Open-loop control method and system for horizontal winding machine

Abstract

The invention relates to the technical field of open-loop control of a horizontal winding machine, and discloses an open-loop control method and system for a horizontal winding machine, comprising: executing an initialization parameter calculation strategy to calculate the actual winding speed and the actual wire tension; executing a virtual simulation model construction strategy according to the target parameters and the wire characteristics to calculate the virtual model result at any time; executing an error feedback correction strategy according to the virtual model result, the actual wire tension and the actual coil diameter to adjust the actual winding speed and the actual wire tension in production; executing a parameter dynamic compensation strategy according to environmental parameters to calculate the actual wire tension after compensating for temperature and humidity; executing a real-time energy-saving optimization strategy to optimize the actual winding speed and the actual wire tension; executing a virtual simulation model optimization strategy according to the optimized actual winding speed and the actual wire tension to update the virtual simulation model. The first-time qualified rate of the coil is improved and the production cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of open-loop control of a horizontal winding machine, and in particular to an open-loop control method and system of a horizontal winding machine. Background Art

[0002] Winding machines are widely used in modern industrial production, especially in the fields of electrical, electronic and mechanical manufacturing. Their main function is to wind metal wires, fiber materials or other similar linear materials into the required shapes of coils, reels, etc. to meet the use requirements of different scenarios. In the control process of traditional winding machines, closed-loop control methods are often used to adjust the winding speed, wire tension and other parameters through real-time feedback. The open-loop control method, which is opposite to the closed-loop control method, combined with the open-loop control method of virtual simulation technology and optimization algorithm can significantly improve the performance and accuracy of the winding machine. Virtual simulation technology can simulate the change law of key parameters in the winding process in advance by establishing a mathematical model of material properties and process parameters, and provide a reference for production.

[0003] Traditional closed-loop control requires real-time measurement and feedback processing, which usually requires high sensor accuracy and real-time response speed, is expensive and complex to maintain. The response time of a closed-loop control system is limited by the speed of the feedback loop. During the high-speed winding process, feedback delays may cause the coil size to be unstable, thereby reducing the yield rate and increasing scrap costs.

[0004] This scheme proposes an open-loop control method and system for a horizontal winding machine, which can significantly improve the production efficiency and control accuracy of the winding machine while reducing system complexity and energy consumption. Summary of the invention

[0005] The present invention provides an open-loop control method and system for a horizontal winding machine, which promotes solving the problems mentioned in the above background technology.

[0006] In the first aspect, the present application provides an open-loop control method for a horizontal winding machine, which adopts the following technical scheme: an open-loop control method for a horizontal winding machine, comprising: step 1: obtaining target parameters, wherein the target parameters include a target coil diameter , Target wire tension and target number of wire turns , where g is used to represent the variables related to the target competition; Obtaining wire properties, including elastic modulus and wire diameter d; Execute the initialization parameter calculation strategy to calculate the actual winding speed and actual wire tension; Step 2: According to the target parameters and the wire characteristics, a virtual simulation model building strategy is executed to calculate the virtual model results and theoretical optimal parameters at any time, wherein the virtual model results include a virtual winding speed, a virtual wire tension, and a virtual coil diameter; Step 3: Measure the actual coil diameter; According to the virtual model results, actual wire tension and actual coil diameter, the error feedback correction strategy is implemented to adjust the actual winding speed and actual wire tension in production; Step 4: Acquire environmental parameters, which include ambient temperature and ambient humidity; According to the environmental parameters, the dynamic parameter compensation strategy is executed to calculate the actual wire tension after compensating for temperature and humidity; Step 5: Execute real-time energy-saving optimization strategy to optimize actual winding speed and actual wire tension; Step 6: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed to update the virtual simulation model.

[0007] Preferably, the executing the initialization parameter calculation strategy to calculate the actual winding speed and the actual wire tension includes: The time when the wire winding starts is recorded as the winding start time ; Get the actual coil diameter at time t ; Get the number of wire turns at time t ; Calculate the actual winding speed at time t ; Get the original length of the wire ; Get the original length of the wound wire at time t ; Get the original length at time t The length of the wire being stretched ; Get the friction force on the wire at time t ; Calculate actual wire tension .

