Full-line automatic linkage cable production method and system

Abstract

The invention relates to the technical field of cable manufacturing, in particular to a full-line automatic linkage cable production method and system, and the method comprises the steps: carrying out the data collection of the operation data of a plurality of process nodes on a cable automatic production line; performing extreme value feature extraction on the operation data to construct a minimum value feature array and a maximum value feature array; whether synchronization abnormity occurs or not is judged in real time according to the minimum value feature array and the maximum value feature array, and if the synchronization abnormity occurs, dispatching control is conducted on the cable automatic production line to eliminate the synchronization abnormity; according to the method, whether synchronization abnormity exists or not can be rapidly judged with the minimum computing power, linear speed deviation caused by process slowdown can be rapidly recognized, the algorithm complexity is low, the duration of the synchronization abnormity can be accurately recognized, the recognition precision of the synchronization abnormity under the condition that the duration of the ascending time period and the descending time period is short is improved, and the recognition efficiency of the synchronization abnormity is improved. And the problem of cable product quality caused by rapid accumulation of linear velocity deviation is avoided.

Description

Technical Field

[0001] This invention relates to the fields of automated cable processing technology and digital twin technology, specifically to a fully automated, interconnected cable production method and system. Background Technology

[0002] The entire cable production line is gradually shifting towards full automation and intelligence. The entire cable production process is a complex multi-stage and multi-process node process, involving material processing, conductor manufacturing, untwisting and stranding, insulation / sheath forming, and other stages. The main stages of cable production can be divided into the following steps: First, raw material preparation is completed, including the drawing and annealing of copper or aluminum rods, as well as the proportioning of plastic granules and shielding materials; next, conductor processing is carried out, which improves flexibility through multi-strand stranding and compaction to reduce conductor gaps; then, the insulation / sheath extrusion stage is entered, where molten plastic is used to coat the conductor using a high-temperature extrusion process, and then cooled and shaped by a water-cooling tank; then, the cabling stage is carried out, where multiple insulated cores are stranded into a cable core in a star or layer stranding manner, and filler material and shielding armor layer are added; next, the outer sheath is extruded; finally, packaging and storage are carried out, and the cable is coiled or reeled by an automatic take-up machine.

[0003] In the continuous production line of wires and cables, the cable manufacturing adopts the "long-length continuous superposition combination" mode. If the line speed of multiple stations in the conductor drawing, nitrogen filling process of core wire foaming, stranding and other links (such as annealing drawing, insulation extrusion, wire feeding control, stranding untwisting, wrapping and braiding, covering and forming, straightening and winding process nodes) needs to be kept precisely synchronized, otherwise line speed deviation will occur. Linear speed deviation refers to the inconsistency in the actual moving speed of the cable between different workstations, usually expressed as a percentage (e.g., ±0.5%). This inconsistency in synchronization rhythm can accumulate rapidly in high-speed production (up to 80-150 meters per minute). This deviation may force the extrusion process of the extruder to slow down, stop, or be interrupted, causing a brief bridging phenomenon in the extruder, thus affecting the uniformity of the extruded thickness. It can also cause tension control imbalance in the cable, resulting in fluctuations in the cable's friction force and tension jumps. Furthermore, mismatch in the traction speed between the stranding machine, wrapping machine, and braiding machine can lead to uneven stretching of the conductor or shielding layer, resulting in uneven wrapping overlaps or uneven braiding density, causing material and time losses. Summary of the Invention

[0004] The purpose of this invention is to propose a fully automated, interconnected cable production method and system to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0005] To achieve the above objectives, according to one aspect of the present invention, a fully automated, interconnected cable production method is provided, the method comprising the following steps: The S100 collects operational data from multiple process nodes on an automated cable production line. S200: Extract extreme value features from the work data to construct a minimum value feature array and a maximum value feature array; S300: In real time, it determines whether a synchronization anomaly has occurred based on the minimum and maximum value feature arrays. If a synchronization anomaly occurs, it performs scheduling control on the automated cable production line to eliminate the synchronization anomaly.

[0006] Furthermore, in S100, the automated cable production line includes multiple process nodes, including annealing and drawing, insulation extrusion, wire feeding control, stranding and untwisting, wrapping and braiding, covering and forming, straightening and take-up.

[0007] Furthermore, in S100, the specific process nodes in the automated cable production line include: Annealing and drawing: The copper rod is drawn and annealed into 0.03-0.200mm copper wire using a wire drawing machine, and multiple copper wires are twisted into copper wire cores using a single twisting machine; Insulation extrusion: Polyolefin material is plasticized and melted in a fluoroplastic extruder, so that the polyolefin plastic particles are heated and melted in the extruder. Nitrogen is purged into the screw of the extruder. Under pressure, the molten plastic liquid and the purged nitrogen are extruded together and coated onto the surface of the copper wire core to obtain an insulated core wire with a good insulation layer. Wire release control: Two insulated core wires are released simultaneously from the wire release frame, and a tension controller is used to ensure that the tension of the insulated core wires is balanced; Twisting and untwisting: The guide wheel of the stranding machine is used to precisely adjust the two insulated core wires to a parallel state, with the spacing error controlled within ±0.1mm; the stranding machine is started to rotate the main shaft and twist the two insulated core wires at a set pitch (such as 20 times the core wire diameter). At the same time, the untwisting machine is activated to counteract the stranding torque, so as to obtain stranded wire and avoid the insulation layer from cracking due to torsional stress. The untwisting compensation parameters are dynamically adjusted by the feedback of the tension sensor. Wrapping and braiding: The stranded wires are fully wrapped with aluminum foil using a wrapping machine to form the cable core; metal wires are cross-braided on the outside of the cable core using a braiding machine to form a mesh-like metal wire braided shielding layer; Overmolding: Plastic is melted in an extruder to obtain liquid plastic, which is then extruded and used to coat the cable core. After cooling, the cable is formed. Straightening and winding: After the residual stress of the cable is eliminated by the straightener, the cable is wound into the I-beam at a constant linear speed, with a winding density deviation of ≤5%.

