Cable cabling control method based on process big data

Abstract

The invention discloses a cable cabling control method based on process big data, and belongs to the technical field of cable cabling control. Comprising the following steps that S1, parameters are collected in real time and preprocessed, historical batch records are called, the tension instability and the arrangement offset trend rate are calculated, simulation coupling factors are constructed, and an optimized coefficient combination is obtained; s2, the current tension instability and the arrangement offset change rate are calculated respectively, and the adjusted take-up speed and the adjusted twisting torque are obtained; according to the method, historical batch records of cables of the same specification are called, the tension instability and the arrangement offset trend rate are calculated, a numerical optimization algorithm is adopted to iteratively search an amplification coefficient and a sensitivity coefficient, a process-oriented optimization objective function is minimized, an optimized coefficient combination is obtained, and the stability of the cable tension is improved. Therefore, self-adaptive parameter optimization based on historical data is realized, the high adaptability of a control strategy to different batches of process fluctuations is improved, and the variability of quality indexes is remarkably reduced.

Description

Technical Field

[0001] This invention relates to a cable assembly control method based on big data of process technology, belonging to the field of cable assembly control technology. Background Technology

[0002] Cable cabling is a core step in wire and cable manufacturing. It primarily refers to the process of stranding multiple insulated or conductor cores into a round cable core according to specific directions, pitches, and rules. The purpose is to ensure the cable's shape, roundness, and stability. This is accompanied by auxiliary operations such as filling and binding to prevent the cable core from loosening and to improve overall mechanical properties. For multi-core power cables or control cables, the cabling process directly affects the cable's flexibility, bending resistance, electrical performance, and long-term operational reliability. Traditional cabling processes often use cage-type or fork-type cabling machines, achieving mechanical stranding. However, as cable products increasingly move towards higher voltage, larger cross-sections, and multi-functionality, the parameter control of the cabling process has become increasingly complex.

[0003] With the advancement of Industry 4.0 and digital transformation, the cable industry faces multiple pressures: capacity expansion, quality improvement, and cost control. The pain points of traditional cable-making processes are becoming increasingly prominent: siloed production data prevents end-to-end traceability; delayed parameter adjustments lead to increased scrap and rework; and heavy reliance on manual labor results in rising labor costs and poor consistency. Traditional control methods struggle to meet the precision requirements of high-performance cables (such as robotic cables or submarine cables). Meanwhile, emerging applications such as new energy vehicles, 5G communications, and clean energy pose new challenges to the flexibility, bending resistance, and intelligence of cables, making traditional methods inadequate.

[0004] Cable cabling control methods based on big data in process technology have emerged in response to current trends. By integrating the Internet of Things, sensors, and big data platforms, they dynamically optimize process parameters and make intelligent decisions, shifting from experience-based to data-driven approaches. This effectively solves problems such as poor stability, low efficiency, and large quality fluctuations in traditional processes, improving the electrical performance, mechanical strength, and service life of cable products, and providing support for intelligent manufacturing and the construction of the energy internet. In the current wave of digital transformation in the cable industry, this approach has broad application prospects and will undoubtedly drive the industry towards high-end and intelligent development.

[0005] However, the existing control methods mentioned above still have some problems, such as poor quality stability in the cable production process, difficulty in ensuring the integrity and consistency of finished products, resulting in low product qualification rate and high scrap rate. Traditional production relies on manual experience to adjust process parameters, lacks data-driven intelligent means, and it is difficult to achieve a dynamic balance between production efficiency and quality. The production process is complicated by multi-parameter coupling disturbances, and existing control methods are difficult to suppress uneven tension and core alignment deviation in real time and in a coordinated manner. Summary of the Invention

[0006] To address the shortcomings of the existing technology, this invention provides a cable cabling control method based on big data of process data.

[0007] The technical solution of the present invention to solve the above-mentioned technical problems is as follows:

[0008] A cable cabling control method based on process big data includes the following steps:

[0009] S1. Real-time acquisition of process parameters and data preprocessing to obtain preprocessed process parameters. Calling historical batch records of cables of the same specification, calculating tension instability and arrangement offset trend rate for each historical batch, constructing a simulated coupling factor, iteratively searching for amplification coefficient and sensitivity coefficient through numerical optimization algorithm to minimize the process-oriented optimization objective function, obtaining the optimized coefficient combination, and analyzing and processing it through statistical methods to obtain the winding speed adjustment gain and stranding torque adjustment gain.

[0010] S2. Based on the pre-processed process parameters, calculate the tension instability and arrangement offset change rate at the current moment. Combine the optimized tension instability amplification coefficient and the optimized arrangement trend sensitivity coefficient to construct the coupling control factor used in real-time control. Further combine the current take-up speed and stranding torque to obtain the adjusted take-up speed and adjusted stranding torque.