[0008] By obtaining parameters such as the target coil diameter, target wire tension, target number of turns, and the elastic modulus, density, and diameter of the wire, the initial conditions for production can be fully established. These parameters provide data support for the calculation of the actual winding speed and wire tension, which helps to improve the accuracy and adaptability of the winding system. Executing the initialization parameter calculation strategy can quickly complete the estimation of key variables and reduce the impact of parameter setting deviations on production. At the same time, through a comprehensive understanding of the target and actual characteristics, a solid foundation can be laid for the construction of the virtual simulation model and support for subsequent dynamic adjustments.

[0009] Preferably, executing a virtual simulation model building strategy according to the target parameters and the wire material characteristics to calculate the virtual model results and theoretical optimal parameters at any time includes: Get Motor angular displacement from time to time t ; Calculate virtual winding speed ; Calculate virtual wire tension ,in, The original length of the tension at time t is The test value of the wire length change caused by the wire; Calculate the virtual coil diameter as follows: ,in, is the initial diameter of the reel; The virtual simulation results .

[0010] Preferably, executing a virtual simulation model building strategy according to the target parameters and the wire material characteristics to calculate the virtual model results and theoretical optimal parameters at any time includes: Calculate the theoretical optimal parameters , specifically: ; in, is the instantaneous power consumption, ,in, is the motor efficiency.

[0011] By building a virtual simulation model, it is possible to simulate the changes in various parameters during the winding process, such as winding speed, wire tension, and coil diameter. This not only reduces the time and resource costs of actual testing, but also enables the rapid optimization of theoretical optimal parameters in simulation. Through the virtual model, the parameter state at any time can be accurately predicted, providing a scientific basis for the adjustment of the winding process. The results of the simulation model can be compared with the actual measurement data to achieve closed-loop optimization, provide reliable theoretical support for error correction in subsequent steps, and improve the operating efficiency and accuracy of the entire system.

[0012] Preferably, the error feedback correction strategy is executed according to the virtual model result, the actual wire tension and the actual coil diameter to adjust the actual winding speed and the actual wire tension in production, including: Calculate the error between the actual coil diameter and the virtual coil diameter at time t ; Calculate the error between the actual wire tension and the virtual wire tension at time t ; Adjust the actual winding speed and record the adjusted actual winding speed as the adjusted winding speed ; ,in, To adjust the parameters of the actual winding speed; Adjust the actual wire tension and record the adjusted actual wire tension as the adjusted wire tension ; ,in, Parameters for adjusting actual wire tension.

[0013] By measuring the actual coil diameter and comparing it with the virtual simulation results, errors generated during the production process can be effectively detected to ensure that the winding machine operates within the target range. The error feedback correction strategy adjusts the winding speed and wire tension according to the deviation to get closer to the target value. This closed-loop adjustment mechanism can dynamically respond to changes in production and significantly improve winding accuracy and product quality. The combination of actual measurement and feedback correction makes production more intelligent, reduces rework caused by error accumulation, optimizes resource utilization, and provides guarantees for high-quality output of the final product.

[0014] Preferably, executing a parameter dynamic compensation strategy according to environmental parameters to calculate the actual wire tension after compensating for temperature and humidity includes: The ambient temperature at time t is expressed as ; The ambient humidity at time t is expressed as ; Get the standard ambient temperature in production and record it as ; Get the standard ambient humidity in production and record it as ; Calculate the temperature difference between the ambient temperature at time t and the standard ambient temperature ; Calculate the humidity difference between the ambient humidity at time t and the standard ambient humidity ; Calculate the equivalent temperature difference ,in, is the humidity-temperature coupling coefficient; The actual wire tension after compensating for temperature and humidity is recorded as the compensated wire tension ; ,in, is the temperature sensitivity coefficient; Calculation of compensation wire tension , where a, b, c are the parameters of the nonlinear fitting model.

[0015] By obtaining the ambient temperature and humidity, the dynamic compensation strategy obtains the environmental parameters in real time and calculates the equivalent compensation value to minimize the impact of environmental changes on the winding tension. Through the compensation calculation based on the nonlinear fitting model, the system can maintain stable operation in different environments. This step significantly improves the robustness of the production process, enhances the adaptability of the equipment in complex environments, reduces the production instability caused by environmental changes, and thus improves the quality consistency of the winding products.

