Automatic spring wire transport apparatus and method

Abstract

The application discloses a spring steel wire automatic conveying device and method, and belongs to the technical field of spring steel wire automatic conveying. The device comprises a conveying track, at least two intelligent driving nodes arranged in an array along the conveying track, each of the intelligent driving nodes comprising a driving wheel pair for clamping and driving the spring steel wire, and a local vibration sensor for measuring the lateral displacement of the spring steel wire at the outlet of the driving wheel pair, at least an optical attitude sensing station arranged on the conveying track, the optical attitude sensing station comprising a binocular vision module for measuring the torsion angle of the spring steel wire, and a laser speed meter for measuring the axial advancing speed of the spring steel wire, and a controller electrically connected with the intelligent driving nodes and the optical attitude sensing station, respectively. The application significantly improves the stability of the conveying process.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automatic transportation of spring steel wire, in particular to a kind of spring steel wire automatic transportation equipment and method thereof. BACKGROUND

[0002] Traditional spring steel wire automatic transportation equipment generally uses open-loop or simple closed-loop control method based on rigid body assumption, and pushes or pulls the steel wire through several driving points;This way is prone to cause bowstring vibration, whipping and torsional winding caused by speed mismatch, fluctuation propagation and energy accumulation when facing the physical characteristics of high elasticity and flexibility of the steel wire itself, especially under high-speed start-stop or direction-changing conditions, which seriously restricts production efficiency and yield;Therefore, how to change the control of single particle motion into the global collaborative management of dynamic topological state of one-dimensional flexible continuum along the line, i.e. tension field, torsion angle and lateral displacement, has become a contradiction to be solved;Traditional solutions lack real-time, online sensing capability of steel wire distributed state and predictive collaborative control algorithm based on the sensing;

[0003] Therefore, the present application aims to study a kind of steel wire topological state active control method and system based on dynamics model and multi-point sensing fusion, by constructing the digital twin state field of the whole steel wire, and based on this, by using predictive control algorithm to cooperatively control each distributed driving unit along the line, to actively suppress the generation of unstable mode, and realize the topological fidelity of the steel wire in high-speed transportation process.

[0004] The above information disclosed in the above background section is only used to strengthen the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application aims to provide a kind of spring steel wire automatic transportation equipment and method thereof to solve the problems raised in the above background.

[0006] The technical scheme of the present application is as follows:

[0007] Conveying track;

[0008] At least two intelligent driving nodes are arranged in an array along the conveying track, each intelligent driving node includes a driving wheel pair for clamping and driving the spring steel wire, and a local vibration sensor for measuring the lateral displacement of the spring steel wire at the outlet of the driving wheel pair;

[0009] At least the optical attitude sensing station is arranged on the conveying track, and the optical attitude sensing station includes a binocular vision module for measuring the torsion angle of the spring steel wire, and a laser speedometer for measuring the axial travel speed of the spring steel wire;

[0010] and a controller electrically connected with the intelligent driving node and the optical posture sensing station, respectively.

[0011] Preferably, the driving wheel pair comprises an upper driving wheel and a lower driving wheel independently driven by a servo motor, and the upper driving wheel is arranged on an electrically controlled lifting platform for adjusting the clamping pressure between the upper driving wheel and the lower driving wheel.

[0012] Preferably, the local vibration sensor is a dual-axis laser displacement sensor for measuring the transverse displacement of the spring wire in the vertical direction and the horizontal direction, respectively.

[0013] Preferably, the binocular vision module calculates the torsion angle by identifying the difference in the rotation angle of the features on the surface of the spring wire in the imaging of the two cameras.

[0014] An automatic spring wire transportation method, comprising:

[0015] The controller collects the transverse displacement of the spring wire from the local vibration sensor to obtain local vibration data, collects the torsion angle and axial travel speed from the optical posture sensing station to obtain posture speed data, and collects driving parameters from the intelligent driving node to obtain driving feedback data;

[0016] The controller loads the local vibration data, the posture speed data and the driving feedback data into a preset wire dynamics model for real-time calculation of a global state field continuously distributed along the conveying track, containing predicted tension, predicted torsion angle and predicted vibration trend;

[0017] The controller receives a target operation instruction and performs state deduction on the target operation instruction based on the current global state field, for predicting future instability information of the spring wire when the target operation instruction is executed;

[0018] The controller generates a cooperative suppression instruction based on the future instability information, and issues the cooperative suppression instruction to at least two intelligent driving nodes before executing the target operation instruction, for cooperatively regulating the driving wheel pairs of the intelligent driving nodes to actively suppress the occurrence of the future instability information.

