A multi-station winding machine digital control method and system

Abstract

The application relates to the technical field of winding machines, and provides a multi-station winding machine digital control method and system, which comprises the following steps: obtaining a winding task, decomposing the winding task, determining standard operation parameters of each station, performing digital modeling on each station according to the standard operation parameters, determining a digital control model of each station, obtaining a station sequence, fusing the digital control of each station based on the station sequence, and generating a sequential operation planning model, supervising operation data of each station through the sequential operation planning model, judging whether there is an operation error, and adjusting operation parameters when the operation error exists. Through decomposition of the winding task, the work of each station is planned, the waiting and pause time in the production process is reduced, and the overall production efficiency is improved. In the supervision process of the operation data, the operation error is adjusted, unmanned self-checking is realized, and the winding machine is controlled in a full-digital mode.

Description

Technical Field

[0001] This invention relates to the field of winding machine control technology, and in particular to a digital control method and system for a multi-station winding machine. Background Technology

[0002] Stretch wrapping machines are commonly used in the industrial packaging field. Their main function is to wrap packaging materials around products to protect and stabilize them.

[0003] Traditional wrapping machines typically employ mechanical or simple electronic controls, primarily based on a control panel configured on the machine. By programming different packaging parameters, such as wrapping speed, tension, and number of layers, they can achieve fully automatic or semi-automatic operation.

[0004] With the development of stretch wrapping machines, multi-station stretch wrapping machines have emerged. These machines are highly automated packaging equipment capable of processing multiple products simultaneously for mass production. They are typically designed with multiple stations, each capable of working independently or collaboratively to improve production efficiency and packaging speed.

[0005] Furthermore, most modern multi-station winding machines have only improved their structure, without improving their operational processes. For example:

[0006] In the research and design of multi-station CNC fiber winding machines, the working principle and structural characteristics of multi-station fiber winding machines were discussed, and improvement measures were proposed. These improvements included adopting a gantry layout structure, a robotic manipulator for loading and unloading mandrels, and a power separation output device for the headstock, in order to improve the level of automation and production efficiency. However, no improvements were made in terms of digital control.

[0007] In the design and research of the yarn hanging device control system of a fiber winding machine, a novel trolley-type synchronous yarn hanging device is proposed, and its structural composition and working principle are analyzed. Furthermore, the rotational inertia and load torque of the yarn hanging device are calculated, and a suitable servo motor is selected. The paper also proposes an overall control strategy based on IPC+PMAC, and analyzes and designs the logic control module and motion control module of the yarn hanging control system. However, its digital control adopts a combination of industrial control from a personal industrial computer and a programmable multi-axis controller, which can achieve high-speed and high-precision control, but cannot adjust for errors.

[0008] It is evident that existing technologies struggle to achieve precise control and multi-station collaborative operation for multi-station winding machines. With the development of industrial automation, higher demands are placed on the precise control and production efficiency of winding machines, necessitating solutions to the aforementioned problems. Summary of the Invention

[0009] This invention provides a digital control method and system for a multi-station winding machine, which solves the problem that traditional winding machines usually use mechanical or simple electronic control, making it difficult to achieve precise control and multi-station collaborative operation.

[0010] In a first aspect, the present invention proposes a digital control method for a multi-station winding machine, comprising:

[0011] Obtain the yarn winding task, decompose the yarn winding task, and determine the standard operating parameters for each workstation;

[0012] Based on the standard operating parameters, digital modeling is performed for each workstation to determine the digital control model for each workstation;

[0013] Obtain the workstation sequence and integrate the digital control of each workstation based on the workstation sequence to generate a sequential operation planning model;

[0014] By using a sequential job planning model, the job data of each workstation is monitored to determine whether there are any job errors, and job parameters are adjusted when job errors are found.

[0015] In conjunction with the first aspect, the decomposition of the yarn winding task includes:

[0016] Obtain the station operation parameters for each station in the yarn winding task, and determine the corresponding station control parameters; among them,

[0017] The workstation control parameters and workstation operation parameters have a synchronous response mechanism at the same time;

[0018] Each workstation is treated as a task node, and these task nodes are ordered sequentially, with task parameters for each node; among them...

[0019] Task parameters include control parameters and standard operating parameters for each workstation.

[0020] In conjunction with the first aspect, the digital modeling includes:

[0021] A station simulation model of a multi-station winding machine is established; among which,

[0022] The motion control characteristics and standard operating parameters of the multi-station winding machine are used to correct the station simulation model of the multi-station winding machine, so that the station simulation model is consistent with the operating parameters and operating characteristics of the actual multi-station winding machine.

[0023] Based on the workstation simulation model, a yarn winding environment is built, and the digital parameters for yarn winding are determined.

[0024] A digital twin model of a multi-station winding machine was built using digital parameters.

[0025] In conjunction with the first aspect, determining the digital control model for each workstation includes:

[0026] Based on the digital twin model, obtain the synchronous control parameters for each workstation and encode the workstation.

[0027] Based on the workstation code, set the working mode response mechanism for each workstation;

[0028] Based on the work mode response mechanism, set the control range for workstation control;

[0029] The control range is imported into the digital twin model to determine the digital control model for each workstation;

[0030] in,

[0031] The digital control model is also used to synchronously respond to the control parameters of each workstation.

[0032] In conjunction with the first aspect, the sequential job planning model includes the following integration steps:

[0033] Determine the work sequence for each workstation based on the workstation order;

[0034] Based on the task sequence, construct a sequential task model architecture for sequential tasks;

[0035] In the sequential operation model architecture, the digital input interface and digital output interface of the digital control model corresponding to each workstation are determined.

[0036] The digital control models of different workstations are sequentially merged through the digital input and output interfaces of adjacent workstations; among them,

[0037] A parameter monitoring mechanism is configured on the digital input interface. When the parameter monitoring mechanism detects that the input parameters of the digital output interface of the previous station are consistent with the standard operation parameters output by the digital output interface of the next station, the next station executes the corresponding operation process.