[0016] Preferably, the implementation of the real-time energy-saving optimization strategy to optimize the actual winding speed and the actual wire tension includes: Use reinforcement learning to optimize the actual winding speed and actual wire tension, and calculate the reward value R of reinforcement learning; Get the actual coil diameter at time t ; Calculate the difference between the actual coil diameter and the target coil diameter ; Get the compensation wire tension ; Calculate the difference between the compensation wire tension and the target wire tension ; ,in, To save energy; Optimize power, specifically: ,in, For system efficiency; Execute real-time energy-saving optimization strategy and obtain output parameters of the optimization strategy and ; The actual winding speed after optimization is recorded as the optimized winding speed ; The actual wire tension after optimization is recorded as the optimized wire tension .

[0017] Preferably, executing the virtual simulation model optimization strategy and updating the virtual simulation model according to the optimized actual winding speed and actual wire tension includes: calculate ; calculate ; Update elastic modulus ,in is the update step size of the elastic modulus; Update the initial diameter of the scroll ,in is the update step size of the initial diameter of the scroll; Reinforcement Learning Optimization: Calculating Reward Value ; , update the virtual simulation model.

[0018] Energy saving is achieved by dynamically adjusting the actual winding speed and wire tension. On the basis of meeting the target production requirements, this strategy can reduce instantaneous power consumption, optimize system efficiency, and further reduce production energy consumption. This not only reduces production costs, but also extends the service life of equipment, while meeting the needs of modern manufacturing for green production. Through real-time evaluation and feedback optimization of energy-saving effects, the entire system can maximize energy-saving potential while maintaining efficient production, providing support for enterprises to save resources and improve economic benefits.

[0019] In a second aspect, the present application provides a system for an open-loop control method of a horizontal winding machine, which adopts the following technical solution: A system for an open-loop control method of a horizontal winding machine, comprising: Target parameter acquisition module: collects target coil diameter, target wire tension, target number of wire turns, obtains wire characteristics, and calculates actual winding speed and actual wire tension; Virtual simulation model module: real-time simulation of virtual winding speed, virtual wire tension, and virtual coil diameter during winding process; Error feedback correction module: compares the actual coil diameter with the virtual model result, executes the error feedback correction strategy to dynamically adjust the actual winding speed and actual wire tension; Environmental parameter compensation module: real-time monitoring of ambient temperature and humidity, and execution of parameter dynamic compensation strategy to correct actual wire tension; Real-time energy-saving optimization module: uses reinforcement learning algorithm to optimize the actual winding speed and actual wire tension; Simulation model dynamic optimization module: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed and the virtual simulation model is updated.

[0020] The present invention has the following beneficial effects: 1. The open-loop control method of the horizontal winding machine can clearly define the ultimate goal of production by obtaining parameters such as the target coil diameter, wire tension, and number of wire turns, thereby guiding the specific operation of the winding machine. This method effectively avoids the error problem in human settings and improves the accuracy of the target parameters. In addition, by obtaining the wire properties (elastic modulus, density, and diameter), the system can achieve personalized production adjustments to make the winding process more in line with the characteristics of the wire. By combining these parameters, the system lays the foundation for stable operation in the early stage of production, providing reliable data support for subsequent links, thereby improving the overall system operation efficiency and product quality.

[0021] 2. The open-loop control method of the horizontal winding machine, by building a virtual simulation model, the system can simulate the winding process before production starts and calculate key parameters such as virtual winding speed, wire tension and coil diameter. In this way, the system effectively reduces the trial and error costs in actual operation and avoids waste of raw materials. At the same time, virtual simulation can also provide theoretical optimal parameters to guide real-time adjustments in the production process, thereby controlling production errors within the minimum range. This method not only improves the system's predictive ability, but also provides a theoretical basis for the entire production process through the construction of a simulation model, making the system operation more accurate and efficient.