[0019] Preferably, the future instability information includes predicted tension fluctuation information or predicted torsion accumulation information.

[0020] Preferably, when the future instability information is the predicted tension fluctuation information, the cooperative suppression instruction is used to instruct a previous node in the adjacent intelligent driving node to reduce the rotating speed and increase the clamping pressure, while instructing a subsequent node to increase the torque output to maintain the segment tension.

[0021] Preferably, when the future instability information is the predicted torsion accumulation information, the cooperative suppression instruction is used to instruct the upper drive wheel and the lower drive wheel of the intelligent drive node to generate a speed difference to apply a reverse torque to the spring wire.

[0022] Preferably, the drive feedback data includes the output torque and rotation speed of the servo motor of the drive wheel pair, and the current clamping pressure level of the electric control lifting platform.

[0023] The present application provides a spring wire automatic transportation equipment and method by improvement, compared with the prior art, has the following improvements and advantages:

[0024] 1. By setting a dual-axis laser displacement sensor on the intelligent drive node, the displacement of the steel wire in the vertical and horizontal two orthogonal directions can be measured at the same time, and more comprehensive vibration information can be obtained. In addition, the binocular vision module can non-contact calculate the torsion angle by identifying the rotation angle difference of the steel wire surface texture characteristics in the imaging of the two cameras. These data provide a high-quality data source for subsequent predictive active control, so that the controller can directly prevent vibration and torsion control, significantly improving the stability of the transportation process.

[0025] 2. The drive wheel pair in the intelligent drive node is independently driven by a servo motor, and the clamping pressure between the upper drive wheel and the lower drive wheel is adjusted by the electric control lifting platform. This adjustability expands the function of the drive wheel pair from single driving to combined adjustment of driving and braking. For example, when it is necessary to suppress tension fluctuation, the electric control lifting platform can increase the clamping pressure to produce a braking effect; when driving normally, the pressure matching the current working condition can be set to avoid damaging the steel wire or causing skidding.

[0026] 3. The controller loads the local vibration data, attitude speed data and drive feedback data to the preset steel wire dynamics model. This model expands the discrete measurement data into a continuous global state field, forming a comprehensive description of the overall state of the steel wire. By feeding back the output torque and rotation speed of the servo motor and the clamping pressure level and other drive feedback data to the controller and inputting them to the dynamics model, the state field calculated by the model can be more close to the physical reality, thereby improving the accuracy of the state field prediction. This high-precision prediction provides a reliable basis for the generation of cooperative suppression instructions, and realizes active management of the state of the steel wire.

[0027] 4、When the controller predicts tension fluctuation information, the coordinated suppression instruction will control the adjacent intelligent driving nodes, for example, the previous node reduces the rotating speed and increases the clamping pressure to generate braking force, while the subsequent node increases the torque output, through the coordinated action of the front and rear nodes, the stable tension level is actively established and maintained in the section where the tension drop will occur, thereby eliminating the physical conditions for producing bowstring vibration, when predicting torsion accumulation, the coordinated suppression instruction will control the upper driving wheel and the lower driving wheel of the driving wheel pair to generate a small speed difference, apply a reverse torque to the steel wire, and gradually release the torsional stress to avoid winding problems caused by excessive torsion. BRIEF DESCRIPTION OF DRAWINGS

[0028] The application will be further explained in connection with the accompanying drawings and embodiments:

[0029] Figure 1 is the overall structure schematic diagram of the device;

[0030] Figure 2 is the connection structure schematic diagram of the intelligent driving node and the optical attitude sensing station;

[0031] Figure 3 is the structure schematic diagram of the intelligent driving node;

[0032] Figure 4 is the structure schematic diagram of the optical attitude sensing station;

[0033] Figure 5 is the method flow structure schematic diagram of the application

[0034] In the figure: 100, conveying track; 200, intelligent driving node; 210, driving wheel pair; 220, local vibration sensor; 300, optical attitude sensing station; 310, binocular vision module; 320, laser speedometer; 400, controller. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical scheme and advantages of the application more clear and obvious, the application will be further described in detail below in combination with specific embodiments. Embodiment 1

[0036] Please refer to Figures 1-4 , the application provides a spring steel wire automatic conveying device, which comprises:

[0037] Conveying track 100;

[0038] At least two intelligent driving nodes 200 are arranged in an array along the conveying track 100, each intelligent driving node 200 comprises a driving wheel pair 210 for clamping and driving the spring steel wire, and a local vibration sensor 220 for measuring the transverse displacement of the spring steel wire at the outlet of the driving wheel pair 210;

[0039] At least one optical attitude sensing station 300 is set on the conveying track 100. The optical attitude sensing station 300 includes a binocular vision module 310 for measuring the torsion angle of the spring steel wire and a laser velocimeter 320 for measuring the axial travel speed of the spring steel wire.

