A non-woven fabric packaging bag intelligent production line collaborative control system

Abstract

The application discloses a non-woven fabric packaging bag intelligent production line cooperative control system, and relates to the technical field of intelligent production, solves the problem that the data storage and processing capacity of a traditional PLC system has been difficult to meet large-scale and high-frequency parameter monitoring and control requirements, and the application converts the correlation between equipment operation parameters and control parameters into standardized control codes by means of a cloud database and a historical feature analysis mechanism, breaks the time-consuming bottleneck of the traditional PLC control system when multiple parameters change, i.e., "adjusting one by one and feeding back step by step", when the operation parameter adjustment is monitored, the control feature selection end can directly match the historical control code to quickly output the control trend, avoids the complex real-time data analysis process, greatly shortens the control response time, effectively improves the control efficiency of the production line, and guarantees the continuity and stability of the non-woven fabric packaging bag production process.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, specifically to a collaborative control system for an intelligent production line of nonwoven packaging bags. Background Technology

[0002] With the advancement of environmental protection policies and the upgrading of consumer demand, non-woven packaging bags, with their advantages such as biodegradability and high toughness, have seen a continuous increase in market demand, driving the rapid development of its production industry towards large-scale and multi-category production. Currently, non-woven packaging bag production lines have gradually achieved intelligent equipment transformation, covering multiple processes such as raw material proportioning, forming, printing, and cutting, involving dozens of types of equipment such as rollers, conveyor belts, and printing machines. Each piece of equipment needs to achieve coordinated operation through precise parameter control to ensure the production quality and efficiency of non-woven packaging bags of different specifications and materials.

[0003] Existing production line control largely relies on traditional PLC (Programmable Logic Controller) systems. Their core logic achieves control response through preset relationships between single parameters, such as a fixed ratio between rotor speed and drive current. However, in actual production scenarios, factors such as the type of nonwoven fabric (e.g., PP spunbond nonwoven fabric, SMS composite nonwoven fabric), packaging bag thickness, and the complexity of printed patterns all require adjustments to multiple sets of operating parameters (e.g., speed, pressure, temperature). In such cases, traditional PLC systems must analyze and adjust the changes in each parameter sequentially according to preset priorities.

[0004] This "one-by-one processing" model has significant limitations: on the one hand, the logical judgment and calculation process when adjusting multiple parameters takes a long time, which can easily lead to a lag in control response, causing fluctuations in the operating status of the equipment, and thus affecting the forming accuracy or printing clarity of the non-woven packaging bags; on the other hand, the parameter correlation rules under different production scenarios need to be manually reprogrammed and entered, the system has poor adaptability to new specifications of products, which increases the workload of operators and also restricts the rapid production changeover capability of the production line.

[0005] Meanwhile, existing control systems lack effective utilization of historical operating data, and the correlation between parameter change trends of various devices and control effects has not formed a standardized mapping system. When abnormal parameter fluctuations occur on the production line, troubleshooting and adjustments must rely on the experience of technical personnel, which not only results in low fault handling efficiency but also makes it difficult to predict potential control risks in advance.

[0006] Furthermore, with the increasing intelligence of production lines, the number of equipment and the number of parameters are constantly increasing. The data storage and processing capabilities of traditional PLC systems are no longer sufficient to meet the needs of large-scale, high-frequency parameter monitoring and control. There is an urgent need to build a control system that can integrate historical data, achieve rapid response of multi-parameter collaboration, and has adaptive capabilities, so as to break through the current control bottleneck of intelligent production of non-woven packaging bags. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a collaborative control system for an intelligent production line of nonwoven packaging bags, which solves the problem that the data storage and processing capabilities of traditional PLC systems are insufficient to meet the needs of large-scale, high-frequency parameter monitoring and control.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a collaborative control system for an intelligent production line of nonwoven packaging bags, comprising:

[0009] The data feature analysis module extracts the operating and control parameters of individual devices within the production line and generates trend change charts for the corresponding devices. From these trend change charts, it identifies and records historical characteristics generated by the corresponding devices within a historical period. The specific method is as follows:

[0010] Using the current time as the calibration time, confirm a set of traceability cycles, which are preset cycles. For a single device, confirm the parameter change curve of the operating parameters within the traceability cycle. The horizontal axis of the curve is the time line, and the vertical axis is the specific value of the operating parameters.

[0011] From the parameter change curve of a single operating parameter, confirm the change trend of different operating parameters within adjacent time periods. Determine the operating parameter before the adjacent time period as YX1 and the operating parameter after the adjacent time period as YX2. Use the formula: (YX2-YX1) = change trend. With a 1-minute interval between each adjacent time period, simultaneously confirm the parameter change curve associated with the control parameter, and then confirm the change trend of the control parameter between adjacent time periods, and record it as the control trend.