[0038] In conjunction with the first aspect, the sequential job planning model includes a supervision layer, a control layer, and a regulation layer; wherein,

[0039] The supervision layer is configured with operation parameter recognition scripts for different workstations;

[0040] The control layer is configured with authentication scripts for the operation parameters of different workstations;

[0041] The adjustment layer is configured with operation parameter optimization scripts for different workstations.

[0042] In conjunction with the first aspect, the job parameter identification script includes the following parameter identification steps:

[0043] Based on the workstation sequence, set control templates for different workstations; among them...

[0044] The control template is used to respond to control parameters at different workstations and upload the response information of the control parameters;

[0045] Based on the response information and control template, determine the identified job parameters and upload the status of the job parameters.

[0046] In conjunction with the first aspect, the job parameter authentication script includes the following execution steps:

[0047] Obtain the operation control parameters and operation standard parameters for each workstation;

[0048] A response authentication model is built based on control parameters, and an identification authentication model is built based on operational parameters; among them...

[0049] There is an associated adjustment mechanism between the response authentication model and the identification authentication model when they are in the same workstation sequence;

[0050] The response authentication model is used to obtain response information from different workstations and determine whether the response information is the first abnormal data.

[0051] When the response information is the first abnormal data, the standard operation parameters are adjusted through the correlation adjustment mechanism;

[0052] By identifying and authenticating the model, we can obtain the work information of different workstations and determine whether the work information is the second abnormal data.

[0053] When the job information is the second abnormal data, the job control parameters are adjusted through the correlation adjustment mechanism;

[0054] The response authentication model and the identification authentication model are constructed based on the k-means clustering algorithm, the linear support vector machine algorithm, and unlabeled sample data.

[0055] In conjunction with the first aspect, the job parameter optimization script includes the following execution steps:

[0056] The status parameters of the job parameter identification script and the job parameter authentication script are counted separately.

[0057] Identify abnormal data and control parameters in different processes, and perform data statistics;

[0058] Based on the data statistics, determine the number of times abnormal data occurs at the workstation under different control parameters, and optimize the workstation control parameters accordingly.

[0059] Secondly, this invention proposes a digital control system for a multi-station winding machine, comprising:

[0060] Standard Operations Module: Obtains yarn winding tasks, decomposes yarn winding tasks, and determines the standard operation parameters for each workstation;

[0061] Digital control module: Based on standard operating parameters, digital modeling is performed for each workstation to determine the digital control model for each workstation;

[0062] Sequential Operation Module: Obtains the workstation sequence and integrates the digital control of each workstation based on the workstation sequence to generate a sequential operation planning model;

[0063] Supervision and adjustment module: Through the sequential operation planning model, the module supervises the operation data of each workstation, determines whether there are operation errors, and adjusts the operation parameters when operation errors exist.

[0064] The beneficial effects of this invention are as follows:

[0065] The primary function of this invention is to decompose the yarn winding task, effectively plan the work at each workstation, reduce waiting and downtime during production, and thus improve overall production efficiency. Digital modeling of standard operating parameters ensures that each workstation follows a unified standard, reducing human error and improving product quality and consistency. Monitoring the operational data of each workstation through a sequential operation planning model allows for real-time monitoring of the process, timely detection and handling of errors, thereby enhancing quality control. In terms of flexibility and adaptability, the digital control of each workstation can be integrated based on the workstation sequence, enabling rapid adaptation to different production tasks and workstation sequences, making the production process more flexible and adaptable. Furthermore, adjusting operating parameters through the digital control model reduces the need for physical adjustments and calibrations to equipment, thus lowering maintenance costs. During the monitoring of operational data, the ability to adjust operational errors also ensures reduced reliance on manual labor, achieving full digitalization.

[0066] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0068] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0069] Figure 1This is a flowchart of a digital control method for a multi-station winding machine according to an embodiment of the present invention;

[0070] Figure 2 This is a system composition diagram of a digital control system for a multi-station winding machine according to an embodiment of the present invention;

[0071] Figure 3 This is a diagram illustrating the composition of the digital control system of a conventional multi-station winding machine in an embodiment of the present invention.

[0072] Figure 4 This is a flowchart illustrating the yarn winding task processing in an embodiment of the present invention.

[0073] Figure 5 This is a flowchart illustrating the construction process of the digital control model in an embodiment of the present invention. Detailed Implementation

[0074] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0075] In existing digital control methods for multi-station winding machines, stationary winding machines are typically used to wrap materials (such as paper, plastic film, metal foil, etc.) onto products or packaging in a specific manner. Digital control refers to using a computer or programmable logic controller (PLC) to control the machine's operation to achieve low-precision, high-efficiency, and automated production. During the winding process, each station's operation is based on a collaboration between manual and machine work, followed by manual inspection for winding errors. Therefore, it can only handle low-precision, high-efficiency automated production.

[0076] like Figure 3 As shown, the traditional digital control system of a multi-station winding machine mainly includes a digital control center, a work area, and an industrial monitoring layer. The work area is the specific production line of the multi-station winding machine, which has multiple workstations. The digital control center, as the control center of the multi-station winding machine, is used to configure the workstation parameters, such as feeding parameters, winding parameters, and discharging parameters. The workstation monitoring layer is used to detect the workstations and determine whether there are any abnormalities. However, the traditional method mainly involves manual inspection. During the manual inspection, if an abnormality is found, it is reported and thresholds for some workstation control parameters are set to determine whether there is an abnormality in the workstation.

[0077] To address the shortcomings of traditional winding machine control methods, this application proposes the following technical solution:

[0078] This application discloses a digital control system for a multi-station winding machine, see reference. Figure 2The multi-station winding machine digital control system of this application realizes digital operation control by receiving control tasks from the access control terminal 40. The multi-station winding machine digital control system consists of a standard operation module 10, a digital control module 20, a sequential operation module 30, and a monitoring and adjustment module 40. During execution, the multi-station winding machine digital control system is implemented through the following embodiments:

[0079] Example 1:

[0080] See Figure 1 The digital control method for a multi-station winding machine proposed in this application includes the following steps:

[0081] Step S100: Obtain the winding task, decompose the winding task, and determine the standard operating parameters for each station. The winding task includes the total winding amount, winding accuracy requirements, winding speed, and unique winding operations for different stations. These tasks are input to the multi-station winding machine as a general task code signal. Then, it is broken down into sub-tasks according to the station function. Different stations include multiple tasks, such as tension control, speed adjustment, and process standards. The target parameters for each station are then determined, i.e., the target control parameters that need to be executed to achieve the winding task. By decomposing the winding task, the independent control objectives of each station can be determined, avoiding efficiency losses or quality defects caused by conflicting objectives across multiple stations.