[0011] Furthermore, the process parameters acquired in S1 are continuously acquired in real time through sensors deployed on the cable production line. Specifically, this includes: independent tension sensors on each pay-off reel acquiring the instantaneous tension value of each wire core; a vision camera or laser displacement sensor performing two-dimensional imaging or scanning of the stranding zone cores to obtain the lateral and longitudinal alignment offsets of each wire core; the encoder built into the take-up frequency converter providing feedback on the current take-up speed; and the servo motor driver of the winch providing feedback on the stranding torque.

[0012] The data preprocessing in S1 for the acquired process parameters involves preprocessing the acquired process parameters, including time synchronization through timestamp alignment and normalization of the process parameters using the maximum-minimum normalization method, to obtain the preprocessed process parameters, including the instantaneous tension value of each wire core after preprocessing. , , The number of wire cores, and the lateral offset of each wire core after preprocessing. and vertical arrangement offset Pre-processed take-up speed Pre-treated stranding torque ;

[0013] The historical batch records of cables of the same specification retrieved in S1 are retrieved from the enterprise production management systems of MES and ERP, including complete process parameter time-series data and quality indicators of each previous batch, forming a structured historical dataset. , Indicates the historical batch number. , This indicates the total number of historical batches. This serves as a time marker for the process parameters of historical batches. Indicates the first The first historical batch The core wire at a historical moment tension, and They represent the first The first historical batch The core wire at a historical moment The horizontal and vertical arrangement offset, Indicates the first Each historical batch at a historical moment The speed of receiving the line, Indicates the first Each historical batch at a historical moment The twisting torque, For the first The quality index of each historical batch is used; the smaller the value, the better the quality.

[0014] In step S1, the tension instability is calculated for each historical batch. This involves first calculating the average tension of each core at all historical moments within the batch, then calculating the sum of squares of the deviations of the tension of each core relative to the average tension at each historical moment, and finally taking the square root of the average. The formula is expressed as follows:

[0015] ;

[0016] in, Indicates the first Tension instability of a historical batch; Represents the square root, used to convert variance to standard deviation; The normalization factor is represented by averaging all squared deviation terms and converting them into variance form for variance calculation. Indicates the first Number of historical moments within each historical batch; Indicates the first Each historical batch at a historical moment No. The tension of the first core wire and the first Deviation of average tension in historical batches; Indicates the first The average tension of each historical batch.

[0017] Furthermore, in S1, the rate of arrangement offset trend is calculated for each historical batch. First, the arrangement offset amplitude of each historical batch at each historical moment is calculated. That is, for all historical moments within the batch, the sum of the square of the horizontal offset and the square of the vertical offset of each wire core is calculated. Then, the average of all wire cores is taken and the square root is taken. Next, the difference in arrangement offset amplitude between adjacent historical moments is divided by the historical time interval to obtain the rate of change of arrangement offset at each historical moment. Finally, the average of the rate of change of arrangement offset at all historical moments of the entire historical batch is taken. The formula is expressed as follows:

[0018] ;

[0019] in, Indicates the first The rate of trend of arrangement offset of each historical batch; This indicates the difference in offset magnitude between adjacent time points; Indicates a historical time interval; Indicates the first Each historical batch at a historical moment The rate of change of arrangement offset; Indicates the first Each historical batch at a historical moment The formula for calculating the arrangement offset is as follows: ; Indicates the first Each historical batch at a historical moment The magnitude of the arrangement offset.

[0020] Furthermore, in S1, the construction of the simulated coupling factor involves, for each historical batch, using the current amplification coefficient and sensitivity coefficient to be optimized, performing basic amplification on the tension instability characteristics of the historical batch, then combining it with the positive part of the historical batch arrangement offset trend rate characteristics for exponential amplification, and finally using the hyperbolic tangent function for saturation constraint to obtain a simulated coupling factor between -1 and 1. The simulated coupling factor is structurally identical to the coupling control factor used in real-time control, thereby ensuring that the optimization results can be seamlessly transferred to online applications.

[0021] The formula for calculating the simulated coupling factor is:

[0022] ;

[0023] in, Indicates the first The simulation coupling control factor for each historical batch depends on the parameter to be optimized. Build it; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. This represents the amplification factor for the tension instability to be optimized; This represents the sensitivity coefficient to the arrangement trend to be optimized; This represents an exponential function, achieving a trend amplification effect. This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends.

[0024] Furthermore, the optimization objective function in S1 finds the maximum and minimum values ​​from the quality indicators of all historical batches to form the upper and lower bounds of quality prediction. It linearly maps the simulation coupling factor of each historical batch so that the range of simulation coupling factor from -1 to 1 corresponds to the interval from the minimum to the maximum quality value. That is, the larger the simulation coupling factor, the worse the predicted quality indicator. It calculates the squared difference between the quality indicator of each historical batch and the predicted quality indicator, and sums the squared differences of all historical batches to form a process-oriented optimization objective function.