[0040] And controller 400, which is electrically connected to intelligent drive node 200 and optical attitude sensing station 300 respectively.

[0041] In traditional steel wire transportation, the lack of online status monitoring of the steel wire makes it prone to vibration and torsion due to its elasticity and flexibility during high-speed start-stop or direction changes, affecting production stability. This automatic spring steel wire transportation equipment solves the above problems by setting up multiple intelligent drive nodes 200 and optical attitude sensing stations 300 distributed along the conveying track 100. Among them, the local vibration sensor 220 on the intelligent drive node 200 can obtain the lateral displacement of the steel wire at the drive point, providing data input for judging the vibration state of the steel wire. The optical attitude sensing station 300, through its binocular vision module 310 and laser velocimeter 320, can obtain the torsion angle and axial velocity of the steel wire at a specific position, providing a basis for judging the torsion and motion state of the steel wire.

[0042] The controller 400 collects data from these sensors located at different positions, manages the overall dynamics of the steel wire distribution along the line, and lays the foundation for maintaining the status during high-speed transportation. The electrical connection between the controller 400 and each sensor is to ensure that the data can be stably transmitted to the controller 400 for processing. This connection can be achieved through industrial bus or wireless communication, as long as the real-time performance and reliability of data transmission can be ensured. For example, a wired connection using the Profinet protocol or a wireless connection using 5G technology is acceptable.

[0043] The drive wheel pair 210 includes an upper drive wheel and a lower drive wheel that are independently driven by a servo motor. The upper drive wheel is mounted on an electrically controlled lifting platform, which is used to adjust the clamping pressure between the upper drive wheel and the lower drive wheel.

[0044] The electrically controlled lifting platform provides the driving wheel pair 210 with the ability to adjust the clamping pressure. In the transportation of steel wires, a constant clamping pressure cannot adapt to different steel wire specifications or varying running speeds. The electrically controlled lifting platform, such as a precision lead screw sliding table driven by a stepper motor, can receive instructions from the controller 400 to fine-tune the vertical position of the upper driving wheel. When it is necessary to exert a braking force on the steel wire to suppress tension fluctuations, the electrically controlled lifting platform can increase the clamping pressure to create a braking effect using increased friction. During normal driving, the pressure can be set to match the current working conditions to avoid damaging the steel wire surface due to excessive pressure or causing driving slippage due to insufficient pressure. This adjustability of pressure expands the function of the driving wheel pair 210 from single driving execution to combined driving and braking adjustment, providing the necessary physical execution means for actively maintaining segment tension in subsequent methods.

[0045] The local vibration sensor 220 is a dual-axis laser displacement sensor used to measure the transverse displacement of the spring steel wire in the vertical and horizontal directions, respectively.

[0046] The local vibration sensor 220 adopts a dual-axis laser displacement sensor, which can be a Keyence CL-3000 series. The bowstring vibration generated by the steel wire during high-speed movement is not a single direction movement, but a complex vibration in space. The dual-axis laser displacement sensor can simultaneously and non-contact measure the displacement of the steel wire in the vertical and horizontal two orthogonal directions, thereby obtaining more comprehensive vibration information. The controller 400 fuses the data of the two dimensions, which can more accurately depict the motion trajectory and vibration amplitude of the steel wire at the exit of the driving node, providing a higher quality data source for the subsequent dynamic model to calculate a more realistic vibration trend, thereby improving the accuracy of vibration instability prediction.

[0047] The binocular vision module 310 provides a non-contact torsion angle measurement approach. The module arranges two industrial cameras along the steel wire direction, continuously shooting the steel wire surface. Due to the existence of microscopic and natural texture features on the steel wire surface, the image processing algorithm built-in the controller 400 will lock the same texture feature point. When the steel wire twists, the feature point will show an angle difference in the imaging planes of the front and rear two cameras. By calculating this angle difference in real time, the torsion angle of the steel wire between the two cameras can be converted. This measurement method avoids the interference that contact sensors may cause to the movement of the steel wire.