[0082] Step 101: Based on standard operating parameters, digital modeling is performed for each workstation to determine the digital control model for each workstation. Digital modeling is based on physical modeling (building a digital control function for tension-motor torque) to convert the physical control behavior (tension control) of the workstation into a mathematical model. In this process, digital modeling can also build a neural network model trained on historical operating data. Digital modeling prevents errors caused by manual parameter setting in multi-station winding machines. Although the probability of error in conventional manual parameter setting is low, it lacks the self-checking capabilities of mathematical or neural network models. Furthermore, mathematical or neural network models can effectively manage the operating efficiency and cost of each workstation.

[0083] Step 102: Obtain the station sequence and integrate the digital control of each station based on the station sequence to generate a sequential operation planning model. The station sequence is the mechanical layout sequence of the multi-station winding machine. For example, the first station is for yarn feeding and straightening, the second station is for winding at the first angle control, and the third station is for winding and forming control. The operation sequence of the multi-station winding machine is then determined by connecting the independent models of each station in series or parallel according to the sequence, forming a globally controlled sequential operation planning model. This model is used to set the winding parameters for each station of the multi-station winding machine according to the station sequence. This can resolve timing conflicts between multiple stations and prevent material waste and energy consumption caused by the premature start of a subsequent station before the previous station has finished.

[0084] Step 103: Using a sequential operation planning model, monitor the operation data of each workstation to determine if any operational errors exist, and adjust the operation parameters if errors are found. After determining the standard operation parameters for each workstation, the sequential operation planning model can collect the actual operation parameters of different workstations in real time using sensors, encoders, and other devices. Then, compare the actual operation parameters with the standard operation parameters to determine if any operational errors exist. This achieves digital monitoring of all workstations, enables the determination of the accuracy of sequential operations across multiple workstations, avoids batch quality defects, and allows for both individual monitoring of each workstation and sequential monitoring of multiple workstations.

[0085] This application primarily addresses the issues of deviations between workstations in multi-station winding machines and the reliance on manual inspection in existing technologies, proposing solutions. To resolve the timing conflicts and error accumulation problems common in traditional multi-station winding machines, this application employs a sequential operation planning model to achieve global supervision, thereby suppressing error accumulation through monitoring and adjustment. In this process, the introduction of task decomposition and digital modeling allows for sequential planning through task decomposition, enabling digital collaborative control and preventing operational deviations.

[0086] Example 2:

[0087] The standard operation module 10 of this application will perform the yarn winding task decomposition operation, see [link / reference]. Figure 4 The standard operation module 10 includes a first task processing device 101 and a second task processing device 102. The first task processing device 101 and the second task processing device 102 are programmable controllers or digital processors in a multi-station winding machine.

[0088] The first task processing device 101 will perform the following steps:

[0089] Step 1011: Obtain the station operation parameters for each station in the yarn winding task, and determine the corresponding station control parameters. Station operation parameters are physical quantities that characterize the actual operating state, such as yarn tension and winding speed. Station control parameters are executable commands that directly affect the actuator, such as motor torque and inverter frequency. Through the mapping relationship between the two, specific operation parameters are converted into executable control parameters.

[0090] Step 1012: The workstation control parameters and workstation operation parameters have a synchronous response mechanism at the same time. The synchronous response mechanism includes the synchronization of control parameters of adjacent workstations and the complementarity of operation parameters, as well as the correspondence between the workstation control parameters and workstation operation parameters of the same workstation at the same time. The synchronous response mechanism includes: the synchronization of control parameters of adjacent workstations and the complementarity of operation parameters.

[0091] Step 1013: Determine the workstation control parameters corresponding to the workstation operation parameters; the control parameters and operation parameters of the same workstation correspond to each other. This avoids problems such as "the previous workstation not completing, the subsequent workstation starting in advance" or "the previous workstation exceeding the limit, the subsequent workstation overloaded" caused by asynchronous control commands between adjacent workstations; it ensures that the control commands of the same workstation directly reflect the operation requirements and improves control accuracy.

[0092] Step 1014: Treat each workstation as a work node, sort the work nodes sequentially, and determine the task parameters of the work nodes; treat each workstation as an independent work node, sort it according to the process logic (such as yarn feeding → tension stabilization → winding forming) or physical layout (such as the mechanical sequence from left to right), and clarify the execution order of each node; solve the problem of chaotic execution order of multiple workstations, such as the idling caused by the winding workstation starting before the yarn feeding workstation.

[0093] Step 1015: Based on the task parameters, determine the control parameters and standard operating parameters for each workstation. The task parameters integrate the control parameters (instruction quantities) and standard operating parameters (target quantities), forming a "dual-parameter constraint" for the node. The dual-parameter constraint not only constrains the instructions but also the targets, ensuring that the node operation conforms to both control logic and quality requirements.

[0094] Through the above steps, after task decomposition, multi-station collaborative control can be executed. By using a synchronous response mechanism and work node sequencing, collaborative rules for multi-station parameters can be configured to achieve rule-driven digital control.

[0095] The specific implementation process is as follows: First, the entire yarn winding task is divided into multiple independent sub-tasks, each corresponding to a specific workstation, with clearly defined task content. For each workstation, relevant workstation operation parameters are obtained, and the work process of the workstation is controlled through these parameters. These workstation operation parameters are the control parameters of the winding machine, corresponding to the functional modules and control parameters of the winding machine, such as the needle-punching speed of the needle-punching part of the winding machine.

[0096] At the same time, the corresponding workstation control parameters for these operational parameters are determined so that the control strategy can be adjusted in real time during actual operation.

[0097] The synchronous response mechanism is designed to ensure that during the winding operation of the winding machine, corresponding station operation parameters are generated during the execution of control parameters, thereby ensuring the accuracy and timeliness of control.