[0025] The amplification factor and sensitivity factor are iteratively searched using a numerical optimization algorithm to minimize the process-oriented optimization objective function. After optimization convergence, the optimized coefficient combination is obtained, including the tension instability amplification factor and the optimized arrangement trend sensitivity factor, expressed by the following formula:

[0026] ;

[0027] in, This represents the optimized tension instability amplification factor; This represents the sensitivity coefficient to the optimized arrangement trend. These represent the instability amplification factor and the arrangement trend sensitivity factor, which minimize the process-oriented optimization objective function. This represents the sum of the squared differences of all historical batches; This indicates the worst quality in history; Indicates the best quality in history; This means mapping the range of values ​​from (-1, 1) to (0, 1); This indicates the predicted quality indicators.

[0028] Furthermore, based on the take-up speed, stranding torque, and quality indicators from historical batches, statistical methods, such as the Pearson correlation coefficient, are used to obtain the take-up speed adjustment gain described in S1 by taking the absolute value of the Pearson correlation coefficient. With twist torque adjustment gain .

[0029] Furthermore, based on the pre-processed process parameters, the average tension of all pre-processed wire cores is calculated. The squared deviation of the tension of each pre-processed wire core from the average value is then calculated. The square root of the average of all squared deviations is then taken to obtain the tension instability at the current moment, as described in S2. A larger tension instability value at the current moment indicates a more significant tension difference between wire cores. The calculation formula is as follows:

[0030] ;

[0031] in, Indicates time Tension instability; Represents the square root; Indicates time The first after preprocessing Root core tension and time The deviation of the average value of all pre-treated core tensions; Indicates time The average tension of all pre-treated wire cores.

[0032] Furthermore, based on the preprocessed process parameters, the square of the lateral and longitudinal arrangement offsets of each wire core are summed, then averaged over all wire cores, and the square root is taken to obtain the real-time arrangement offset amplitude. The difference in arrangement offset amplitudes between adjacent moments is then divided by the time interval to obtain the arrangement offset change rate at the current moment, as described in S2. The calculation formula is as follows:

[0033] ;

[0034] in, Indicates time The rate of change of arrangement offset; Indicates time The formula for calculating the arrangement offset is as follows: ; Indicates time The magnitude of the arrangement offset; This indicates the time interval between adjacent moments.

[0035] Furthermore, the coupling control factor in S2 is calculated using the following formula:

[0036] ;

[0037] in, Indicates the coupling control factor; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. Indicates time Tension instability; This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends.

[0038] Furthermore, the current take-up speed and twisting torque are obtained as reference values. The coupling control factor is multiplied by the take-up speed adjustment gain and the twisting torque adjustment gain respectively to form their respective adjustment ranges. For the adjustment of the take-up speed, a subtraction direction is used, that is, 1 is subtracted from the adjustment range of the take-up speed and then multiplied by the current take-up speed. For the adjustment of the twisting torque, an addition direction is used, that is, 1 is added to the adjustment range of the twisting torque and then multiplied by the current twisting torque. The adjusted take-up speed and the adjusted twisting torque are obtained respectively, and simultaneously sent to the actuator to achieve real-time linkage adjustment: when At this time, reduce the winding speed and increase the torque to enhance the twisting tightness and suppress the alignment deviation. At the same time, appropriately increasing the speed and reducing the torque can improve production efficiency;

[0039] The formula for calculating the adjusted take-up speed in S2 is as follows:

[0040] ;

[0041] in, This indicates the adjusted take-up speed; This indicates the take-up speed after preprocessing; Indicates the gain for adjusting the take-up speed;

[0042] The formula for calculating the adjusted twisting torque in S2 is as follows:

[0043] ;

[0044] in, This indicates the adjusted twisting torque; This indicates the twisting torque after pretreatment; This indicates the gain of the twist torque adjustment.

[0045] The beneficial effects of this invention are:

[0046] 1. By calling historical batch records of cables of the same specification and calculating tension instability and arrangement offset trend rate, a simulated coupling factor was constructed. A numerical optimization algorithm was used to iteratively search for amplification coefficient and sensitivity coefficient to minimize the process-oriented optimization objective function and obtain the optimized coefficient combination. This achieved adaptive parameter optimization based on historical data, improved the high adaptability of the control strategy to process fluctuations of different batches, and significantly reduced the variability of quality indicators.

[0047] 2. By analyzing historical batch data using statistical methods, the gain for adjusting the take-up speed and the gain for adjusting the stranding torque were obtained, realizing the scientific quantification of the adjustment parameters, avoiding the blindness of traditional experience-based adjustments, and improving the adjustment accuracy and stability.

[0048] 3. By calculating the real-time coupling control factor, the coupled quantitative control of the deterioration trend of tension non-uniformity and arrangement offset is realized, and the early warning and strong response capability for potential instability are obtained. This effectively suppresses the core pushing or loosening phenomenon caused by tension non-uniformity, significantly improves the roundness and consistency of the cable core, and reduces the occurrence rate of quality defects such as ellipticity.