[0048] When constructing the model, the entire steel wire is discretized into a series of mass point units connected by virtual springs and dampers in a virtual computing environment. The stiffness of the virtual spring is related to the elastic modulus E and the cross-sectional area A of the steel wire, representing the tensile and bending properties of the steel wire.

[0049] The binocular vision module 310 provides a non-contact torsion angle measurement approach, which arranges two industrial cameras before and after the steel wire to continuously shoot the steel wire surface; due to the micro and natural texture features on the steel wire surface, the image processing algorithm built in the controller 400 locks the same texture feature point; when the steel wire is twisted, the feature point will show an angle difference in the imaging planes of the two cameras; by calculating the angle difference in real time, the torsion angle of the steel wire between the two cameras can be converted; this measurement method avoids the interference of the contact sensor on the movement of the steel wire, provides support for accurately obtaining the torsion accumulation, which is a key state parameter, and is the data basis for subsequent predictive anti-torsion control.

[0050] Embodiment 2

[0051] Please refer to Figure 5 An automatic spring wire transportation method, comprising:

[0052] The controller 400 collects the lateral displacement of the spring wire from the local vibration sensor 220 to obtain local vibration data, collects the torsion angle and axial running speed from the optical attitude perception station 300 to obtain attitude speed data, and collects the driving parameters from the intelligent driving node 200 to obtain driving feedback data;

[0053] The controller 400 loads the local vibration data, the attitude speed data and the driving feedback data into a preset steel wire dynamics model, which is used to calculate the global state field of the spring wire continuously distributed along the conveying track 100, including the predicted tension, the predicted torsion angle and the predicted vibration trend in real time;

[0054] The controller 400 receives the target operation instruction, and performs state deduction on the target operation instruction based on the current global state field, so as to predict the future instability information of the spring wire when the target operation instruction is executed;

[0055] The target operation instruction is a high-level control command, which represents the expected state of the user or the upper manufacturing execution system to the spring wire transportation process, such as a specific acceleration, deceleration or constant speed operation; these instructions will be used by the controller 400 for state deduction before being executed, so as to predict their influence on the physical state of the spring wire, such as tension and torsion; when it is predicted that the execution instruction may cause future instability information, the controller 400 will suspend or modify the original instruction, and generate a coordinated suppression instruction to cope with it in advance;

[0056] The controller 400 generates a coordinated suppression instruction based on the future instability information, and issues the coordinated suppression instruction to at least two intelligent driving nodes 200 before executing the target operation instruction, so as to cooperatively control the driving wheels 210 of the intelligent driving nodes 200 to actively suppress the occurrence of the future instability information.

[0057] The core of the spring wire automatic transportation method is a predictive active control logic. The controller 400, which can be a Siemens SIMATIC IPC series industrial computer, collects local vibration data, attitude speed data, and drive feedback data distributed discretely through various sensors. Although these data are local, they reflect the true physical state of the wire at key nodes. The controller 400 inputs these real measurement data as correction parameters and boundary conditions into a pre-set wire dynamics model that matches the wire diameter and elastic modulus and other physical characteristics;

[0058] The model expands the discrete measurement data into a continuous global state field along the conveying track 100 through calculation. This state field includes the predicted tension, predicted twist angle, and predicted vibration trend of each section of the wire, forming a comprehensive description of the overall state of the wire. To enable those skilled in the art to understand, the construction and calculation logic of the wire dynamics model are further described. In the construction of the model, the entire wire is discretized into a series of mass point units connected by virtual springs and dampers in a virtual calculation environment. The mass of each mass point unit is assigned according to the actual linear density parameter of the wire, and the stiffness of the virtual spring is related to the elastic modulus E and cross-sectional area A of the wire, representing the tensile and bending properties of the wire.

[0059] The calculation logic of the model is that it takes real-time data collected by various sensors as boundary conditions or correction anchors of the model. For example, the controller 400 applies the axial travel speed measured by the optical attitude sensing station 300 to the mass point unit at the corresponding position in the model, and takes the lateral displacement measured by the local vibration sensor 220 as the forced displacement constraint of the mass point unit at that position. Based on these measured anchor data, the controller 400 calculates the interaction force between adjacent mass point units according to the basic dynamics equation, such as Newton's second law, and combines Hooke's law in material mechanics, which describes the relationship between tension and strain. The basic relationship can be expressed as Real-time iterative calculation of the interaction force between adjacent mass point units is performed to solve the predicted tension T of each point in the entire mass point chain. Wherein, E is the elastic modulus of the wire, A is the cross-sectional area, ΔL is the elongation of the virtual spring between mass points, and L is its original length. Similarly, by introducing the torsional stiffness parameter, the predicted twist angle can also be calculated.

