adjusted according to the merged transition segment.

9. The automatic control method for braiding density of covered yarn as described in claim 1, characterized in that, An automatic control device for the braiding density of covered yarn is provided, the device comprising a control unit and a covered yarn braiding machine, the braiding machine comprising a yarn feeding zone, a weaving zone, and a winding zone; the method further includes: Obtain a surface image of the cable to be braided; The texture uniformity is obtained by calculating the texture variance based on the surface image. The rotation speed of the interlacing zone is adjusted according to the texture uniformity.

10. The automatic control method for braiding density of covered yarn as described in claim 9, characterized in that, The real-time acquisition of regional characteristics, braiding pitch, and braiding density of multiple areas of the cable to be braided includes: The wire feeding speed of the wire feeding area is obtained in real time; Each braiding pitch is calculated based on the wire feeding speed and the adjusted rotational speed of the interlacing zone; Each of the described weaving densities is determined based on the described weaving pitch and the described surface image.

Images