[0012] According to the set sorting method of operating parameters, sort the change trends associated with each operating parameter in the same period, generate a set of change trend columns, and place the control trends associated with the same period after the change trend columns.

[0013] Confirm multiple different control trends associated with the same trend column, generate control intervals belonging to the corresponding trend column, and record the generated control intervals as the control codes of the corresponding trend columns. Different control codes exist for different trend columns.

[0014] If a certain trend column has only one control trend, then the only control trend is recorded as the control code of the corresponding trend column.

[0015] The data monitoring center monitors the various operating parameters of individual equipment in the intelligent production line in real time. When it detects that one or more operating parameters have been adjusted, it directly transmits the adjusted operating parameters to the control feature selection terminal.

[0016] The control feature selection process involves confirming the parameter change process of each operating parameter of a single device based on the monitoring process, identifying the change features from the change process, and then selecting the control feature based on the recorded control code. The specific method is as follows:

[0017] Record the current time as the baseline time and the previous time as the comparison time. Confirm the change trend of the operating parameters of a single device belonging to the same item. Based on the operating parameters associated with the baseline time and the comparison time, confirm the change trend associated with the corresponding item. Sort the change trends of several different operating parameters at the current time according to the set operating parameter sorting method to confirm the change trend column.

[0018] Identify whether the trend column exists within the recorded trend column:

[0019] If it exists, the control code is selected directly and transmitted to the control parameter output terminal;

[0020] If it does not exist, the current device will be marked as the device to be confirmed, and the control parameter logic confirmation terminal will be executed directly.

[0021] Preferred options also include:

[0022] The cloud database stores a large number of operating and control parameters for different equipment within the production line.

[0023] Preferably, the control parameter output terminal, based on the selected control code, starts from the minimum value of the interval and gradually increases the control parameter. The control trend of the increase gradually increases in each unit time until the monitored trend column is consistent with the trend column sorted by the control feature selection terminal. The current control trend is confirmed and executed until the various operating parameters of the device are consistent with the adjusted operating parameters, and the current control parameters remain unchanged.

[0024] Preferably, the control parameter logic confirmation terminal directly confirms the control trend associated with the calibrated device to be confirmed through the PLC control system, and directly transmits the confirmed control trend to the control parameter output terminal.

[0025] Preferably, the control parameter output terminal executes the trend change process of the control parameters according to the control trend confirmed by the device to be confirmed, and stops when the various operating parameters of the device are consistent with the adjusted operating parameters, keeping the current control parameters unchanged.

[0026] This invention provides a collaborative control system for an intelligent production line of nonwoven packaging bags. Compared with existing technologies, it has the following advantages:

[0027] This system leverages cloud databases and historical feature analysis mechanisms to transform the correlation between equipment operating parameters and control parameters into standardized control codes. This breaks through the time-consuming bottleneck of traditional PLC control systems that require "step-by-step adjustment and feedback" when multiple parameters change. When an adjustment in operating parameters is detected, the selected control feature can directly match historical control codes to quickly output control trends, avoiding complex real-time data analysis processes, significantly shortening control response time, effectively improving the control efficiency of the production line, and ensuring the continuity and stability of the non-woven packaging bag production process.

[0028] In terms of control precision and adaptability, the system accurately captures the control patterns corresponding to multiple combinations of operating parameters through a coding method of "change trend column + control interval". The control parameter output adopts a "gradual improvement + real-time verification" mode during control execution, dynamically optimizing control parameters based on actual feedback from operating parameters to ensure the accuracy of the control process. Simultaneously, for new scenarios where no historical code is found, supplementary processing is performed through linkage between the control parameter logic confirmation terminal and the PLC control system. This retains the reliability of traditional control systems while achieving flexible adaptation to different types of non-woven fabric processing scenarios, thus broadening the system's application scope. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the principle framework of the present invention. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] First Embodiment

[0032] Please see Figure 1 This application provides a collaborative control system for an intelligent production line of nonwoven packaging bags, including a cloud database, a data feature analysis terminal, a data monitoring center, a control feature selection terminal, a control parameter logic confirmation terminal, and a control parameter output terminal. The cloud database, data feature analysis terminal, control feature selection terminal, and control parameter output terminal are electrically connected sequentially from the output node to the input node, and the data monitoring center, control feature selection terminal, control parameter logic confirmation terminal, and control parameter output terminal are electrically connected sequentially from the output node to the input node.

[0033] Among them, the cloud database stores a large number of operating parameters and control parameters of different equipment in the production line. The operating parameters are generally set by relevant personnel. When processing different types of non-woven fabrics, different operating parameters need to be set. Different operating parameters are associated with different control parameters to ensure the normal operation of the production line.