[0022] 3. The open-loop control method of the horizontal winding machine can quickly detect and correct deviations in the production process by comparing the actual coil diameter with the virtual coil diameter. Through the error feedback correction strategy, the system can adjust the winding speed and wire tension in real time to ensure that the product is always in the best production state. This method reduces the quality problems that may be caused by error accumulation, while significantly improving the consistency and accuracy of the product. Through this dynamic correction mechanism, the system not only improves the reliability of production, but also reduces the rework rate, thereby reducing the overall production cost.

[0023] 4. The open-loop control method of the horizontal winding machine can evaluate the potential impact of these environmental factors on the winding process by obtaining the ambient temperature and humidity. Through the parameter dynamic compensation strategy, the system effectively corrects the wire tension deviation caused by temperature and humidity changes. This method significantly improves the system's adaptability to external environmental changes and ensures the stability and consistency of the winding process. In addition, the introduction of the environmental compensation module effectively reduces the negative impact of environmental factors on product quality, thereby improving the overall reliability of production.

[0024] 5. The open-loop control method of the horizontal winding machine, through the combination of reinforcement learning and energy-saving strategies, the system can optimize the winding speed and wire tension, significantly reducing energy consumption while ensuring product quality. This method not only reduces production costs, but also conforms to the concept of modern industrial green manufacturing. In addition, real-time energy-saving optimization can dynamically adapt to different production conditions, further improving the efficiency and flexibility of the system. By optimizing power consumption, the system can achieve the best production effect with the lowest energy consumption, creating more economic benefits for the enterprise.

[0025] 6. The open-loop control method of the horizontal winding machine can continuously update the virtual simulation model through real-time feedback of the optimized actual parameters, making it more in line with the actual production situation. This method not only improves the accuracy of the model, but also enhances the adaptability of the system in production. Through the introduction of reinforcement learning, the simulation model can automatically learn and adjust, further improving the level of intelligent production. The continuously optimized simulation model provides a solid foundation for the long-term operation of the system, while ensuring the continuous improvement of production efficiency and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the process of the present invention.

[0027] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Embodiment 1, refer to Figure 1 , an open-loop control method for a horizontal winding machine, comprising: Step 1: Obtain target parameters, including target coil diameter , Target wire tension and target number of wire turns , where g is used to represent the variables related to the target competition; Get wire properties, including elastic modulus and wire diameter d; Execute the initialization parameter calculation strategy to calculate the actual winding speed and actual wire tension; Step 2: According to the target parameters and wire characteristics, the virtual simulation model building strategy is executed to calculate the virtual model results and theoretical optimal parameters at any time. The virtual model results include virtual winding speed, virtual wire tension and virtual coil diameter; Step 3: Measure the actual coil diameter; According to the virtual model results, actual wire tension and actual coil diameter, the error feedback correction strategy is implemented to adjust the actual winding speed and actual wire tension in production; Step 4: Obtain environmental parameters, including ambient temperature and ambient humidity; According to the environmental parameters, the dynamic parameter compensation strategy is executed to calculate the actual wire tension after compensating for temperature and humidity; Step 5: Execute real-time energy-saving optimization strategy to optimize the actual winding speed and actual wire tension; Step 6: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed to update the virtual simulation model.

[0030] Execute the initialization parameter calculation strategy to calculate the actual winding speed and actual wire tension, including: The time when the wire winding starts is recorded as the winding start time ; Get the actual coil diameter at time t ; Get the number of wire turns at time t ; Calculate the actual winding speed at time t ; Get the original length of the wire ; Get the original length of the wound wire at time t ; Get the original length at time t The length of the wire being stretched ; Get the friction force on the wire at time t ; Calculate actual wire tension .

[0031] By obtaining core parameters such as the target coil diameter, target wire tension, and target number of wire turns, the system can clarify production requirements and avoid errors caused by human settings, thereby laying an accurate foundation for subsequent processes. By combining material properties (such as elastic modulus, density, and diameter), the system can formulate winding strategies according to material conditions and improve adaptability to complex production scenarios. In this way, the system not only improves the accuracy of initial data, but also provides guarantees for production stability, while reducing unnecessary adjustments and waste of resources.