[0008] Furthermore, in S100, the operating data includes any two physical quantities from the following: tension value, wire diameter value, stress value, capacitance value, air pressure value, and extruder current value. Among them, the tension value is collected by a tension sensor at the process node of wire release control when the insulated core wire is released from the wire release frame; The wire diameter value is monitored by using a laser diameter gauge at the stranding untwisting process node; Among them, the stress value is monitored by fiber optic stress sensor at the stranding untwisting process node to monitor the torsional stress value of the stranded wire; The capacitance value is collected by pulling the formed cable into the capacitance tester during the overmolding process. Among them, the gas pressure value is monitored by a gas pressure sensor in the screw bore of the extruder during the insulation extrusion process to monitor the nitrogen pressure of the nitrogen-filled gas.

[0009] Furthermore, in S200, the method for extracting extreme value features from the job data to construct minimum and maximum value feature arrays includes: Any two physical quantities in the work data collected at preset time intervals are constructed into two first arrays and second arrays according to the time order of collection (e.g., tension value and capacitance value). Let the physical quantity data collected in the first array be called the first data, and the physical quantity data collected in the second array be called the second data; (Because the sources of each physical quantity in the work data are different, the data sampling frequencies of the sensors that collect the physical quantities are different, and the data volume of the physical quantity data in the first array and the second array are different, the data cannot be directly aligned. Extreme value feature extraction is required through the following directions); Among them, the preset time interval is set to 500 to 5000 milliseconds.

[0010] The array with fewer physical quantity data in the first and second arrays is called the "small quantity array," and the array with more physical quantity data is called the "large quantity array." The extreme value feature extraction and extreme value feature array construction are performed as follows: The valley turning point and peak turning point are marked sequentially for each physical quantity data in a small number of arrays, specifically as follows: If any physical quantity data exists in a small number of arrays that is greater than the previous physical quantity data, and the two physical quantity data preceding the physical quantity data are greater than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the valley turning point. If any physical quantity data in a small number of arrays is less than the previous physical quantity data, and the two physical quantity data before the physical quantity data are less than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the peak turning point. All the physical quantity data corresponding to the valley values ​​at the time of transition are arranged in the order of acquisition time to form a minimum value feature array; All the physical quantity data corresponding to the peak turning time are arranged in the order of acquisition time to form a maximum value feature array.

[0011] In the context of an automated cable production line, where multiple process nodes perform different procedures on the same cable, the same increasing or decreasing trend can occur between the valley turning point and the peak turning point during the changes in physical quantity data of different attributes at two different process nodes. This indicates a phased linear correlation in the changing trends of these two physical quantities on the same cable. The above solution extracts the minimum and maximum feature arrays of this changing trend. The valley turning point and peak turning point in these feature arrays represent the time interval after a local valley occurs within a preset time period. At the instant of the upward movement and the instant after the peak, due to the inertia of production, the changes of these two physical quantities in the two corresponding process nodes that produce a stage-based linear correlation will exhibit a synchronous phenomenon (not that the values ​​of the physical quantities change synchronously, but that the trends of increase or decrease are synchronous). If an asynchrony occurs at this moment, the physical quantities obtained at these two moments can pinpoint the time when the two process nodes are most likely to be out of sync. Asynchrony (abnormal synchronization of the trend of change) means that the linear velocity deviation is accumulating, causing the two process nodes to become disconnected. After the linear velocity deviation accumulates to a certain extent, it causes quality problems in the cable.

[0012] Furthermore, in S300, the method for determining whether a synchronization anomaly has occurred based on the minimum and maximum feature arrays includes the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the rising trend period; otherwise, proceed to step S305. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the falling trend period and proceed to step S304; otherwise, proceed to step S305. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: A synchronization error has been detected. Proceed to step S306. S306: End of procedure.

[0013] The fact that the physical quantity data collected in the multi-quantity array during the rising phase all increase sequentially indicates that the multi-quantity array and the small-quantity array during the rising phase are synchronous and continuous rising data (for any two physical quantities among tension, wire diameter, and stress current values, minimum and maximum feature arrays are extracted; since these highly correlated physical quantities show a linear relationship between increase and decrease, direct comparison can be made when the tension jump caused by frictional fluctuations is small). Similarly, the falling phase also shows synchronous and continuous falling data. The above method can quickly determine whether it is a rising phase with minimal computational power. Synchronization anomalies can quickly identify linear speed deviations caused by process slowdowns. The algorithm has low complexity and is suitable for industrial equipment with low computing power, low cost, and low latency, such as FPGAs (Field Programmable Gate Arrays), Digital Signal Processors (DSPs), and Microcontrollers (MCUs). However, if the physical quantity data of multiple arrays is not linear during the rising or falling phases, but instead exhibits a large amount of nonlinear fluctuation data (e.g., tension jumps caused by cable friction fluctuations, traction speed fluctuations, uneven nitrogen filling speeds, etc.), it will lead to low judgment and recognition accuracy or even failure to judge, and the algorithm complexity is high. To solve this problem, this application provides the following method:

[0014] Preferably, in S300, step S303 is replaced with the following step: Let UpMin be the minimum value and UpMax be the maximum value among the physical quantity data collected during the rising phase in the multi-quantity array; Let k1 be the sequence number of the physical quantity data collected in the multi-quantity array during the rising period, let N1 be the number of physical quantity data collected in the multi-quantity array during the rising period, and Up(k1) be the k1th physical quantity data collected in the multi-quantity array during the rising period. Within the range of k1, the physical quantity data collected in the multi-quantity array during the rising period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the rising period are less than the sum of UpMin and the rising fluctuation compensation value UpPay, then the data is marked as synchronized during the rising period; otherwise, proceed to step S305. Among them, the upward trend fluctuation compensation value ; Let DoMin be the minimum value and DoMax be the maximum value among the physical quantity data collected in the multi-quantity array during the downward trend period; Let k2 be the sequence number of the physical quantity data collected in the multi-quantity array during the downtrend period, let N2 be the number of physical quantity data collected in the multi-quantity array during the downtrend period, and let Do(k2) be the k2th physical quantity data collected in the multi-quantity array during the downtrend period. Within the range of k2, the physical quantity data collected in the multi-quantity array during the downward trend period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the downward trend period are less than the difference between DoMax and the downward trend fluctuation compensation value DoPay, then the data is marked as synchronized during the downward trend period and the process proceeds to step S304; otherwise, the process proceeds to step S305. Among them, the downward fluctuation compensation value ; Among them, the upward and downward fluctuation compensation values ​​of the above methods are the sum of the differences between the peak and valley values ​​of the accumulated difference and the current physical quantity. This compensation value can simulate the linear velocity deviation caused by the fluctuation value. By increasing or decreasing the compensation value of the peak and valley values ​​in the multi-quantity array during the upward or downward period, the accuracy of judgment and identification caused by nonlinear fluctuation data can be improved. In order to further address the problem that the linear velocity deviation accumulated in any two of the physical quantities such as wire diameter, capacitance, air pressure, and extruder current values ​​can force the extruder to slow down, stop, or even interrupt the extrusion process, this application proposes the following preferred solution.

[0015] Preferably, in S300, the method for determining whether a synchronization anomaly has occurred based on the minimum value feature array and the maximum value feature array includes the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the rising trend period; otherwise, mark the data as not synchronized during the rising trend period. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the falling trend period; otherwise, mark the data as not synchronized during the falling trend period. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: The total duration of all rising and falling periods marked as data out of sync is recorded as the synchronization duration; if the duration of the extruder bridging phenomenon exceeds the synchronization duration, it is determined that a synchronization abnormality has occurred and the process proceeds to step S306; otherwise, it is determined that no synchronization abnormality has occurred and the process proceeds to step S306. S306: End of procedure.

[0016] The above method utilizes the cumulative total duration of the asynchronous rising and falling periods of data. It also addresses the extruder bridging phenomenon (which occurs when production speed is too high, material consumption in the extruder is too fast, or nitrogen charging is too slow, potentially causing temporary bridging; this bridging disappears due to the increased pressure of nitrogen at high temperatures inside the extruder). For any two physical quantities among wire diameter, capacitance, gas pressure, and extruder current, minimum and maximum feature arrays are extracted. Since these weakly correlated physical quantities are difficult to compare directly, this method bypasses the comparison of physical quantity trends and directly compares the duration of the bridging phenomenon with the time dimension. This accurately identifies the duration of synchronization anomalies, improving the accuracy of synchronization anomaly identification when the duration of rising and falling periods is short, and avoiding the problem of rapid accumulation of linear velocity deviations.

[0017] The criteria for judging the occurrence of bridging phenomenon are: the extruder current value drops to below 60% of the rated power, or the air pressure value in the screw bore of the extruder is greater than the maximum air pressure value in the previous preset time interval.

[0018] Furthermore, in S300, the method for eliminating synchronization anomalies through scheduling control of automated cable production lines is as follows: Let the current tension of the insulated core wire pulled by the tension controller be ZL, then adjust the tension to ZL. ZL×CL; where CL is set to [0.05, 0.3].

[0019] Preferably, it further includes: until no synchronization anomaly occurs after a preset time interval.

[0020] Among them, the above methods, after detecting the synchronous abnormal signal caused by the rapid accumulation of linear velocity deviation under high-speed production (up to 80-150 meters / minute), especially when the physical quantity is any two of the physical quantities such as tension value, wire diameter value and stress value, extracting minimum and maximum feature arrays, the synchronous abnormal signal is the most accurate. In response to the problem of tension control imbalance caused by the accumulated linear velocity deviation of any two feature arrays of these physical quantities, the cable friction fluctuation causes tension jump, or the traction speed mismatch between stranding machine, wrapping machine and braiding machine leads to uneven stretching of conductor or shielding layer, which in turn leads to uneven wrapping overlap or uneven braiding density, resulting in material and time loss, the tension controller fine-tunes the tension to eliminate the accumulation of linear velocity deviation and thus alleviate or eliminate these problems.