[0060] Thus, the discrete measurement points are extended to a continuous global state field; when performing future state extrapolation, the controller 400 converts the target operation instruction, such as the target acceleration, into a virtual driving force or a speed boundary condition applied to the driving wheel pair 210 mass point unit; the model performs short-term dynamic simulation calculation on the entire mass point chain system under this new condition, thereby predicting the state evolution trend of the steel wire in the future extremely short time slice and identifying the future instability information that may exceed the safety threshold from among them; the safety threshold is not a fixed constant, but a dynamic safety threshold determined by the controller according to the input steel wire specifications, such as the wire diameter, the material elastic modulus, and the current operation condition, such as the target speed;

[0061] Specifically, the determination logic includes: calculating the theoretical critical value of instability of the steel wire under a specific working condition, such as the critical tension or the critical torsion angle, based on the material mechanics theory, such as the Euler critical load theory, correcting it in combination with the experimental data collected by the equipment during the debugging stage, and setting a safety factor lower than the theoretical critical value, for example, 70%-90% of the theoretical value, to finally obtain the dynamic safety threshold for real-time judgment; this method ensures the adaptability and reliability of the predictive control; when receiving a new target operation instruction, such as an acceleration instruction, the controller 400 does not execute it immediately, but performs a rapid future state extrapolation using the constructed global state field to calculate which section of the steel wire may have instability information of low tension or excessive torsion if the instruction is executed directly; according to the predicted future instability information, the controller 400 generates a set of coordinated suppression instructions executed by multiple intelligent driving nodes 200 in reverse, and issues them in advance; this series of actions enables the equipment to intervene before instability occurs, actively maintaining the stability of the steel wire through multi-point coordinated control, rather than passively correcting after detecting vibration or torsion.

[0062] The future instability information includes predicted tension fluctuation information or predicted torsion accumulation information.

[0063] The future instability information is specifically defined as predicted tension fluctuation information and predicted torsion accumulation information; this is because in the physical process of high-speed transportation of the steel wire, the direct cause of the bowstring vibration phenomenon is the instantaneous drop of the tension in the local section, and the direct cause of the torsion winding phenomenon is the continuous accumulation of torsional stress along the length direction of the steel wire; therefore, taking tension and torsion as the core prediction target enables the control method to directly prevent the root cause of instability, improving the pertinence and effectiveness of the control.

[0064] When the future instability information is the predicted tension fluctuation information, the coordinated suppression instruction is used to instruct the previous node in the adjacent intelligent driving node 200 to reduce the rotating speed and increase the clamping pressure, and simultaneously instruct the subsequent node to increase the torque output, so as to maintain the section tension.

[0065] When the controller 400 predicts that a certain section will experience tension fluctuation, i.e. tension drop, the generated cooperative suppression instruction will regulate the two intelligent driving nodes 200 before and after the section; the prequel node, i.e. the node where the wire is about to leave, will be instructed to appropriately reduce the rotation speed of the driving wheel pair 210, and increase the clamping pressure through the electrically controlled lifting platform, with the purpose of generating a controllable, local braking force; at the same time, the sequel node, i.e. the node where the wire is about to enter, will be instructed to increase the output torque of the driving wheel pair 210 in advance and gently. Through the cooperation of pulling by the prequel node and pushing by the sequel node, a stable tension level is actively established and maintained in the section predicted to experience tension drop, thereby eliminating the physical conditions for the generation of bowstring vibration.

[0066] When the future instability information is the predicted torsional accumulation information, the cooperative suppression instruction is used to instruct the upper driving wheel and the lower driving wheel of the intelligent driving node 200 to generate a speed difference to apply a reverse torque to the spring wire.

[0067] When the controller 400 predicts that the wire end will experience excessive torsional accumulation, the generated cooperative suppression instruction will act on one or more intelligent driving nodes 200 along the way. The instruction will control the upper driving wheel and the lower driving wheel of the driving wheel pair 210 to rotate with a small speed difference; since the wire is clamped between the two driving wheels, this speed difference will apply a weak reverse torque to the wire through friction, which is opposite to the direction of torsional accumulation of the wire; this reverse torque can gradually release the torsional stress before it reaches a dangerous level, achieving active management of the torsional state and effectively avoiding wire winding caused by excessive torsion.