[0049] 4. By simultaneously adjusting the take-up speed and stranding torque, the system automatically reduces the speed and increases the torque to enhance stranding tightness when disturbances increase, and moderately increases the speed and decreases the torque to improve production efficiency when the system is stable. This achieves a dynamic balance between quality and efficiency, improves the overall stability and production efficiency of the cable forming process, reduces scrap rate and energy consumption, and enhances the intelligence level and economic benefits of the production process. Attached Figure Description

[0050] 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 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.

[0051] Figure 1 This is a flowchart of a cable cabling control method based on process big data according to the present invention. Detailed Implementation

[0052] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0053] See attached document Figure 1 The diagram illustrates a flowchart of a cable cabling control method based on process big data, according to an embodiment of the present invention. The method includes the following steps:

[0054] S1. Real-time acquisition of process parameters and data preprocessing to obtain preprocessed process parameters. Historical batch records of cables of the same specification are called up, and tension instability and arrangement offset trend rate are calculated for each historical batch. A simulated coupling factor is constructed, and the amplification coefficient and sensitivity coefficient are iteratively searched through numerical optimization algorithm to minimize the process-oriented optimization objective function and obtain the optimized coefficient combination. The winding speed adjustment gain and stranding torque adjustment gain are obtained through statistical analysis and processing.

[0055] During the data acquisition phase, sensors deployed on the cable production line enable continuous real-time acquisition of process parameters. Specifically, this includes: independent tension sensors on each pay-off reel acquiring the instantaneous tension value of each wire core; visual cameras or laser displacement sensors performing two-dimensional imaging or scanning of the stranding zone cores to obtain the lateral and longitudinal alignment offsets of each core; the encoder built into the take-up inverter providing feedback on the current take-up speed; and the winding cage servo motor driver providing feedback on the stranding torque. The acquired process parameters undergo data preprocessing, including time synchronization through timestamp alignment and normalization using the maximum-minimum normalization method, resulting in preprocessed process parameters, including the preprocessed instantaneous tension value of each wire core. , , The number of wire cores, and the lateral offset of each wire core after preprocessing. and vertical arrangement offset Pre-processed take-up speed Pre-treated stranding torque .

[0056] From the enterprise's production management system, including MES and ERP, historical batch records of cables of the same specification are retrieved, including complete process parameter time-series data and quality indicators such as roundness or ellipticity for each previous batch, forming a structured historical dataset. , Indicates the historical batch number. , This indicates the total number of historical batches. This serves as a time marker for the process parameters of historical batches. Indicates the first The first historical batch The core wire at a historical moment tension, and They represent the first The first historical batch The core wire at a historical moment The horizontal and vertical arrangement offset, Indicates the first Each historical batch at a historical moment The speed of receiving the line, Indicates the first Each historical batch at a historical moment The twisting torque, For the first For historical batch quality indicators, such as roundness or ellipticity, the smaller the value, the better the quality.

[0057] Calculate the tension instability and arrangement offset trend rate for each historical batch;

[0058] The tension instability of each historical batch is calculated by first determining the average tension of each core at all historical moments within the batch, then calculating the sum of squares of the deviations of the tension of each core relative to the average tension at each historical moment, and finally taking the square root of the average. The formula is expressed as follows:

[0059] ;

[0060] in, Indicates the first Tension instability of a historical batch; Represents the square root, used to convert variance to standard deviation; The normalization factor is represented by averaging all squared deviation terms and converting them into variance form for variance calculation. Indicates the first Number of historical moments within each historical batch; Indicates the first Each historical batch at a historical moment No. The tension of the first core wire and the first Deviation of average tension in historical batches; Indicates the first Average tension of historical batches;

[0061] The rate of arrangement offset trend for each historical batch is calculated by first determining the arrangement offset amplitude at each historical moment within each historical batch. This involves summing the squares of the horizontal and vertical offsets of each wire core for all historical moments within the batch, then averaging the results across all wire cores and taking the square root. Furthermore, the difference in arrangement offset amplitude between adjacent historical moments is divided by the historical time interval to calculate the rate of change of arrangement offset at each historical moment. Finally, the average rate of change of arrangement offset across all historical moments in the entire historical batch is taken. The formula is expressed as follows:

[0062] ;

[0063] in, Indicates the first The rate of trend of arrangement offset of each historical batch; This indicates the difference in offset magnitude between adjacent time points; Indicates a historical time interval; Indicates the first Each historical batch at a historical moment The rate of change of arrangement offset; Indicates the first Each historical batch at a historical moment The formula for calculating the arrangement offset is as follows: ; Indicates the first Each historical batch at a historical moment The magnitude of the arrangement offset;

[0064] Based on the tension instability and arrangement offset trend rate of each historical batch, a simulated coupling factor is constructed. For each historical batch, the tension instability characteristics of the historical batch are amplified on a basic basis using the amplification coefficient and sensitivity coefficient to be optimized, and then exponentially amplified by combining the positive part of the arrangement offset trend rate characteristics of the historical batch. Finally, a saturation constraint is applied through the hyperbolic tangent function to obtain a simulated coupling factor between -1 and 1. The simulated coupling factor is structurally identical to the coupling control factor used in real-time control, thereby ensuring that the optimization results can be seamlessly transferred to online applications.