[0098] Sequential sorting treats all workstations as a whole and sorts them sequentially to ensure the correct order of operations at each workstation. Simultaneously, each workstation is assigned a unique set of task parameters, including control parameters and standard operating procedure parameters.

[0099] The synchronous response mechanism ensures that any parameter adjustments during the yarn winding process are reflected in the work execution in real time, guaranteeing the accuracy and timeliness of the operation. Treating each workstation as a work node facilitates clearer definition and management of its work content, making the entire production process more modular and easier to manage and optimize. Sequential sorting allows for flexible adjustment of the workstation order, optimizing the production process, reducing waiting time, and providing traceability. In case of a problem, the specific workstation and parameter settings can be quickly located for timely correction.

[0100] Example 3:

[0101] The digital modeling in this application uses a high-precision digital model to map the physical device to the digital model, creating a virtual-real relationship. Specifically:

[0102] Establish a station simulation model for the multi-station winding machine. This model establishes the physical relationships between stations, such as the relationship between motor torque and yarn tension, and the relationship between winding speed and number of layers. This prevents deviations from the model due to factors such as mechanical wear and ambient temperature.

[0103] The workstation simulation model includes a correction mechanism, which is used to correct the workstation simulation model by using the motion control characteristics and standard operating parameters of the multi-station winding machine, so that the workstation simulation model is consistent with the operating parameters and operating characteristics of the actual multi-station winding machine under the same control parameters.

[0104] The correction mechanism compares the model's predicted values ​​with the actual measured values ​​to determine the error, which then triggers adjustments to the model parameters. Ultimately, this ensures that the model outputs parameters consistent with actual operation under the same control parameters. The correction mechanism also ensures that the digital model can serve as a virtual experimental platform, replacing some physical tests and reducing debugging costs.

[0105] Based on the workstation simulation model, a yarn winding environment is built, and the digital parameters of yarn winding are determined. The yarn winding environment simulates the characteristics of yarn materials, ambient temperature, humidity, etc. in the actual production scenario, so that the simulation model can fit the actual environment and determine the key scenario-based digital parameters.

[0106] A digital twin model of a multi-station winding machine is built using digital parameters. The digital twin model is a digital mirror model of the multi-station winding machine. By synchronizing the control parameters (such as motor commands) and operating parameters (such as sensor data) of the physical equipment in real time, the real-time state of the equipment (such as yarn path and tension change curve) is reproduced in digital space.

[0107] The above solution can solve the problem of the disconnect between the built model and the actual scene, and can also realize the synchronization of the digital simulation model between the virtual and the real, thus achieving dynamic digital twin.

[0108] Specifically, in the digital modeling process, a station simulation model of a multi-station winding machine is established to simulate the actual operating state of the machine, including its motion control characteristics, operating parameters, and the specific yarn winding operation. Then, the station simulation model is corrected based on the parameters of the station simulation model.

[0109] The station simulation model is established based on the parameters of an actual winding machine in operation. However, because winding machines are subject to wear and tear, there will be some discrepancies between the model and actual conditions. Therefore, this invention modifies the station simulation model to make it as close as possible to the actual operating state of the winding machine. Based on the modified station simulation model, the yarn winding environment is built, and the digital parameters for yarn winding are determined.

[0110] A digital twin model of a multi-station winding machine is built by using digital parameters, which can also be regarded as a synchronous twin model, thereby realizing digital control.

[0111] The workstation simulation model helps improve the efficiency and standardization of workstation division based on simulation parameters at the initial stage of digitally controlled winding machine development. Then, through model correction, it ensures a high degree of consistency between the model and the actual machine's operating parameters and characteristics, thereby improving the model's accuracy and reliability. Because the above solution is a workstation simulation model, it can simulate various winding tasks, thus reducing trial-and-error costs. Finally, the establishment of a digital twin model facilitates the mapping between virtual and actual production operations, enabling comprehensive supervision of the winding process.

[0112] Example 4:

[0113] See Figure 5 The process of building the digital control model package for each workstation also includes:

[0114] Step 2011: Obtain the synchronization control parameters of each station and encode the station; the synchronization control parameters are the core instructions for multi-station collaboration (such as the yarn feeding speed needing to be synchronized with the tension control parameters). The station code uses a unique identifier to determine the operational function of each station; the coding rules are based on the position and process sequence of the station in the actual scenario, so that each station of the multi-station winding machine has an identity identifier and accurately determines the target station.

[0115] Step 2012: Based on the workstation code, set the working mode response mechanism for each workstation; because different workstations have different functions, response rules can be set by matching the working mode with the workstation code. This achieves function-based response rule matching, thus preventing control failures caused by conflicting response rules.

[0116] Step 2013: Based on the working mode response mechanism, set the control range for the workstation control; the control range is the interval between the maximum and minimum values ​​of the workstation parameters, i.e., the control boundary. Determining the range through the working mode prevents overcompensation and avoids equipment damage or quality defects caused by parameter exceeding limits.

[0117] Step 2014: Import the control area into the digital twin model to determine the digital control model for each workstation. The digital twin model is a twin model. By embedding the control area into the digital twin model, the exclusive control logic for each workstation can be determined, realizing the integration from digital twin to control logic. This allows the digital model to represent the physical state while also enabling real-time control of the workstation equipment.

[0118] The digital control model is also used to synchronize the control parameters of each workstation. By receiving the control parameters of each workstation in real time, the digital control model dynamically adjusts the parameters based on coding, mode, and interval rules to achieve parameter synchronization and synchronous operation of multiple workstations.

[0119] The specific implementation process of the above plan is as follows:

[0120] Based on the digital twin model, the synchronous control parameters of each workstation are obtained, and workstation codes are generated to ensure the uniqueness of each code. After determining the structure and working principle of the multi-station winding machine, digital technology is used to construct a digital control model for each workstation.

[0121] Because the digital twin model of the multi-station winding machine references kinematic and dynamic models during its creation, it can characterize the highly coupled motion relationships between multiple stations. Therefore, the synchronous control parameters of each station can be extracted through the digital control model, allowing for the simulation of how each station interacts with the others and their relative velocities and accelerations.