[0068] The driving feedback data includes the output torque and rotation speed of the servo motor of the driving wheel pair 210, and the current clamping pressure level of the electrically controlled lifting platform.

[0069] The driving feedback data specifies the specific information that needs to be fed back from the actuator to the controller 400; the torque and rotation speed output by the servo motor directly reflect the actual size of the driving energy applied to the wire, while the current clamping pressure level of the electrically controlled lifting platform reflects the coupling efficiency of the driving energy transmitted to the wire through the driving wheel pair 210; taking these actual execution parameters as input and feeding them back to the wire dynamics model can make the global state field solved by the model more close to the physical reality; so that the model not only knows what the controller 400 wants to execute, but also knows what the driving mechanism actually executes, which improves the accuracy of state field prediction.

[0070] It should be noted that the above examples are only used to illustrate the technical solutions of the present application but not limit the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced, without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An automatic spring wire transporting apparatus, characterized by, The application relates to a spring steel wire production system, comprising: a conveying track (100); at least two smart driving nodes (200) arranged along the conveying track (100), each of the smart driving nodes (200) comprising a driving wheel pair (210) for clamping and driving a spring steel wire, and a local vibration sensor (220) for measuring the lateral displacement of the spring steel wire at the outlet of the driving wheel pair (210); at least an optical posture sensing station (300) arranged on the conveying track (100), the optical posture sensing station (300) comprising a binocular vision module (310) for measuring the torsion angle of the spring steel wire, and a laser speed meter (320) for measuring the axial advancing speed of the spring steel wire; and a controller (400) electrically connected with the smart driving nodes (200) and the optical posture sensing station (300) respectively; the local vibration sensor (220) is a two-axis laser displacement sensor for measuring the lateral displacement of the spring steel wire in the vertical direction and the horizontal direction respectively; the binocular vision module (310) calculates the torsion angle by identifying the rotation angle difference of the features on the surface of the spring steel wire in the imaging of two cameras.

2. The spring wire automatic transporting apparatus according to claim 1, wherein the driving wheel pair (210) comprises an upper driving wheel and a lower driving wheel independently driven by a servo motor, and the upper driving wheel is arranged on an electrically-controlled lifting platform for adjusting the clamping pressure between the upper driving wheel and the lower driving wheel.

3. A method for automatically transporting spring steel wire, applied to the spring steel wire automatic transporting device according to any one of claims 1 to 2, characterized in that, The application further relates to a spring steel wire production method, comprising: collecting, by the controller (400), the lateral displacement of the spring steel wire from the local vibration sensor (220) to obtain local vibration data, collecting the torsion angle and the axial advancing speed from the optical posture sensing station (300) to obtain posture speed data, and collecting driving parameters from the smart driving nodes (200) to obtain driving feedback data; loading, by the controller (400), the local vibration data, the posture speed data and the driving feedback data into a preset steel wire dynamics model, for real-time solving a global state field continuously distributed along the conveying track (100) and containing a predicted tension, a predicted torsion angle and a predicted vibration trend; receiving, by the controller (400), a target operation instruction, and performing state deduction on the target operation instruction based on the current global state field, for predicting future instability information of the spring steel wire when the target operation instruction is executed; generating, by the controller (400), a cooperative suppression instruction based on the future instability information, and issuing the cooperative suppression instruction to at least two smart driving nodes (200) before the target operation instruction is executed, for cooperatively controlling the driving wheel pairs (210) of the smart driving nodes (200) to actively suppress the occurrence of the future instability information.

4. A method of automatically transporting spring wire according to claim 3, wherein The future instability information comprises predicted tension fluctuation information or predicted torsion accumulation information.

5. A method of automatically transporting spring wire according to claim 4, wherein When the future instability information is the predicted tension fluctuation information, the cooperative suppression instruction is used to instruct the previous node in the adjacent intelligent driving node (200) to reduce the rotating speed and increase the clamping pressure, while instructing the subsequent node to increase the torque output to maintain the section tension.

6. A method of automatically transporting spring wire according to claim 4, wherein When the future instability information is the predicted torsion accumulation information, the cooperative suppression instruction is used to instruct the upper driving wheel and the lower driving wheel of the intelligent driving node (200) to generate a speed difference to apply a reverse torque to the spring steel wire.

7. A method of automatically transporting spring wire according to claim 3, wherein The driving feedback data includes the output torque and rotating speed of the servo motor of the driving wheel pair (210), and the current clamping pressure level of the electric control lifting platform.

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