[0034] The data feature analysis module extracts the operating and control parameters of individual equipment within the production line and generates trend change charts for the corresponding equipment. From these trend change charts, it identifies and records the historical features generated by the corresponding equipment within a historical period. Specifically, different operating parameters exhibit different change processes, and these different change processes are associated with different change trends, which synchronously drive the control parameters to follow suit. Therefore, in order to more accurately identify the change processes between parameters, the corresponding historical features are recorded based on the actual change process of the trends. This allows for a comprehensive description of the corresponding parameter change trends, facilitating subsequent feature selection.

[0035] The specific method for confirming the historical characteristics generated by a single device within a historical period is as follows:

[0036] Using the current time as the calibration time, confirm a set of traceability cycles, which are preset cycles, generally 480h. For a single device, confirm the parameter change curve of the operating parameters within the traceability cycle. The horizontal axis of the curve is the time line, and the vertical axis is the specific value of the operating parameters.

[0037] From the parameter change curve of a single operating parameter, confirm the change trend of different operating parameters within adjacent time periods. Determine the operating parameter before the adjacent time period as YX1 and the operating parameter after the adjacent time period as YX2. Use the formula: (YX2-YX1) = change trend. With a 1-minute interval between each adjacent time period, simultaneously confirm the parameter change curve associated with the control parameter, and then confirm the change trend of the control parameter between adjacent time periods, and record it as the control trend.

[0038] According to the set sorting method of operating parameters, sort the change trends associated with each operating parameter in the same period, generate a set of change trend columns, and place the control trends associated with the same period after the change trend columns.

[0039] Confirm multiple different control trends associated with the same trend column, generate control intervals belonging to the corresponding trend column, and record the generated control intervals as the control codes of the corresponding trend column. Different control codes exist for different trend columns. If a trend column has only one set of control trends, then the only set of control trends is recorded as the control code of the corresponding trend column.

[0040] Specifically, when a machine is running, the operating parameters and control parameters are synchronously related. However, a group of devices is not only related to one set of operating parameters. The control parameters can be understood as source parameters, most appropriately as voltage or current. Each set of operating parameters will drive the control parameters to change accordingly when they change in different states. Therefore, the process of change between parameter trends can effectively facilitate the subsequent control process without going through the data analysis process of the PLC terminal. The control parameters can be quickly determined and executed, thereby improving the control efficiency of the corresponding intelligent production line and effectively reducing the control time.

[0041] In the initial stage, adjusting the control parameters based on the changing trends of operating parameters is done after all operating parameters and associated control parameters have been set and are in a standard state. Each parameter's change is handled by corresponding logic. For example, if the rotational speed of a certain rotor needs to be increased by 100 r / min, the control current needs to be increased by 100A. This logic is pre-set. When a single operating parameter changes, the corresponding PLC control system can quickly provide feedback. However, when multiple operating parameters change, the PLC control system will sequentially confirm the control parameters based on the changing trends. For example, after a change in rotational speed, the adjusted current is confirmed, and then the current is adjusted again based on the torque change trend. This process is gradual and consumes time, leading to untimely control.

[0042] Second Embodiment

[0043] This embodiment is a further embodiment of the first embodiment, mainly targeting the process of selecting control parameters. Based on the data monitoring process of the data monitoring center, the change process of the corresponding equipment with respect to the corresponding operating parameters is confirmed. Then, based on the control feature selection end, the control trend is selected from the recorded control code to select the control feature.

[0044] The data monitoring center monitors the various operating parameters of individual equipment in the intelligent production line in real time. When it detects that one or more operating parameters need to be adjusted, it directly transmits the adjusted operating parameters to the control feature selection terminal.

[0045] The control feature selection process involves confirming the parameter change process of each operating parameter of a single device based on the monitoring process, identifying the change features from the change process, and then selecting the control features based on the recorded control codes. The specific method for selecting the control features is as follows:

[0046] Record the current time as the baseline time and the previous time as the comparison time. Confirm the change trend of the operating parameters of a single device belonging to the same item. Based on the operating parameters associated with the baseline time and the comparison time, confirm the change trend associated with the corresponding item. Sort the change trends of several different operating parameters at the current time according to the set operating parameter sorting method to confirm the change trend column.

[0047] Identify whether the trend column exists within the recorded trend column:

[0048] If it exists, the control code is selected directly and transmitted to the control parameter output terminal;

[0049] If it does not exist, the current device will be directly marked as the device to be confirmed, and the control parameter logic confirmation terminal will be executed directly.

[0050] Specifically, when different nonwoven packaging bag production processes are executed, the operators need to adjust the parameters of the associated equipment. When the system detects the parameter adjustment, it needs to quickly confirm the control parameters to improve the data processing process, increase processing efficiency, avoid affecting the subsequent nonwoven packaging bag production process, and increase the production rate of nonwoven packaging bags.