[0122] Then, station-level encoding is performed on the synchronization control parameters to facilitate mapping them to corresponding variables in the actual control system. This typically involves using some encoding rules or algorithms.

[0123] For example, Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) can convert continuous analog signals into discrete digital signals. Based on the workstation code, a working mode response mechanism is set for each workstation. Each workstation has a working mode response mechanism that defines how each workstation responds to control inputs under different working conditions. In practice, the working mode response mechanism can be implemented through software programming or hardware circuitry.

[0124] In the work mode response mechanism, corresponding response strategies can be defined based on different control inputs and workstation states. For example, for a specific control input, different control strategies need to be adopted to adapt to different workstation states. Furthermore, the work mode response mechanism can be combined with other control strategies, such as voice control, to achieve more advanced control functions.

[0125] Through the work mode response mechanism, control intervals can also be set on the workstation control, that is, a control interval is set on each workstation. The control interval is used to limit the type and parameter range of the control strategy for each workstation.

[0126] During implementation, different workstations are categorized, and corresponding control intervals are defined for the operational data of each category. In practice, by setting up a controller detection function, when the controller detects a specific control input, it selects an appropriate control interval based on that input and the current workstation status, and maps the control input to the corresponding control strategy within that interval. The control intervals are then imported into the digital twin model to determine the digital control model for each workstation. This process includes mapping the control intervals into the model, calculating the weight coefficients of each workstation's control interval, minimizing the model's loss function, and so on.

[0127] In this way, a separate digital control model can be provided for each workstation. The separate digital control model is built according to the specific working conditions, working standards and workstation code of each workstation, and can be adjusted and optimized individually.

[0128] By utilizing the defined digital control model for each workstation, the controller can synchronously respond to the control parameters of each workstation. After determining the digital control model for each workstation, the controller can apply the model to the control parameters of each workstation and synchronously respond to the control response of each workstation, making the control response of each workstation more accurate and real-time.

[0129] Example 5:

[0130] The sequential job planning model includes the following integration steps:

[0131] Based on the station sequence, determine the work sequence of each station; the station sequence is an inherent attribute of multi-station winding machines. By clearly defining the position of each station in the sequence, the execution order can be prevented from being chaotic. For example, the winding station may start before the yarn feeding station, causing idling.

[0132] Based on the job sequence, a sequential job model architecture is built. The sequential job model architecture is a logical framework for multi-workstation collaboration. It transforms the job sequence into the relationship between different workstations and into a computable model structure, preventing the fragmentation or chaos of multi-workstation control logic.

[0133] In the sequential operation model architecture, the digital input interface and digital output interface of the digital control model corresponding to each workstation are determined. The digital input interface and digital output interface are data transmission ports between adjacent workstations. The input interface receives the output data of the preceding workstation (such as the output parameter "yarn feeding amount" of the yarn feeding workstation), and the output interface transmits data to the following workstation (such as the output parameter "stabilized tension" of the tension workstation). In actual implementation, the digital input interface and digital output interface are configured with the Modbus protocol.

[0134] The digital control models of different workstations are sequentially merged through the digital input and output interfaces of adjacent workstations. By physically connecting the output interface of the previous workstation with the input interface of the subsequent workstation, bus interconnection is achieved, and the independent workstation control is transformed into a globally collaborative control model. This allows the control logic of the subsequent workstation to directly depend on the output results of the previous workstation, thereby achieving workstation collaboration and synchronization.

[0135] The digital input interface is equipped with a parameter monitoring mechanism. This mechanism ensures that the next workstation executes the corresponding workflow when the output parameters of the previous workstation's digital output interface match the standard operating parameters of the next workstation's digital input interface. The mechanism compares the actual output parameters of the previous workstation with the standard operating parameters of the subsequent workstation in real time. If they match, the subsequent workstation is triggered to start; otherwise, a delay alarm is triggered. This prevents the subsequent workstation from "prematurely executing" (e.g., the tension workstation starting when yarn feeding is insufficient, leading to unstable tension) or "delaying" (e.g., the tension workstation failing to start in time after yarn feeding, resulting in efficiency loss) due to incomplete workstation operations, thus intercepting subsequent chain defects caused by errors in the previous workstation.

[0136] Traditional multi-station control primarily uses time relays, but the fixed delay of time relays cannot adapt to fluctuations in actual operating parameters. This application achieves precise triggering through a digital interface and parameter monitoring mechanism, eliminating the need to adjust the delay of time relays or perform manual training. By replacing time-driven control with data-driven control, it ensures coordinated control of the sequential operation planning model at each workstation.

[0137] This application determines the work sequence for each workstation based on the workstation order. This ensures a clear and unambiguous operational sequence for each workstation and identifies the specific work data for each workstation. Based on the work sequence, a sequential work model architecture is constructed, allowing for hierarchical design of the sequential work model and determination of hierarchical control parameters. Within this architecture, the digital input and output interfaces of the digital control model corresponding to each workstation are defined, establishing a bridge for data exchange and control between workstations. Then, through the digital input and output interfaces of adjacent workstations, the digital control models of different workstations are sequentially merged, enabling information sharing and collaborative work between different workstations.

[0138] Finally, a parameter monitoring mechanism was implemented at the digital input interface. This mechanism monitors whether the input parameters of the previous workstation's digital output interface are consistent with the standard operating parameters output by the next workstation's digital output interface. Upon consistency determination, the corresponding work process is automatically executed. This ensures the continuity and stability of the work, improves work efficiency, and reduces the error rate.

[0139] This application organically combines sequential operation planning model, digital control model, digital input interface and parameter monitoring mechanism to achieve efficient and stable operation of multi-station winding machine and provide control accuracy.

[0140] Example 6:

[0141] The sequential job planning model in this application includes a supervision layer, a control layer, and a regulation layer; wherein,

[0142] The supervision layer is configured with operation parameter recognition scripts for different workstations;

[0143] The control layer is configured with authentication scripts for the operation parameters of different workstations;

[0144] The adjustment layer is configured with operation parameter optimization scripts for different workstations.