[0065] The formula for calculating the simulated coupling factor is:

[0066] ;

[0067] in, Indicates the first The simulation coupling control factor for each historical batch depends on the parameter to be optimized. Build it; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. This represents the amplification factor for the tension instability to be optimized; This represents the sensitivity coefficient to the arrangement trend to be optimized; This represents an exponential function, achieving a trend amplification effect. This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends;

[0068] A process-oriented optimization objective function is constructed. The maximum and minimum values ​​of quality indicators from all historical batches are identified to form the upper and lower bounds for quality prediction. The simulated coupling factor for each historical batch is linearly mapped so that the range of the simulated coupling factor from -1 to 1 corresponds to the interval from the minimum to the maximum quality value. That is, a larger simulated coupling factor maps to a worse predicted quality indicator. The squared difference between the quality indicator and the predicted quality indicator for each historical batch is calculated, and the squared differences for all historical batches are summed to form the process-oriented optimization objective function. The design of the process-oriented optimization objective function fully considers the physical laws of cable cabling: uneven tension is the fundamental disturbance, and the deterioration trend of the arrangement will exponentially amplify the overall instability, leading to a significant decrease in quality. Numerical optimization algorithms, such as the Levenberg-Marquardt algorithm or the trust domain algorithm, are used to iteratively search for the amplification coefficient and sensitivity coefficient to minimize the process-oriented optimization objective function. After optimization convergence, the optimized coefficient combination is obtained, including the tension instability amplification coefficient and the optimized arrangement trend sensitivity coefficient, expressed by the following formula:

[0069] ;

[0070] in, This represents the optimized tension instability amplification factor; This represents the sensitivity coefficient to the optimized arrangement trend. These represent the instability amplification factor and the arrangement trend sensitivity factor, which minimize the process-oriented optimization objective function. This represents the sum of the squared differences of all historical batches; This indicates the worst quality in history; Indicates the best quality in history; This means mapping the range of values ​​from (-1, 1) to (0, 1); Indicates the predicted quality indicators;

[0071] Based on the take-up speed, stranding torque, and quality indicators from historical batches, statistical methods, such as the Pearson correlation coefficient, are used, and the absolute value of the Pearson correlation coefficient is taken to obtain the take-up speed adjustment gain. With twist torque adjustment gain .

[0072] S2. Based on the pre-processed process parameters, calculate the tension instability and arrangement offset change rate at the current moment. Combine the optimized tension instability amplification coefficient and the optimized arrangement trend sensitivity coefficient to construct the coupling control factor used in real-time control. Further combine the current take-up speed and stranding torque to obtain the adjusted take-up speed and adjusted stranding torque.

[0073] Based on the pre-processed process parameters, the average tension of all pre-processed wire cores is calculated. Then, the squared deviation of the tension of each pre-processed wire core from the average is calculated. The square root of the average of all squared deviations is then taken to obtain the tension instability at the current moment. A larger tension instability value at the current moment indicates a more significant tension difference between wire cores. The calculation formula is as follows:

[0074] ;

[0075] in, Indicates time Tension instability; Represents the square root; Indicates time The first after preprocessing Root core tension and time The deviation of the average value of all pre-treated core tensions; Indicates time The average value of all pre-treated wire core tensions;

[0076] When the tension of all pre-processed cores is close to the average value, the real-time tension instability is close to zero, indicating that it is in a balanced state with good tension uniformity and minimal disturbance to the core arrangement. When the tension of individual cores deviates significantly from the average value, the real-time tension instability increases rapidly, reflecting a high degree of dispersion in tension distribution, which may lead to cores pushing each other or loosening, thereby accelerating the arrangement shift.

[0077] Based on the preprocessed process parameters, the square of the lateral and longitudinal arrangement offsets of each wire core are summed, then averaged over all wire cores, and the square root is taken to obtain the real-time arrangement offset amplitude. Furthermore, the arrangement offset change rate at the current moment is obtained by dividing the difference in arrangement offset amplitudes between adjacent moments by the time interval. The calculation formula is as follows:

[0078] ;

[0079] in, Indicates time The rate of change of arrangement offset; Indicates time The formula for calculating the arrangement offset is as follows: ; Indicates time The magnitude of the arrangement offset; Indicates the time interval between adjacent moments;

[0080] To comprehensively reflect the coupling degree between uneven tension and the worsening trend of core alignment deviation during cable cabling, a coupling control factor for real-time control is constructed based on the current tension instability and alignment deviation change rate, combined with the optimized tension instability amplification coefficient and the optimized alignment trend sensitivity coefficient.