[0021] Preferably, in S300, the method for scheduling and controlling the automated cable production line to eliminate synchronization anomalies is as follows: increase the temperature of the extruder's feeding zone by 50% and increase the nitrogen filling speed in the extruder's screw bore to increase the gas pressure value until no synchronization anomaly occurs after a preset time interval.

[0022] Preferably, in S300, the method for scheduling and controlling the automated cable production line to eliminate synchronization anomalies is as follows: Increase the current nitrogen charging rate by 0.03–0.2 m / h to maintain the nitrogen pressure in the extruder's screw bore at a pressure range 2.5–3 kPa higher than atmospheric pressure.

[0023] Preferably, it further includes: until no synchronization anomaly occurs after a preset time interval.

[0024] Among them, the above method, after detecting the synchronous abnormal signal caused by the rapid accumulation of linear velocity deviation under high-speed production (up to 80-150 m / min), especially the synchronous abnormal signal is most accurate when extracting minimum and maximum characteristic arrays from any two of the physical quantities such as wire diameter, capacitance, air pressure, and extruder current. In response to the problem that the linear velocity deviation accumulated in any two characteristic arrays of these physical quantities forces the extruder to slow down, stop, or even interrupt the extrusion process, the method uses gas pressure to quickly eliminate the slight bridging phenomenon by heating and pressurizing the screw of the extruder, thereby avoiding insufficient material supply in insulation extrusion, ensuring the balance of wire diameter and the uniformity of extrusion thickness.

[0025] Furthermore, it also includes: generating digital twin data corresponding to the operation data, and transmitting the digital twin data to the output device.

[0026] Preferably, the specific method for generating digital twin data corresponding to the job data is as follows: Mark data with synchronization anomalies in the job data; Kalman filtering is used to smooth and complete the physical quantity data in the operation data, which is then used as digital twin data.

[0027] Preferably, the specific method for transmitting digital twin data to the output device is as follows: Write digital twin data into the InfluxDB time-series database for retrospective analysis; Digital twin data is transmitted to the display terminal via the MQTT / OPC UA protocol to ensure low latency (<100ms).

[0028] On the industrial human-machine interface panel of the display terminal: display the 3D twin model, highlight process nodes (such as red warning of the temperature in the feeding zone of the extruder), and overlay trend curves (such as synchronous anomaly analysis of more than two physical quantities in digital twin data).

[0029] Preferably, when a synchronization anomaly is triggered, the digital twin interface automatically pops up adjustment steps (such as "heat up by 50%)" and generates an anomaly report (including the duration of the synchronization anomaly T_max and the bridging duration T_bridge).

[0030] This invention also provides a fully automated, interconnected cable production system. The fully automated, interconnected cable production system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the fully automated, interconnected cable production method. The fully automated, interconnected cable production system can run on computing devices such as desktop computers, laptops, handheld computers, and cloud data centers. The runnable system may include, but is not limited to, a processor, a memory, and servo motors. The processor executes the computer program within the following system units: The data acquisition unit is used to collect operational data from multiple process nodes on the automated cable production line. The feature extraction unit is used to extract extreme value features from the job data to construct a minimum value feature array and a maximum value feature array; The scheduling and control unit is used to determine in real time whether a synchronization anomaly has occurred based on the minimum and maximum value feature arrays. If a synchronization anomaly occurs, the scheduling and control unit of the automated cable production line is used to eliminate the synchronization anomaly.

[0031] As described above, the fully automated linkage cable production method and system of the present invention has the following beneficial effects: it can quickly determine whether there is a synchronization anomaly with minimal computing power, quickly identify the line speed deviation caused by the slowdown of the process, has low algorithm complexity, can accurately identify the duration of the synchronization anomaly, improves the identification accuracy of the synchronization anomaly when the duration of the rising and falling phases is short, and avoids cable product quality problems caused by the rapid accumulation of line speed deviation. Attached Figure Description

[0032] The above and other features of the present invention will become more apparent from the detailed description of the embodiments shown in conjunction with the accompanying drawings. In the accompanying drawings, the same reference numerals denote the same or similar elements. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without any creative effort. In the drawings: Figure 1 The diagram shown is a structural diagram of a fully automated, interconnected cable production system. Detailed Implementation

[0033] The following will provide a clear and complete description of the concept, specific structure, and technical effects of the present invention in conjunction with the embodiments and accompanying drawings, so as to fully understand the purpose, solution, and effects of the present invention. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0034] Example 1: The S100 collects operational data from multiple process nodes on an automated cable production line. S200: Extract extreme value features from the work data to construct a minimum value feature array and a maximum value feature array; S300: In real time, it determines whether a synchronization anomaly has occurred based on the minimum and maximum value feature arrays. If a synchronization anomaly occurs, it performs scheduling control on the automated cable production line to eliminate the synchronization anomaly.

[0035] Furthermore, in S100, the automated cable production line includes multiple process nodes, including annealing and drawing, insulation extrusion, wire feeding control, stranding and untwisting, wrapping and braiding, covering and forming, straightening and take-up.