[0032] According to the target parameters and wire characteristics, the virtual simulation model building strategy is executed to calculate the virtual model results and theoretical optimal parameters at any time, including: Get Motor angular displacement from time to time t ; Calculate virtual winding speed ; Calculate virtual wire tension ,in, The original length of the tension at time t is The test value of the wire length change caused by the wire; Calculate the virtual coil diameter as follows: ,in, is the initial diameter of the reel; The virtual simulation results .

[0033] According to the target parameters and wire characteristics, the virtual simulation model building strategy is executed to calculate the virtual model results and theoretical optimal parameters at any time, including: Calculate the theoretical optimal parameters , specifically: ; in, is the instantaneous power consumption, ,in, is the motor efficiency.

[0034] By building a virtual simulation model, the system can simulate the winding process before production and predict key parameters such as winding speed, wire tension and coil diameter. This method effectively reduces the cost of testing and avoids the waste of resources caused by trial and error. Guided by the theoretical optimal parameters of the virtual model, the system can optimize production in real time, making actual production closer to the ideal state, thereby improving product quality and production efficiency. In this way, the system not only achieves high-precision production, but also effectively shortens the R&D cycle.

[0035] According to the virtual model results, actual wire tension and actual coil diameter, the error feedback correction strategy is implemented to adjust the actual winding speed and actual wire tension in production, including: Calculate the error between the actual coil diameter and the virtual coil diameter at time t ; Calculate the error between the actual wire tension and the virtual wire tension at time t ; Adjust the actual winding speed and record the adjusted actual winding speed as the adjusted winding speed ; ,in, To adjust the parameters of the actual winding speed; Adjust the actual wire tension and record the adjusted actual wire tension as the adjusted wire tension ; ,in, Parameters for adjusting actual wire tension.

[0036] Through the error feedback correction strategy, the system can dynamically adjust the actual winding speed and wire tension, effectively correct the deviation in production, and ensure the consistency of the production process with the target parameters. In this way, the system can detect errors in time and respond quickly, significantly improving the consistency and accuracy of the product. At the same time, through the introduction of dynamic correction, production efficiency is improved and the rework rate is significantly reduced. This method not only improves the stability of the system, but also effectively reduces production costs.

[0037] According to the environmental parameters, the dynamic compensation strategy is executed to calculate the actual wire tension after compensating for temperature and humidity, including: The ambient temperature at time t is expressed as ; The ambient humidity at time t is expressed as ; Get the standard ambient temperature in production and record it as ; Get the standard ambient humidity in production and record it as ; Calculate the temperature difference between the ambient temperature at time t and the standard ambient temperature ; Calculate the humidity difference between the ambient humidity at time t and the standard ambient humidity ; Calculate the equivalent temperature difference ,in, is the humidity-temperature coupling coefficient; The actual wire tension after compensating for temperature and humidity is recorded as the compensated wire tension ; ,in, is the temperature sensitivity coefficient; Calculation of compensation wire tension , where a, b, c are the parameters of the nonlinear fitting model.

[0038] By dynamically monitoring and compensating for changes in ambient temperature and humidity, the system can correct the effects of these factors on wire tension and ensure the stability and accuracy of the production process. In this way, the system effectively improves its ability to adapt to changes in the external environment and avoids quality problems caused by environmental fluctuations. This compensation strategy not only improves product consistency, but also provides higher reliability and flexibility for production operations, thereby enhancing the overall anti-interference ability of the system.

[0039] Execute real-time energy-saving optimization strategies to optimize actual winding speed and actual wire tension, including: Use reinforcement learning to optimize the actual winding speed and actual wire tension, and calculate the reward value R of reinforcement learning; Get the actual coil diameter at time t ; Calculate the difference between the actual coil diameter and the target coil diameter ; Get the compensation wire tension ; Calculate the difference between the compensation wire tension and the target wire tension ; ,in, To save energy; Optimize power, specifically: ,in, For system efficiency; Execute real-time energy-saving optimization strategy and obtain output parameters of the optimization strategy and ; The actual winding speed after optimization is recorded as the optimized winding speed ; The actual wire tension after optimization is recorded as the optimized wire tension .