[0081] Specifically, the current arrangement offset change rate is processed on a one-sided basis, retaining only the positive value. That is, only the worsening trend of increasing offset amplitude is considered, while the negative trend of improvement is ignored. One-sided processing avoids unnecessary over-intervention during self-recovery. The current arrangement offset change rate after one-sided processing is amplified as the exponential part of an exponential function. The smaller the current arrangement offset change rate, the more moderate the amplification effect. Once the current arrangement offset change rate increases significantly, the amplification effect will increase exponentially, thereby achieving early warning and strong response to potential rapid instability. The strength of the exponential function amplification effect is controlled by the optimized arrangement trend sensitivity coefficient, which is suitable for production lines with relatively stable processes and avoids frequent adjustments due to oversensitivity, which would affect production continuity.

[0082] The fundamental contribution of tension instability to the coupling control factor used in real-time control is controlled by the optimized tension instability amplification factor.

[0083] The hyperbolic tangent function is introduced for saturation constraint to ensure that the output is bounded, thereby preventing oscillations or instability caused by over-adjustment. At the same time, the output is negative when stable, which supports a moderate increase in production efficiency under stable conditions.

[0084] The formula for calculating the coupling control factor is:

[0085] ;

[0086] in, Indicates the coupling control factor; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. Indicates time Tension instability; This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends;

[0087] Based on the winding speed adjustment gain, stranding torque adjustment gain, and coupling control factor, a dual-parameter synchronous linkage adjustment of winding speed and stranding torque is achieved through a winding speed and stranding torque linkage adjustment algorithm, thereby achieving coordinated and stable control of tension and core arrangement during cable cabling.

[0088] The current take-up speed and twisting torque are used as reference values. The coupling control factor is multiplied by the take-up speed adjustment gain and the twisting torque adjustment gain respectively to form their respective adjustment ranges. For the adjustment of the take-up speed, a subtraction direction is used, that is, 1 is subtracted from the take-up speed adjustment range and then multiplied by the current take-up speed. For the adjustment of the twisting torque, an addition direction is used, that is, 1 is added to the twisting torque adjustment range and then multiplied by the current twisting torque. The adjusted take-up speed and the adjusted twisting torque are obtained respectively, and simultaneously sent to the actuator to achieve real-time linkage adjustment: when At this time, reduce the winding speed and increase the torque to enhance the twisting tightness and suppress the alignment deviation. When the system stabilizes, increase the speed appropriately and reduce the torque to improve production efficiency.

[0089] The formula for calculating the adjusted take-up speed is:

[0090] ;

[0091] in, This indicates the adjusted take-up speed; This indicates the take-up speed after preprocessing; Indicates the gain for adjusting the take-up speed;

[0092] The formula for calculating the adjusted twisting torque is:

[0093] ;

[0094] in, This indicates the adjusted twisting torque; This indicates the twisting torque after pretreatment; Indicates the gain of the twisting torque adjustment;

[0095] Real-time linkage adjustment of winding speed and stranding torque can quickly suppress the dual disturbances of uneven tension and arrangement deviation, improve the roundness and consistency of the cable core, and moderately increase the production speed when stable, thus achieving a dynamic balance between quality and efficiency.

[0096] By calling historical batch records of cables of the same specification and calculating tension instability and arrangement offset trend rate, a simulated coupling factor was constructed. A numerical optimization algorithm was then used to iteratively search for the amplification coefficient and sensitivity coefficient, minimizing the process-oriented optimization objective function and obtaining the optimized coefficient combination. This achieved adaptive parameter optimization based on historical data, improving the control strategy's high adaptability to process fluctuations in different batches and significantly reducing the variability of quality indicators. Statistical analysis of historical batch data yielded the gain for winding speed adjustment and stranding torque adjustment, achieving scientific quantification of adjustment parameters, avoiding the blindness of traditional experience-based adjustments, and improving adjustment accuracy and stability. Real-time calculation of the coupling control factor was used to achieve… The coupled quantitative control of tension unevenness and the deteriorating trend of arrangement misalignment has achieved early warning and strong response capability for potential instability, thus effectively suppressing the core pushing or loosening caused by tension unevenness, significantly improving the roundness and consistency of the cable core, and reducing the incidence of quality defects such as ellipticity. By synchronously adjusting the take-up speed and stranding torque, the system can automatically reduce the speed and increase the torque to enhance stranding tightness when the disturbance increases, and moderately increase the speed and decrease the torque to improve production efficiency when the system is stable. This achieves a dynamic balance between quality and efficiency, improving the overall stability and production efficiency of the cable forming process, reducing scrap rate and energy consumption, and enhancing the intelligence level and economic benefits of the production process.