[0036] Furthermore, in S100, the specific process nodes in the automated cable production line include: Annealing and drawing: The copper rod is drawn and annealed into 0.08mm copper wire using a wire drawing machine, and the four copper wires are twisted into copper wire cores using a single twisting machine; Insulation extrusion: Polyolefin material is plasticized and melted in a fluoroplastic extruder, so that the polyolefin plastic particles are heated and melted in the extruder. Nitrogen is purged into the screw of the extruder. Under pressure, the molten plastic liquid and the purged nitrogen are extruded together and coated onto the surface of the copper wire core to obtain an insulated core wire with a good insulation layer. Wire release control: Two insulated core wires are released simultaneously from the wire release frame, and a tension controller is used to ensure that the tension of the insulated core wires is balanced; Stranding and untwisting: The guide wheel of the stranding machine is used to precisely adjust the two insulated core wires to a parallel and parallel state, with the spacing error controlled within ±0.1mm; the stranding machine is started to rotate the main shaft and strand the two insulated core wires at a pitch of 20 times the core wire diameter. At the same time, the untwisting machine is activated to counteract the stranding torque, resulting in stranded wire. This avoids the insulation layer from cracking due to torsional stress. The untwisting compensation parameters are dynamically adjusted through feedback from the tension sensor. Wrapping and braiding: The stranded wires are fully wrapped with aluminum foil using a wrapping machine to form the cable core; metal wires are cross-braided on the outside of the cable core using a braiding machine to form a mesh-like metal wire braided shielding layer; Overmolding: Plastic is melted in an extruder to obtain liquid plastic, which is then extruded and used to coat the cable core. After cooling, the cable is formed. Straightening and winding: After the residual stress of the cable is eliminated by the straightener, the cable is wound into the I-beam at a constant linear speed, with a winding density deviation of ≤5%.

[0037] Furthermore, in S100, the operating data includes any two physical quantities from the following: tension value, wire diameter value, stress value, capacitance value, air pressure value, and extruder current value. Among them, the tension value is collected by a tension sensor at the process node of wire release control when the insulated core wire is released from the wire release frame; The wire diameter value is monitored by using a laser diameter gauge at the stranding untwisting process node; Among them, the stress value is monitored by fiber optic stress sensor at the stranding untwisting process node to monitor the torsional stress value of the stranded wire; The capacitance value is collected by pulling the formed cable into the capacitance tester during the overmolding process. Among them, the gas pressure value is monitored by a gas pressure sensor in the screw bore of the extruder during the insulation extrusion process to monitor the nitrogen pressure of the nitrogen-filled gas.

[0038] Furthermore, in S200, the method for extracting extreme value features from the job data to construct minimum and maximum value feature arrays includes: The wire diameter and capacitance values ​​collected at preset time intervals are constructed into two arrays, a first array and a second array, according to the order of collection time. The physical quantity data collected in the first array is designated as the first data, and the physical quantity data collected in the second array is designated as the second data; the preset time interval is set to 800 milliseconds.

[0039] The array with fewer physical quantity data in the first and second arrays is called the "small quantity array," and the array with more physical quantity data is called the "large quantity array." The extreme value feature extraction and extreme value feature array construction are performed as follows: The valley turning point and peak turning point are marked sequentially for each physical quantity data in a small number of arrays, specifically as follows: If any physical quantity data exists in a small number of arrays that is greater than the previous physical quantity data, and the two physical quantity data preceding the physical quantity data are greater than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the valley turning point. If any physical quantity data in a small number of arrays is less than the previous physical quantity data, and the two physical quantity data before the physical quantity data are less than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the peak turning point. All the physical quantity data corresponding to the valley values ​​at the time of transition are arranged in the order of acquisition time to form a minimum value feature array; All the physical quantity data corresponding to the peak turning time are arranged in the order of acquisition time to form a maximum value feature array.

[0040] Furthermore, in S300, the method for determining whether a synchronization anomaly has occurred based on the minimum and maximum feature arrays includes the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the rising trend period; otherwise, proceed to step S305. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the falling trend period and proceed to step S304; otherwise, proceed to step S305. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: A synchronization error has been detected. Proceed to step S306. S306: End of procedure.

[0041] Furthermore, in S300, the method for eliminating synchronization anomalies through scheduling control of automated cable production lines is as follows: Let the current tension traction of the tension controller be ZL, then adjust the tension to ZL. ZL×0.2.

[0042] Preferably, it also includes: no synchronization anomaly occurs until 800 milliseconds have elapsed.

[0043] Furthermore, it also includes: generating digital twin data corresponding to the operation data, and transmitting the digital twin data to the output device.

[0044] Preferably, the specific method for generating digital twin data corresponding to the job data is as follows: Mark data with synchronization anomalies in the job data; Kalman filtering is used to smooth and complete the physical quantity data in the operation data, which is then used as digital twin data.

[0045] Preferably, the specific method for transmitting digital twin data to the output device is as follows: Write digital twin data into the InfluxDB time-series database for retrospective analysis; Digital twin data is transmitted to the display terminal via the MQTT protocol to ensure low latency (<100ms).

[0046] On the industrial human-machine interface panel of the display terminal: display the 3D twin model, highlight process nodes (such as red warning of the temperature in the feeding zone of the extruder), and overlay trend curves (such as synchronous anomaly analysis of more than two physical quantities in digital twin data).

[0047] Preferably, when a synchronization anomaly is triggered, the digital twin interface automatically pops up control suggestions (including "suggest increasing the temperature by 50%)" and generates an anomaly report (including the duration of the synchronization anomaly T_max and the bridging duration T_bridge).

[0048] Example 2: Example 2 is based on Example 1 with the following adjustments: In S200, the method for extracting extreme features from work data to construct minimum and maximum feature arrays will replace "the two physical quantities data, wire diameter and capacitance, collected at preset time intervals" with "the two physical quantities data, tension and wire diameter, collected at preset time intervals".