[0145] In the process of building the sequential operation model, a control layer was implemented. The design of the control layer can avoid functional confusion and chaotic parameter processing logic.

[0146] The monitoring layer is used to configure different operation parameter recognition scripts. These scripts automatically identify and extract relevant operation parameters, such as machine speed and pressure setpoints, based on actual work data at each workstation. The scripts acquire raw parameters (such as analog signals from tension sensors and pulse signals from encoders) from each workstation through data acquisition interfaces (e.g., sensors, PLCs) and convert them into recognizable digital parameters. The scripts need to adapt to the parameter types of different workstations, resulting in inconsistent parameter formats across multiple workstations.

[0147] The control layer is used to configure different job parameter authentication scripts. These scripts perform secondary verification and validation of job parameters from the monitoring layer to ensure data accuracy. Only authenticated parameters can proceed to the next stage. The scripts validate the identification parameters passed from the monitoring layer using preset standard parameter ranges. The job parameter authentication scripts can also determine parameter validity based on rules, preventing the occurrence of unidentified abnormal parameters.

[0148] The adjustment layer is used to configure different job parameter optimization scripts. These scripts perform real-time data analysis and processing on certified parameters, optimizing and adjusting them according to actual conditions to achieve the best working results. For example, they can dynamically adjust certain parameter settings in response to changes in equipment operating status to ensure stable equipment operation.

[0149] Example 7:

[0150] The operation parameter identification script in this application includes the following parameter identification steps:

[0151] Based on the workstation sequence, control templates are set for different workstations. The workstation sequence is an inherent attribute of the multi-station winding machine (such as the process sequence of yarn feeding → tension → winding). The control template is a parameter response template for each workstation. The control template constrains the parameter output format of each workstation through predefined fields, so that the parameter format of the workstation is not chaotic.

[0152] The control template is used to respond to the control parameters of different workstations and upload the response information of the control parameters. The responsive template of the control template interacts with the workstation controller, i.e., the PLC of the multi-station winding machine. The response information includes the original parameter values, acquisition time, equipment identification and other metadata. The data is uploaded for monitoring through the communication protocol, so that the data of the workstation can be actively collected and fully recorded.

[0153] Based on the response information and control template, the identified operational parameters are determined, and their status is uploaded. Other operational parameters are standardized results of the response information. Status upload involves attaching status indicators such as valid, invalid, and abnormal to the operational parameters, thereby quickly filtering out data that requires key monitoring.

[0154] This application sets up control templates according to the order of workstations. These templates can respond to control parameters from different workstations and quickly upload response information. Then, using the control templates and response information, specific operational parameters need to be identified, including but not limited to speed, pressure, and temperature. Finally, the specific operational parameters are uploaded, achieving comprehensive identification and recording of operational parameters. This allows for the uploading of operational parameters, including the work process and status, enabling clear recording of data for each work operation.

[0155] Example 8:

[0156] The job parameter authentication script includes the following execution steps:

[0157] Obtain the operation control parameters and operation standard parameters for each workstation; the operation control parameters are the real-time parameters during the actual operation of the workstation, and the operation standard parameters are the ideal parameters required by the design or process.

[0158] A response authentication model is built based on control parameters, and an identification authentication model is built based on operational parameters. The response authentication model is a verification model of the dynamic behavior of the workstation, establishing the workstation's response pattern (a linear relationship pattern in which both speed and tension increase) through control parameters. The identification authentication model is a "static attribute verifier," establishing the workstation's characteristic pattern based on operational parameters (tension needs to be within a pre-configured tension range, which is a unique feature of the workstation).

[0159] The response authentication model and the identification authentication model have an associated adjustment mechanism in the same workstation sequence. The workstation sequence (e.g., yarn release → tension → winding) determines the dependency relationship between workstations (e.g., the parameters of the tension workstation are affected by the yarn release workstation). The associated adjustment mechanism is a "collaborative bridge" between the two models (e.g., when the response model detects an abnormal yarn release speed, it triggers the identification model to adjust the standard parameters of the tension workstation). By sharing workstation sequence information, the models can achieve hierarchical awareness and prevent model scheduling conflicts.

[0160] The system uses a response authentication model to acquire response information from different workstations and determine whether the response information is the first abnormal data. When the response information is the first abnormal data, the system adjusts the standard operation parameters through a correlation adjustment mechanism. The response information is a feedback signal of the dynamic behavior of the workstation, and the first abnormal data refers to behavior or operation that does not conform to expectations. The correlation adjustment mechanism analyzes the causes of the abnormality and adjusts the standard operation parameters to prevent abnormal dynamic behavior and rigidity of standard parameters.

[0161] By identifying and authenticating the model, the system acquires operational information from different workstations and determines whether this information constitutes a second type of anomalous data. When such information is identified as a second type of anomalous data, the system adjusts the operational control parameters through a correlation adjustment mechanism. Operational information is a characteristic signal of the workstation's static attributes.

[0162] The response authentication model and the identification authentication model are constructed based on the k-means clustering algorithm, the linear support vector machine algorithm, and unlabeled sample data. The second type of outlier data refers to static attributes that do not meet the standards; the correlation adjustment mechanism adjusts the operation control parameters by analyzing the causes of the anomalies.

[0163] The response authentication model and identification authentication model of this application verify parameters from different dimensions to achieve full coverage of anomalies.

[0164] This application establishes a response authentication model that can provide corresponding machine behavior responses based on the control parameters of different workstations. It also establishes an identification authentication model to respond to and classify different operational behaviors. Then, through these two models and an associated adjustment mechanism, the two models collaborate to achieve more accurate parameter adjustments.

[0165] In practice, the response authentication model can obtain response information from different workstations. If the response information is the first abnormal data, and it is only the first abnormality, the compensation purpose can be achieved by adjusting the standard work parameters. If the identification authentication model obtains the second abnormal data, it indicates that the control parameters are also incorrect, thus requiring adjustment of the work control parameters through a correlation adjustment mechanism. Finally, in actual operation, the response authentication model and the identification authentication model are constructed based on the k-means clustering algorithm / linear support vector machine algorithm and unlabeled sample data.