[0051] In the control parameter output terminal, based on the selected control code, the control parameter is gradually increased starting from the minimum value of the interval. The control trend of the increase gradually increases in each unit of time (0.1 each time, with an interval of 1 second), and the change trend column of various operating parameters of the corresponding equipment is monitored in real time (also checked once every 1 second). It stops when the monitored change trend column is consistent with the change trend column sorted at the control feature selection terminal, confirms the current control trend and executes it, and stops when the various operating parameters of the equipment are consistent with the adjusted operating parameters, keeping the current control parameters unchanged.

[0052] Specifically, in the actual operation process, after the adjustment parameters are determined, the adjustment parameters will have a numerical ratio relative to the original parameters. After the parameters are set, they will change according to the numerical ratio, which will generate a trend, thereby finding the optimal control trend to achieve the relevant processing of rapid control.

[0053] Among them, the control parameter logic confirmation terminal, for the calibrated device to be confirmed, directly confirms the control trend associated with the device to be confirmed through the PLC control system, and directly transmits the confirmed control trend to the control parameter output terminal;

[0054] Among them, the control parameter output terminal executes the trend change process of the control parameters according to the control trend confirmed by the device to be confirmed, and stops when the various operating parameters of the device are consistent with the adjusted operating parameters, keeping the current control parameters unchanged.

[0055] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0056] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A collaborative control system for an intelligent production line of nonwoven packaging bags, characterized in that, include: The data feature analysis end extracts the operating parameters and control parameters of individual equipment in the production line and generates a trend change chart for the corresponding equipment. Then, from the trend change chart, it identifies and records the historical features generated by the corresponding equipment in the historical period. The data monitoring center monitors the various operating parameters of individual equipment in the intelligent production line in real time. When it detects that one or more operating parameters have been adjusted, it directly transmits the adjusted operating parameters to the control feature selection terminal. The control feature selection process involves confirming the parameter change process of each operating parameter of a single device based on the monitoring process, identifying the change features from the change process, and then selecting the control feature based on the recorded control code. The specific method is as follows: Using the current time as the calibration time, confirm a set of traceability cycles, which are preset cycles. For a single device, confirm the parameter change curve of the operating parameters within the traceability cycle. The horizontal axis of the curve is the time line, and the vertical axis is the specific value of the operating parameters. From the parameter change curve of a single operating parameter, confirm the change trend of different operating parameters within adjacent time periods. Determine the operating parameter before the adjacent time period as YX1 and the operating parameter after the adjacent time period as YX2. Use the formula: (YX2-YX1) = change trend. With a 1-minute interval between each adjacent time period, simultaneously confirm the parameter change curve associated with the control parameter, and then confirm the change trend of the control parameter between adjacent time periods, and record it as the control trend. According to the set sorting method of operating parameters, sort the change trends associated with each operating parameter in the same period, generate a set of change trend columns, and place the control trends associated with the same period after the change trend columns. Multiple different control trends associated with the same trend column are identified, and control intervals belonging to the corresponding trend column are generated. The generated control intervals are recorded as the control codes of the corresponding trend columns. Different control codes exist for different trend columns.

2. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 1, characterized in that, Also includes: The cloud database stores a large number of operating and control parameters for different equipment within the production line.

3. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 1, characterized in that, If a trend column has only one control trend, then the only control trend is recorded as the control code of the corresponding trend column.

4. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 1, characterized in that, The specific method for selecting control features at the control feature selection end is as follows: Record the current time as the baseline time and the previous time as the comparison time. Confirm the change trend of the operating parameters of a single device belonging to the same item. Based on the operating parameters associated with the baseline time and the comparison time, confirm the change trend associated with the corresponding item. Sort the change trends of several different operating parameters at the current time according to the set operating parameter sorting method to confirm the change trend column. Identify whether the trend column exists within the recorded trend column: If it exists, the control code is selected directly and transmitted to the control parameter output terminal; If it does not exist, the current device will be marked as the device to be confirmed, and the control parameter logic confirmation terminal will be executed directly.

5. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 4, characterized in that, The control parameter output terminal, based on the selected control code, starts from the minimum value of the interval and gradually increases the control parameter. The control trend of each unit time gradually increases until the monitored trend column is consistent with the trend column sorted by the control feature selection terminal. The current control trend is confirmed and executed until the various operating parameters of the device are consistent with the adjusted operating parameters, and the current control parameters remain unchanged.

6. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 5, characterized in that, The control parameter logic confirmation terminal, for the calibrated device to be confirmed, directly confirms the control trend associated with the device to be confirmed through the PLC control system, and directly transmits the confirmed control trend to the control parameter output terminal.

7. The intelligent production line collaborative control system for nonwoven packaging bags according to claim 6, characterized in that, The control parameter output terminal executes the trend change process of the control parameters according to the control trend confirmed by the device to be confirmed, and stops when the various operating parameters of the device are consistent with the adjusted operating parameters, keeping the current control parameters unchanged.

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