[0049] In S300, step S303 is replaced with the following steps: Preferably, in S300, step S303 is replaced with the following step: Let UpMin be the minimum value and UpMax be the maximum value among the physical quantity data collected during the rising phase in the multi-quantity array; Let k1 be the sequence number of the physical quantity data collected in the multi-quantity array during the rising period, let N1 be the number of physical quantity data collected in the multi-quantity array during the rising period, and Up(k1) be the k1th physical quantity data collected in the multi-quantity array during the rising period. Within the range of k1, the physical quantity data collected in the multi-quantity array during the rising period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the rising period are less than the sum of UpMin and the rising fluctuation compensation value UpPay, then the data is marked as synchronized during the rising period; otherwise, proceed to step S305. Among them, the upward trend fluctuation compensation value ; Let DoMin be the minimum value and DoMax be the maximum value among the physical quantity data collected in the multi-quantity array during the downward trend period; Let k2 be the sequence number of the physical quantity data collected in the multi-quantity array during the downtrend period, let N2 be the number of physical quantity data collected in the multi-quantity array during the downtrend period, and let Do(k2) be the k2th physical quantity data collected in the multi-quantity array during the downtrend period. Within the range of k2, the physical quantity data collected in the multi-quantity array during the downward trend period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the downward trend period are less than the difference between DoMax and the downward trend fluctuation compensation value DoPay, then the data is marked as synchronized during the downward trend period and the process proceeds to step S304; otherwise, the process proceeds to step S305. Among them, the downward fluctuation compensation value ; Example 3: Example 3 is based on Example 1, but replaces the method of determining whether a synchronization anomaly has occurred based on the minimum and maximum feature arrays with the following method: Preferably, in S300, the method for determining whether a synchronization anomaly has occurred based on the minimum value feature array and the maximum value feature array includes the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the rising trend period; otherwise, mark the data as not synchronized during the rising trend period. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the falling trend period; otherwise, mark the data as not synchronized during the falling trend period. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: The total duration of all rising and falling periods marked as data out of sync is recorded as the synchronization duration; if the duration of the extruder bridging phenomenon exceeds the synchronization duration, it is determined that a synchronization abnormality has occurred and the process proceeds to step S306; otherwise, it is determined that no synchronization abnormality has occurred and the process proceeds to step S306. S306: End of procedure.

[0050] Based on Example 1, the method for scheduling and controlling the automated cable production line to eliminate synchronization anomalies in S300 will be replaced with the following method: Preferably, in S300, the method for scheduling and controlling the automated cable production line to eliminate synchronization anomalies is as follows: increase the temperature of the extruder's feeding zone by 50% and increase the nitrogen filling speed in the extruder's screw bore to increase the gas pressure value until no synchronization anomaly occurs after 800 milliseconds.

[0051] Preferably, the method for increasing the nitrogen filling speed inside the extruder's screw bore to increase the gas pressure is as follows: Increase the current nitrogen charging speed by 0.2 m / h to maintain the nitrogen pressure inside the extruder's screw bore at a pressure range 3 kPa higher than atmospheric pressure.

[0052] The criteria for judging the occurrence of bridging phenomenon are: the extruder current value drops to below 60% of the rated power, or the air pressure value in the screw bore of the extruder is greater than the maximum air pressure value in the previous preset time interval.

[0053] Comparative example: A method for manufacturing a twisted-pair cable includes the following steps: Annealing and drawing: The copper rod is drawn and annealed into 0.08mm copper wire using a wire drawing machine, and the four copper wires are twisted into copper wire cores using a single twisting machine; Insulation extrusion: Polyolefin material is plasticized and melted in a fluoroplastic extruder, so that the polyolefin plastic particles are heated and melted in the extruder. Nitrogen is purged into the screw of the extruder. Under pressure, the molten plastic liquid and the purged nitrogen are extruded together and coated onto the surface of the copper wire core to obtain an insulated core wire with a good insulation layer. Wire release control: Two insulated core wires are released simultaneously from the wire release frame, and a tension controller is used to ensure that the tension of the insulated core wires is balanced; Stranding and untwisting: The guide wheel of the stranding machine is used to precisely adjust the two insulated core wires to a parallel and parallel state, with the spacing error controlled within ±0.1mm; the stranding machine is started to rotate the main shaft and strand the two insulated core wires at a pitch of 20 times the core wire diameter. At the same time, the untwisting machine is activated to counteract the stranding torque, resulting in stranded wire. This avoids the insulation layer from cracking due to torsional stress. The untwisting compensation parameters are dynamically adjusted through feedback from the tension sensor. Wrapping and braiding: The stranded wires are fully wrapped with aluminum foil using a wrapping machine to form the cable core; metal wires are cross-braided on the outside of the cable core using a braiding machine to form a mesh-like metal wire braided shielding layer; Overmolding: Plastic is melted in an extruder to obtain liquid plastic, which is then extruded and used to coat the cable core. After cooling, the cable is formed. Straightening and winding: After the residual stress of the cable is eliminated by the straightener, the cable is wound into the I-beam at a constant linear speed, with a winding density deviation of ≤5%.