[0166] Example 9:

[0167] The job parameter optimization script in this application includes the following execution steps:

[0168] The status parameters of the job parameter identification script and the job parameter authentication script are counted separately.

[0169] Identify abnormal data and control parameters in different processes, and perform data statistics;

[0170] Based on the data statistics, determine the number of times abnormal data occurs at the workstation under different control parameters, and optimize the workstation control parameters accordingly.

[0171] This invention comprises three steps. The first step involves statistically analyzing the status parameters of the job parameter identification script and the job parameter authentication script to clarify the specific situation of each parameter under different states and perform status analysis. Status parameters are the labels of the execution results of the identification and authentication scripts. Statistical analysis of these status parameters can obtain key indicators such as the format error rate in the identification stage and the compliance rate in the authentication stage, reflecting the health of the parameter processing flow and preventing a one-sided focus on only the final result.

[0172] The second step involves identifying abnormal data and control parameters in different processes and performing detailed statistical analysis on these parameters. This is to determine which parameters exhibited anomalies in each process and what the characteristics of these anomalies were. In-depth analysis of this data helps identify which parameter control strategies need optimization. Abnormal data are problem parameters flagged during the identification or certification phase, while control parameters are adjustable variables in the workstation operation. Data statistics, by linking abnormal data with control parameters, prevent ambiguity in the correlation between anomalies and control parameters.

[0173] The third step involves analyzing the statistical results to determine the frequency of abnormal data occurrences at each workstation under different control parameters, and then optimizing the workstation control parameters based on this data. This approach enables refined management and optimization of the work process, thereby improving the efficiency and quality of the entire production line. By comparing the number of abnormal occurrences corresponding to different control parameters, low-abnormality control parameter values ​​are identified and set as new control parameters for optimization.

[0174] Example 10:

[0175] This application also proposes a digital control system for a multi-station winding machine, including:

[0176] Standard Operation Module 10: Obtain the yarn winding task, decompose the yarn winding task, and determine the standard operation parameters for each workstation;

[0177] Digital control module 20: Based on standard operating parameters, digital modeling is performed for each workstation to determine the digital control model for each workstation;

[0178] Sequential Operation Module 30: Obtains the workstation sequence and integrates the digital control of each workstation based on the workstation sequence to generate a sequential operation planning model;

[0179] Supervision and adjustment module 40: Through the sequential operation planning model, it supervises the operation data of each workstation, judges whether there is an operation error, and adjusts the operation parameters when there is an operation error.

[0180] The principle behind the above technical solution is as follows:

[0181] As attached Figure 2 As shown, in order to solve the control problem of multi-station winding machines in the prior art, this application proposes the above-mentioned solution in the specific implementation process:

[0182] First, the standard operation module 10 needs to acquire the yarn winding tasks to be executed. During this process, the yarn preparation task determines the yarn inspection, cleaning, and tension adjustment. The winding specifications are set to determine parameters such as yarn diameter, tension, and speed. Additionally, quality checks are performed on issues such as inconsistent yarn tension, fuzzing, and yarn breakage during the winding process. These tasks are then broken down in detail, aiming to reduce complex tasks into individually manageable sub-tasks, enabling synchronous task processing and parallel task deployment. Each sub-task corresponds to the work task of each workstation, thereby determining the standard operation parameters for each workstation and achieving work control.

[0183] The digital control module 20 sets standard operating parameters for each workstation based on the nature of the task and the characteristics of the machine. These parameters may include speed, pressure, tension, etc., which are the basic conditions that each workstation must follow during execution.

[0184] We digitally model the parameters of each workstation and establish corresponding mathematical models. These models can help us understand and predict worker behavior when performing tasks, thus providing a basis for adjusting tasks in the next step.

[0185] The sequential operation module 30 integrates the digital control models of all workstations according to their order, forming a unified task planning model. This model helps optimize the entire production process and improve efficiency.

[0186] The monitoring and adjustment module 40 monitors the operational data of each workstation through a task planning model. By comparing the actual production situation with the expected target situation, we can identify and correct potential errors. If an error is found, we can fine-tune the corresponding control parameters to achieve the desired effect.

[0187] The primary function of this application is to decompose the yarn winding task, effectively plan the work of each station, reduce waiting and downtime during production, and thus improve overall production efficiency. Digital modeling of standard operating parameters ensures that each station's operations follow uniform standards, reducing human error and improving product quality and consistency. Monitoring the operational data of each station through a sequential operation planning model allows for real-time monitoring of the process, timely detection and handling of errors, thereby enhancing quality control. In terms of flexibility and adaptability, the digital control of each station can be integrated based on the station sequence, enabling rapid adaptation to different production tasks and station sequences, making the production process more flexible and adaptable. Furthermore, adjusting operational parameters through the digital control model reduces the need for physical adjustments and calibrations to equipment, thus lowering maintenance costs. During the monitoring of operational data, the ability to adjust operational errors also ensures reduced reliance on manual labor, achieving full digitalization.