[0040] By combining reinforcement learning with energy-saving strategies, the system can optimize winding speed and wire tension, significantly reducing energy consumption while ensuring production efficiency. In this way, the system not only achieves green and energy-saving production, but also effectively improves resource utilization. At the same time, real-time energy-saving optimization can dynamically adjust to different production conditions, further enhancing the flexibility and economy of system operation. This approach creates more benefits for enterprises while complying with the needs of sustainable development.

[0041] According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed and the virtual simulation model is updated, including: calculate ; calculate ; Update elastic modulus ,in is the update step size of the elastic modulus; Update the initial diameter of the scroll ,in is the update step size of the initial diameter of the scroll; Reinforcement Learning Optimization: Calculating Reward Value ; , update the virtual simulation model.

[0042] By feeding optimized actual production data back to the simulation model in real time, the system can dynamically update model parameters to make the simulation results closer to the actual situation. In this way, the system continuously improves the predictive ability and reliability of the virtual model, providing more accurate guidance for future production processes. At the same time, through the introduction of reinforcement learning, the system can optimize autonomously and improve the level of intelligence. This dynamic optimization strategy not only improves product quality and production efficiency, but also lays a solid foundation for the long-term application of the system.

[0043] Example 2, refer to Figure 2 , a system of an open-loop control method for a horizontal winding machine, comprising: Target parameter acquisition module: collects target coil diameter, target wire tension, target number of wire turns, obtains wire characteristics, and calculates actual winding speed and actual wire tension; Virtual simulation model module: real-time simulation of virtual winding speed, virtual wire tension, and virtual coil diameter during winding process; Error feedback correction module: compares the actual coil diameter with the virtual model result, executes the error feedback correction strategy to dynamically adjust the actual winding speed and actual wire tension; Environmental parameter compensation module: real-time monitoring of ambient temperature and humidity, and execution of parameter dynamic compensation strategy to correct actual wire tension; Real-time energy-saving optimization module: uses reinforcement learning algorithm to optimize the actual winding speed and actual wire tension; Simulation model dynamic optimization module: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed and the virtual simulation model is updated.

[0044] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0045] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. An open-loop control method for a horizontal winding machine, characterized in that: include: Step 1: Obtain target parameters, including target coil diameter , Target wire tension and target number of wire turns , where g is used to represent the variables related to the target competition; Obtaining wire properties, including elastic modulus and wire diameter d; Execute the initialization parameter calculation strategy to calculate the actual winding speed and actual wire tension; Step 2: According to the target parameters and the wire characteristics, a virtual simulation model building strategy is executed to calculate the virtual model results and theoretical optimal parameters at any time, wherein the virtual model results include a virtual winding speed, a virtual wire tension, and a virtual coil diameter; Step 3: Measure the actual coil diameter; According to the virtual model results, actual wire tension and actual coil diameter, the error feedback correction strategy is implemented to adjust the actual winding speed and actual wire tension in production; Step 4: Acquire environmental parameters, which include ambient temperature and ambient humidity; According to the environmental parameters, the dynamic parameter compensation strategy is executed to calculate the actual wire tension after compensating for temperature and humidity; Step 5: Execute real-time energy-saving optimization strategy to optimize actual winding speed and actual wire tension; Step 6: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed to update the virtual simulation model.

2. The open-loop control method of a horizontal winding machine according to claim 1, characterized in that: The execution of the initialization parameter calculation strategy to calculate the actual winding speed and the actual wire tension includes: The time when the wire winding starts is recorded as the winding start time ; Get the actual coil diameter at time t ; Get the number of wire turns at time t ; Calculate the actual winding speed at time t ; Get the original length of the wire ; Get the original length of the wire wound at time t ; Get the original length at time t The actual length of the wire being stretched ; Get the friction force on the wire at time t ; Calculate actual wire tension .

3. The open-loop control method of a horizontal winding machine according to claim 2, characterized in that: The method of executing a virtual simulation model building strategy according to the target parameters and the wire material characteristics, and calculating the virtual model results and theoretical optimal parameters at any time, includes: Get Motor angular displacement from time to time t ; Calculate virtual winding speed ; Calculate virtual wire tension ,in, The original length of the tension at time t is The test value of the wire length change caused by the wire; Calculate the virtual coil diameter as follows: ,in, is the initial diameter of the reel; The virtual simulation results .