[0097] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cable cabling control method based on process big data, characterized in that, Includes the following steps, S1. Real-time acquisition of process parameters and data preprocessing to obtain preprocessed process parameters. Calling historical batch records of cables of the same specification, calculating tension instability and arrangement offset trend rate for each historical batch, constructing a simulated coupling factor, iteratively searching for amplification coefficient and sensitivity coefficient through numerical optimization algorithm to minimize the process-oriented optimization objective function, obtaining the optimized coefficient combination, and analyzing and processing it through statistical methods to obtain the winding speed adjustment gain and stranding torque adjustment gain. S2. Based on the pre-processed process parameters, calculate the tension instability and arrangement offset change rate at the current moment. Combine the optimized tension instability amplification coefficient and the optimized arrangement trend sensitivity coefficient to construct the coupling control factor used in real-time control. Further combine the current take-up speed and stranding torque to obtain the adjusted take-up speed and adjusted stranding torque.

2. The cable cabling control method based on process big data according to claim 1, characterized in that: The process parameters acquired in S1 are continuously acquired in real time through sensors deployed on the cable production line. Specifically, this includes: independent tension sensors on each pay-off reel acquiring the instantaneous tension value of each wire core; a vision camera or laser displacement sensor performing two-dimensional imaging or scanning of the stranding zone cores to obtain the lateral and longitudinal alignment offsets of each wire core; the encoder built into the take-up frequency converter providing feedback on the current take-up speed; and the servo motor driver of the winch providing feedback on the stranding torque. The data preprocessing in S1 for the acquired process parameters involves preprocessing the acquired process parameters, including time synchronization through timestamp alignment and normalization of the process parameters using the maximum-minimum normalization method, to obtain the preprocessed process parameters, including the instantaneous tension value of each wire core after preprocessing. , , The number of wire cores, and the lateral offset of each wire core after preprocessing. and vertical arrangement offset Pre-processed take-up speed Pre-treated stranding torque ; The historical batch records of cables of the same specification retrieved in S1 are retrieved from the enterprise production management systems of MES and ERP, including complete process parameter time-series data and quality indicators of each previous batch, forming a structured historical dataset. , Indicates the historical batch number. , This indicates the total number of historical batches. This serves as a time marker for the process parameters of historical batches. Indicates the first The first historical batch The core wire at a historical moment tension, and They represent the first The first historical batch The core wire at a historical moment The horizontal and vertical arrangement offset, Indicates the first A historical batch at a historical moment The speed of receiving the line, Indicates the first A historical batch at a historical moment The twisting torque, For the first The quality index of each historical batch is such that the smaller the value, the better the quality. The calculation of tension instability for each historical batch in S1 involves first determining the average tension of each core at all historical moments within the batch, then calculating the sum of squares of the deviations of the tension of each core relative to the average tension at each historical moment, and finally taking the square root of the average. The formula is expressed as follows: ； in, Indicates the first Tension instability of a historical batch; Represents the square root, used to convert variance to standard deviation; The normalization factor is represented by averaging all squared deviation terms and converting them into variance form for variance calculation. Indicates the first Number of historical moments within each historical batch; Indicates the first A historical batch at a historical moment No. The tension of the first core wire and the first Deviation of average tension in historical batches; Indicates the first The average tension of each historical batch.

3. The cable cabling control method based on process big data according to claim 2, characterized in that: In step S1, the arrangement offset trend rate is calculated for each historical batch. First, the arrangement offset amplitude of each historical batch at each historical moment is calculated. That is, for all historical moments within the batch, the sum of the square of the horizontal offset and the square of the vertical offset of each wire core is calculated. Then, the average of all wire cores is taken and the square root is taken. Further, the difference in arrangement offset amplitude between adjacent historical moments is divided by the historical time interval to obtain the arrangement offset change rate at each historical moment. Finally, the average of the arrangement offset change rates for all historical moments of the entire historical batch is taken. The formula is expressed as follows: ； in, Indicates the first The rate of trend of arrangement offset of each historical batch; This indicates the difference in offset magnitude between adjacent time points; Indicates a historical time interval; Indicates the first A historical batch at a historical moment The rate of change of arrangement offset; Indicates the first A historical batch at a historical moment The formula for calculating the arrangement offset is as follows: ; Indicates the first A historical batch at a historical moment The magnitude of the arrangement offset.

4. The cable cabling control method based on process big data according to claim 3, characterized in that: In S1, the simulated coupling factor is constructed by using the current amplification coefficient and sensitivity coefficient to amplify the tension instability characteristics of the historical batch for each historical batch. Then, it is exponentially amplified by combining the positive part of the historical batch arrangement offset trend rate characteristics. Finally, it is saturated by the hyperbolic tangent function to obtain a simulated coupling factor between -1 and 1. The simulated coupling factor is structurally identical to the coupling control factor used in real-time control, thereby ensuring that the optimization results can be seamlessly transferred to online applications. The formula for calculating the simulated coupling factor is: ； in, Indicates the first The simulation coupling control factor for each historical batch depends on the parameter to be optimized. Build it; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. This represents the amplification factor for the tension instability to be optimized; This represents the sensitivity coefficient to the arrangement trend to be optimized; This represents an exponential function, achieving a trend amplification effect. This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends.