[0188] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for the digital control of a multi-station winding machine, characterized in that, The method comprises the following steps: obtaining a winding task, decomposing the winding task, and determining standard operation parameters of each station; modeling each station according to the standard operation parameters to determine a digital control model of each station; obtaining a station sequence and fusing the digital control model of each station based on the station sequence to generate a sequential operation planning model; supervising the operation data of each station through the sequential operation planning model, judging whether there is an operation error, and adjusting the operation parameters when there is an operation error; the winding task decomposition comprises: obtaining station operation parameters of each station in the winding task, and determining station control parameters corresponding to the station operation parameters; wherein the station control parameters and the station operation parameters have a synchronous response mechanism at the same time, the synchronous response mechanism comprises control parameter synchronization and operation parameter complementation of adjacent stations, and the station control parameters of the same station correspond to the station operation parameters at the same time; each station is regarded as an operation node, the operation nodes are sequentially sorted, and task parameters of the operation nodes are determined; wherein, the task parameters comprise the control parameters and the standard operation parameters of each station; the digital modeling comprises: establishing a station simulation model of the multi-station winding machine; wherein the station simulation model comprises a correction mechanism, the correction mechanism is used for correcting the station simulation model through the motion control characteristics of the multi-station winding machine and the standard operation parameters, and making the station simulation model consistent with the running parameters and running characteristics of the actual running multi-station winding machine under the same control parameters; determining winding digital parameters according to the station simulation model; building a digital twin model of the multi-station winding machine through the digital parameters; the sequential operation planning model comprises the following fusion steps: determining the operation sequence of each station according to the station sequence; building a sequential operation model architecture of the sequential operation according to the operation sequence; in the sequential operation model architecture, the digital input interface and the digital output interface of the digital control model corresponding to each station are determined; the digital control models of different stations are sequentially fused through the digital input interface and the digital output interface of adjacent stations; wherein the digital input interface is configured with a parameter monitoring mechanism, the parameter monitoring mechanism is used for executing the corresponding operation process when the parameter monitoring mechanism monitors that the output parameters of the last station digital output interface are consistent with the standard operation parameters output by the next station digital input interface; the sequential operation planning model comprises a supervision layer, a control layer and an adjustment layer; wherein, the supervision layer is configured with an operation parameter identification script of different stations; the control layer is configured with an operation parameter authentication script of different stations; the adjustment layer is configured with an operation parameter optimization script of different stations; the operation parameter authentication script comprises the following execution steps: obtaining the operation control parameters and the operation standard parameters of each station; building a response authentication model according to the control parameters and building an identification authentication model according to the operation parameters; wherein, the response authentication model and the identification authentication model have an associated adjustment mechanism at the same station sequence; obtaining response information of different stations through the response authentication model, and judging whether the response information is first abnormal data; When the response information is the first abnormal data, the work standard parameter is adjusted through the correlation adjustment mechanism; The work information of different stations is obtained through the identification authentication model, and it is judged whether the work information is second abnormal data; When the work information is the second abnormal data, the work control parameter is adjusted through the correlation adjustment mechanism.

2. A multi-station winding machine digitizing control method as claimed in claim 1, characterized in that, The determination of the digital control model of each station comprises: Synchronous control parameters of each station are obtained, and station coding is performed; According to the station coding, the work mode response mechanism of each station is set; According to the work mode response mechanism, the control interval of station control is set; The control interval is imported into the digital twin model to determine the digital control model of each station; The digital control model is also used for synchronous response of the control parameters of each station.

3. A multi-station winding machine digitizing control method as claimed in claim 1, characterized in that, The work parameter identification script comprises the following parameter identification steps: According to the station sequence, the control template of different work stations is set; wherein, The control template is used to respond to the control parameters of different stations, and the response information of the control parameters is uploaded; According to the response information and the control template, the identified work parameters are determined, and the state of the work parameters is uploaded.

4. A multi-station winding machine digitizing control method as claimed in claim 1, characterized in that, The work parameter optimization script comprises the following execution steps: The state parameters of the work parameter identification script and the work parameter authentication script are counted respectively; The abnormal data and control parameters in different processes are determined, and data statistics are performed; According to the data statistics, it is judged that the number of abnormal data of the station under different control parameters, and the station control parameter is optimized.

5. A multi-station winding machine digital control system, characterized by, Comprise: Standard work module: obtain the winding task, perform winding task decomposition, and determine the standard work parameters of each station; Digital control module: according to the standard work parameters, digital modeling is carried out for each station to determine the digital control model of each station; Sequential work module: obtain the station sequence, and fuse the digital control of each station based on the station sequence to generate a sequential work planning model; Supervision adjustment module: through the sequential work planning model, the work data of each station is supervised to judge whether there is work error, and when there is work error, the work parameter adjustment is performed; The winding task decomposition comprises: Obtain the station work parameters of each station in the winding task, and determine the station control parameters corresponding to the station work parameters; Wherein, the station control parameters and the station work parameters have a synchronous response mechanism at the same time, the synchronous response mechanism comprises the control parameter synchronization and the work parameter complementation of adjacent stations, and the station control parameters of the same station correspond to the station work parameters at the same time; Each station is regarded as a work node, the work nodes are sequentially sorted, and the task parameters of the work nodes are determined; wherein, The task parameters comprise the control parameters and the standard work parameters of each station; The digital modeling comprises: Establish a station simulation model of the multi-station winding machine; Wherein, the station simulation model comprises a correction mechanism, which is used for correcting the station simulation model through the motion control characteristics and the standard work parameters of the multi-station winding machine, and making the station simulation model consistent with the running parameters and running characteristics of the actual running multi-station winding machine under the same control parameters; According to the station simulation model, a winding environment is built, and winding digital parameters are determined; A digital twin model of the multi-station winding machine is built through the digital parameters; The sequential operation planning model includes the following fusion steps: According to the station sequence, the operation sequence of each station is determined; According to the operation sequence, a sequential operation model architecture is built; In the sequential operation model architecture, the digital input interface and the digital output interface of the digital control model corresponding to each station are determined; The digital control models of different stations are sequentially fused through the digital input interface and the digital output interface of adjacent stations; The digital input interface is configured with a parameter monitoring mechanism, which is used to monitor whether the output parameters of the digital output interface of the previous station and the standard operation parameters output by the digital input interface of the next station are consistent, and if so, the next station executes the corresponding operation process; The sequential operation planning model includes a supervision layer, a control layer and an adjustment layer; wherein, The supervision layer is configured with an operation parameter identification script of different stations; The control layer is configured with an operation parameter authentication script of different stations; The adjustment layer is configured with an operation parameter optimization script of different stations; The operation parameter authentication script includes the following execution steps: Get the operation control parameters and operation standard parameters of each station; According to the control parameters, a response authentication model is built, and according to the operation parameters, an identification authentication model is built; wherein, The response authentication model and the identification authentication model have an associated adjustment mechanism in the same station sequence; Through the response authentication model, response information of different stations is obtained, and it is judged whether the response information is first abnormal data; When the response information is the first abnormal data, the associated adjustment mechanism is used to adjust the operation standard parameters; Through the identification authentication model, the operation information of different stations is obtained, and it is judged whether the operation information is second abnormal data; When the operation information is the second abnormal data, the associated adjustment mechanism is used to adjust the operation control parameters.

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