4. The open-loop control method of a horizontal winding machine according to claim 1, characterized in that: The method of executing a virtual simulation model building strategy according to the target parameters and the wire material characteristics, and calculating the virtual model results and theoretical optimal parameters at any time, includes: Calculate the theoretical optimal parameters , specifically: ； in, is the instantaneous power consumption, , is the motor efficiency.

5. The open-loop control method of a horizontal winding machine according to claim 1, characterized in that: The error feedback correction strategy is executed according to the virtual model result, the actual wire tension and the actual coil diameter to adjust the actual winding speed and the actual wire tension in production, including: Calculate the error between the actual coil diameter and the virtual coil diameter at time t ; Calculate the error between the actual wire tension and the virtual wire tension at time t ; Adjust the actual winding speed and record the adjusted actual winding speed as the adjusted winding speed ; ,in, To adjust the parameters of the actual winding speed; Adjust the actual wire tension and record the adjusted actual wire tension as the adjusted wire tension ; ,in, Parameters for adjusting actual wire tension.

6. The open-loop control method of a horizontal winding machine according to claim 1, characterized in that: The method of executing a parameter dynamic compensation strategy according to environmental parameters and calculating the actual wire tension after compensating for temperature and humidity includes: The ambient temperature at time t is expressed as ; The ambient humidity at time t is expressed as ; Get the standard ambient temperature in production and record it as ; Get the standard ambient humidity in production and record it as ; Calculate the temperature difference between the ambient temperature at time t and the standard ambient temperature ; Calculate the humidity difference between the ambient humidity at time t and the standard ambient humidity ; Calculate the equivalent temperature difference ,in, is the humidity-temperature coupling coefficient; The actual wire tension after compensating for temperature and humidity is recorded as the compensated wire tension ; Calculation of compensation wire tension , where a, b, c are the parameters of the nonlinear fitting model, is the temperature sensitivity coefficient.

7. The open-loop control method of a horizontal winding machine according to claim 6, characterized in that: The implementation of the real-time energy-saving optimization strategy to optimize the actual winding speed and the actual wire tension includes: Use reinforcement learning to optimize the actual winding speed and actual wire tension, and calculate the reward value R of reinforcement learning; Calculate the difference between the actual coil diameter and the target coil diameter at time t ; Get the compensation wire tension ; Calculate the difference between the compensation wire tension and the target wire tension ; ,in, To save energy; Optimize power, specifically: ,in, For system efficiency; Execute real-time energy-saving optimization strategy and obtain output parameters of the optimization strategy and ; The actual winding speed after optimization is recorded as the optimized winding speed ; The actual wire tension after optimization is recorded as the optimized wire tension .

8. The open-loop control method of a horizontal winding machine according to claim 7, characterized in that: The method of executing the virtual simulation model optimization strategy and updating the virtual simulation model according to the optimized actual winding speed and actual wire tension includes: calculate ; calculate ; Update elastic modulus ,in, is the update step size of the elastic modulus; Update the initial diameter of the scroll ,in, is the update step size of the initial diameter of the scroll; Reinforcement Learning Optimization: Calculating Reward Value ; , update the virtual simulation model.

9. A system for implementing the open-loop control method of the horizontal winding machine according to claim 1, characterized in that: include: Target parameter acquisition module: collects target coil diameter, target wire tension, target number of wire turns, obtains wire characteristics, and calculates actual winding speed and actual wire tension; Virtual simulation model module: real-time simulation of virtual winding speed, virtual wire tension, and virtual coil diameter during winding process; Error feedback correction module: compares the actual coil diameter with the virtual model result, executes the error feedback correction strategy to dynamically adjust the actual winding speed and actual wire tension; Environmental parameter compensation module: real-time monitoring of ambient temperature and humidity, and execution of parameter dynamic compensation strategy to correct actual wire tension; Real-time energy-saving optimization module: uses reinforcement learning algorithm to optimize the actual winding speed and actual wire tension; Simulation model dynamic optimization module: According to the optimized actual winding speed and actual wire tension, the virtual simulation model optimization strategy is executed and the virtual simulation model is updated.

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