5. The cable cabling control method based on process big data according to claim 4, characterized in that: The optimization objective function in S1 finds the maximum and minimum values ​​of the quality indicators of all historical batches to form the upper and lower bounds of quality prediction. It linearly maps the simulation coupling factor of each historical batch so that the range of simulation coupling factor from -1 to 1 corresponds to the interval from the minimum to the maximum quality value. That is, the larger the simulation coupling factor, the worse the predicted quality indicator. It calculates the squared difference between the quality indicator of each historical batch and the predicted quality indicator, and sums the squared differences of all historical batches to form the process-oriented optimization objective function. The amplification factor and sensitivity factor are iteratively searched using a numerical optimization algorithm to minimize the process-oriented optimization objective function. After optimization convergence, the optimized coefficient combination is obtained, including the tension instability amplification factor and the optimized arrangement trend sensitivity factor, expressed by the following formula: ； in, This represents the optimized tension instability amplification factor; This represents the sensitivity coefficient to the optimized arrangement trend. These represent the instability amplification factor and the arrangement trend sensitivity factor, which minimize the process-oriented optimization objective function. This represents the sum of the squared differences of all historical batches; This indicates the worst quality in history; Indicates the best quality in history; This means mapping the range of values ​​from (-1, 1) to (0, 1); This indicates the predicted quality indicators.

6. The cable cabling control method based on process big data according to claim 5, characterized in that: Based on the take-up speed, stranding torque, and quality indicators from historical batches, statistical methods, such as the Pearson correlation coefficient, are used, and the absolute value of the Pearson correlation coefficient is taken to obtain the take-up speed adjustment gain described in S1. With twist torque adjustment gain .

7. The cable cabling control method based on process big data according to claim 6, characterized in that: Based on the pre-processed process parameters, the average tension of all pre-processed wire cores is calculated. Then, the squared deviation of the tension of each pre-processed wire core relative to the average is calculated. The square root of the average of all squared deviations is then taken to obtain the tension instability at the current moment, as described in S2. A larger tension instability value at the current moment indicates a more significant difference in tension between wire cores. The calculation formula is as follows: ； in, Indicates time Tension instability; Represents the square root; Indicates time The first after preprocessing Root core tension and time The deviation of the average value of all pre-treated core tensions; Indicates time The average tension of all pre-treated wire cores.

8. The cable cabling control method based on process big data according to claim 7, characterized in that: Based on the preprocessed process parameters, the square of the lateral and longitudinal arrangement offsets of each wire core are summed, then averaged over all wire cores, and the square root is taken to obtain the real-time arrangement offset amplitude. Furthermore, the difference in arrangement offset amplitudes between adjacent moments is divided by the time interval to obtain the arrangement offset change rate at the current moment, as described in S2. The calculation formula is as follows: ； in, Indicates time The rate of change of arrangement offset; Indicates time The formula for calculating the arrangement offset is as follows: ; Indicates time The magnitude of the arrangement offset; This indicates the time interval between adjacent moments.

9. The cable cabling control method based on process big data according to claim 8, characterized in that: The coupling control factor in S2 is calculated using the following formula: ； in, Indicates the coupling control factor; This represents the hyperbolic tangent function, used for saturation constraints to ensure that the output is bounded. Indicates time Tension instability; This indicates that only positive deteriorating trends are considered, in order to avoid excessive suppression of benign trends.

10. The cable cabling control method based on process big data according to claim 9, characterized in that: The current take-up speed and twisting torque are used as reference values. The coupling control factor is multiplied by the take-up speed adjustment gain and the twisting torque adjustment gain respectively to form their respective adjustment ranges. For the take-up speed adjustment, a subtraction direction is used, that is, 1 is subtracted from the take-up speed adjustment range and then multiplied by the current take-up speed. For the twisting torque adjustment, an addition direction is used, that is, 1 is added to the twisting torque adjustment range and then multiplied by the current twisting torque. The adjusted take-up speed and adjusted twisting torque are obtained respectively, and simultaneously sent to the actuator to achieve real-time linkage adjustment: when At this time, reduce the winding speed and increase the torque to enhance the twisting tightness and suppress the alignment deviation. At the same time, appropriately increasing the speed and reducing the torque can improve production efficiency; The formula for calculating the adjusted take-up speed in S2 is as follows: ； in, This indicates the adjusted take-up speed; This indicates the take-up speed after preprocessing; Indicates the gain for adjusting the take-up speed; The formula for calculating the adjusted twisting torque in S2 is as follows: ； in, This indicates the adjusted twisting torque; This indicates the twisting torque after pretreatment; This indicates the gain of the twist torque adjustment.

Images