[0054] 800-meter lengths of the finished cables prepared in Examples 1, 2, 3, and the comparative example were cut from each. The cables from Examples 1, 2, and the comparative example were equipped with a laser diameter gauge (to test the uniformity of wire diameter: sampling once every 10 meters, taking 10 measurements every 100 meters) and an ultrasonic thickness gauge (to test the uniformity of the insulation layer (which contains nitrogen bubbles that act as a foaming layer), sampling once every 100 meters to measure the thickness). The test data obtained through testing is as follows: The test data for Example 1 are as follows: average wire diameter 1.487 mm; standard deviation of wire diameter 0.015; average thickness 0.63 mm; thickness range 0.05. The test data for Example 2 are as follows: average wire diameter 1.481 mm; standard deviation of wire diameter 0.011; average thickness 0.68 mm; thickness range 0.06. The test data for Example 3 are as follows: average wire diameter 1.521 mm; standard deviation of wire diameter 0.024; average thickness 0.54 mm; thickness range 0.03. The comparative test data are as follows: average wire diameter 1.552 mm; standard deviation of wire diameter 0.032; average thickness 0.72 mm; thickness range 0.09.

[0055] Data Analysis and Conclusions Examples 1 and 2, which control the tension of the cable, have better wire diameter uniformity and more precise control over the standard wire diameter. Example 3, which directly controls the extruder, performs best in terms of thickness, and is superior to the comparative examples.

[0056] like Figure 1The diagram shows a structural diagram of a fully automated, interconnected cable production system. This system includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the fully automated, interconnected cable production method. This fully automated, interconnected cable production system can run on computing devices such as desktop computers, laptops, handheld computers, and cloud data centers. The system can include, but is not limited to, a processor, memory, and servo motors. The processor executes the computer program within the following system units: The data acquisition unit is used to collect operational data from multiple process nodes on the automated cable production line. The feature extraction unit is used to extract extreme value features from the job data to construct a minimum value feature array and a maximum value feature array; The scheduling and control unit is used to determine in real time whether a synchronization anomaly has occurred based on the minimum and maximum value feature arrays. If a synchronization anomaly occurs, the scheduling and control unit of the automated cable production line is used to eliminate the synchronization anomaly.

[0057] Those skilled in the art will understand that the examples described are merely examples of a fully automated, interconnected cable production method and system, and do not constitute a limitation on a fully automated, interconnected cable production method and system. It may include more or fewer components, or a combination of certain components, or different components. For example, the fully automated, interconnected cable production system may also include input / output devices, network access devices, buses, etc.

[0058] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete component gate circuits, transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the fully automated, interconnected cable production system, connecting various sub-areas of the system via various interfaces and lines.

[0059] The memory can be used to store the computer program and / or modules. The processor implements various functions of the fully automated linkage cable production method and system by running or executing the computer program and / or modules stored in the memory, and by calling the data stored in the memory. The memory may mainly include a program storage area and a data storage area. The memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital card (SD card), flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0060] Although the invention has been described in considerable detail and particularly with regard to several of the described embodiments, it is not intended to limit itself to any of these details or embodiments or any particular embodiment, thereby effectively covering the intended scope of the invention. Furthermore, the invention has been described above with respect to embodiments foreseeable by the inventors in order to provide a useful description, and non-substantial modifications to the invention that have not yet been foreseen may still represent equivalent modifications.

Claims

1. A fully automated, interconnected cable production method, characterized in that, The method includes the following steps: The S100 collects operational data from multiple process nodes on an automated cable production line. S200: Extract extreme value features from the work data to construct a minimum value feature array and a maximum value feature array; S300: In real time, it determines whether a synchronization anomaly has occurred based on the minimum and maximum value feature arrays. If a synchronization anomaly occurs, it performs scheduling control on the automated cable production line to eliminate the synchronization anomaly.

2. According to claim 1, in a fully automated cable production method, in S100, the automated cable production line includes multiple process nodes, wherein the process nodes specifically include: Annealing and drawing: The copper rod is drawn and annealed into copper wire using a wire drawing machine, and multiple copper wires are twisted into copper cores using a single twisting machine; Insulation extrusion: Polyolefin material is plasticized and melted in a fluoroplastic extruder, so that the polyolefin plastic particles are heated and melted in the extruder. Nitrogen is purged into the screw of the extruder, and the molten plastic liquid and the purged nitrogen are extruded together to coat the surface of the copper wire core to obtain an insulated core wire with a good insulation layer. Wire release control: Two insulated core wires are released simultaneously from the wire release frame, and a tension controller is used to ensure that the tension of the insulated core wires is balanced; Stranding and untwisting: The guide wheel of the stranding machine is used to precisely adjust the two insulated core wires to a parallel state; the stranding machine is started to strand the two insulated core wires, and the untwisting machine is activated at the same time to counteract the stranding torque to obtain stranded wire; Wrapping and braiding: The stranded wires are fully wrapped with aluminum foil using a wrapping machine to form the cable core; a braiding machine is used to cross-braid metal wires on the outside of the cable core to form a shielding layer; Overmolding: The plastic is melted and extruded through an extruder to cover the cable core, and the cable is formed after cooling; Straightening and winding: After passing through the straightener, the cable is wound up onto the I-beam reel.

3. In the fully automated linkage cable production method according to claim 1, in S100, the operation data includes any two physical quantities among tension value, wire diameter value, stress value, capacitance value, air pressure value, and extruder current value.

4. According to claim 1, in a fully automated, interconnected cable production method, in step S200, the method for extracting extreme value features from the work data to construct a minimum value feature array and a maximum value feature array includes: Any two physical quantity data from the operation data collected at preset time intervals are constructed into two arrays, a first array and a second array, according to the order of collection time. The physical quantity data collected in the first array is called the first data, and the physical quantity data collected in the second array is called the second data. The array with fewer physical quantity data in the first array and the array with more physical quantity data in the second array are called the small quantity array and the array with more physical quantity data in the second array. The extreme value feature extraction and extreme value feature array construction are performed as follows: The valley turning point and peak turning point are marked sequentially for each physical quantity data in a small number of arrays, specifically as follows: If any physical quantity data exists in a small number of arrays that is greater than the previous physical quantity data, and the two physical quantity data preceding the physical quantity data are greater than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the valley turning point. If any physical quantity data in a small number of arrays is less than the previous physical quantity data, and the two physical quantity data before the physical quantity data are less than the previous physical quantity data, then the acquisition time corresponding to the physical quantity data is marked as the peak turning point. All the physical quantity data corresponding to the valley values ​​at the time of transition are arranged in the order of acquisition time to form a minimum value feature array; All the physical quantity data corresponding to the peak turning time are arranged in the order of acquisition time to form a maximum value feature array.

5. The fully automated linkage cable production method according to claim 4, in S300, the method for determining whether a synchronization anomaly has occurred based on the minimum value feature array and the maximum value feature array includes the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the rising trend period; otherwise, proceed to step S305. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, mark the data as synchronized during the falling trend period and proceed to step S304; otherwise, proceed to step S305. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: A synchronization error has been detected. Proceed to step S306. S306: End of procedure.

6. The fully automated linkage cable production method according to claim 5, wherein step S303 is replaced by the following step: recording the minimum value of the physical quantity data collected in the multi-quantity array during the rising period as UpMin and the maximum value as UpMax; Let k1 be the sequence number of the physical quantity data collected in the multi-quantity array during the rising period, let N1 be the number of physical quantity data collected in the multi-quantity array during the rising period, and Up(k1) be the k1th physical quantity data collected in the multi-quantity array during the rising period. Within the range of k1, the physical quantity data collected in the multi-quantity array during the rising period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the rising period are less than the sum of UpMin and the rising fluctuation compensation value UpPay, then the data is marked as synchronized during the rising period; otherwise, proceed to step S305. Let DoMin be the minimum value and DoMax be the maximum value among the physical quantity data collected in the multi-quantity array during the downward trend period; Let k2 be the sequence number of the physical quantity data collected in the multi-quantity array during the downtrend period, let N2 be the number of physical quantity data collected in the multi-quantity array during the downtrend period, and let Do(k2) be the k2th physical quantity data collected in the multi-quantity array during the downtrend period. Within the range of k2, the physical quantity data collected in the multi-quantity array during the downward trend period are judged sequentially: if all the physical quantity data collected in the multi-quantity array during the downward trend period are less than the difference between DoMax and the downward trend fluctuation compensation value DoPay, then the data is marked as synchronized during the downward trend period and the process proceeds to step S304; otherwise, the process proceeds to step S305.

7. In the fully automated linkage cable production method according to claim 5, in S300, the method of determining whether a synchronization anomaly occurs based on the minimum value feature array and the maximum value feature array is replaced by the following steps: S301: Use i as the index of the physical quantity data in the minimum value feature array; use j as the index of the physical quantity data in the maximum value feature array; set the initial values ​​of i and j to 1, and the total number of physical quantity data in the minimum value feature array to NX; S302: If i is greater than or equal to NX, proceed to step S306; otherwise, take the time interval between the valley turning point corresponding to the i-th physical quantity data in the minimum feature array and the peak turning point corresponding to the j-th physical quantity data in the maximum feature array as the rising trend period; take the time interval between the peak turning point corresponding to the j-th physical quantity data in the maximum feature array and the valley turning point corresponding to the (i+1)-th physical quantity data in the minimum feature array as the falling trend period. S303: Take the physical quantity data collected in the multi-quantity array during the rising trend period. If each physical quantity data is greater than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the rising trend period; otherwise, mark the data as not synchronized during the rising trend period. Take the physical quantity data collected in the multi-quantity array during the falling trend period. If each physical quantity data is less than or equal to the previous physical quantity data in chronological order, then mark the data as synchronized during the falling trend period; otherwise, mark the data as not synchronized during the falling trend period. S304: Increment the value of i by 1 and the value of j by 1, then proceed to step S302; S305: The total duration of all rising and falling periods marked as data out of sync is recorded as the synchronization duration; if the duration of the extruder bridging phenomenon exceeds the synchronization duration, it is determined that a synchronization abnormality has occurred and the process proceeds to step S306; otherwise, it is determined that no synchronization abnormality has occurred and the process proceeds to step S306. S306: End of procedure.

8. In the fully automated linkage cable production method according to claim 1, in S300, the method for scheduling and controlling the automated cable production line to eliminate synchronization abnormalities is as follows: increase the temperature of the feeding zone of the extruder by 50% and increase the nitrogen filling speed in the screw bore of the extruder to increase the gas pressure value until no synchronization abnormality occurs after a preset time interval.

9. According to claim 1, in the fully automated linkage cable production method, in S300, the method for scheduling and controlling the automated cable production line to eliminate synchronization abnormalities is to increase the current nitrogen charging speed by 0.03 to 0.2 m / h, so that the nitrogen pressure in the screw bore of the extruder is maintained within a pressure range of 2.5 to 3 kPa higher than atmospheric pressure.

10. A fully automated, interconnected cable production system, characterized in that, The system includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the fully automated linkage cable production method according to any one of claims 1 to 9.

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