Intelligent control method for tail length of material

Abstract

The invention relates to the field of disposable hygienic products, in particular to a material tail length intelligent control method which comprises the following steps: S1, setting the outer diameter of a paper tube, a material thickness coefficient and a splicing delay strategy in an independent control touch screen system; s2, a high-precision sensor is used for measuring the paper tube outer diameter and the material thickness of each roll of material in the production process in real time, and measured data is transmitted to a control system; s3, according to the set material thickness coefficient and the splicing delay strategy, automatically calculating and adjusting the delay duration or the delay piece number during material splicing; s4, the control system generates and executes a control instruction according to a result of the intelligent calculation step, and drives production equipment to carry out material splicing and material tail length adjustment; and S5, recording key data in the production process, performing trend analysis by using a data analysis tool, and optimizing control parameters. The technical problems that the material tail length control accuracy of an existing hygienic product production line is poor, and intelligent control cannot be achieved are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of disposable sanitary products, in particular to a material tail length intelligent control method. BACKGROUND

[0002] In the production process of sanitary products such as sanitary napkins, accurate control of the material tail length is of great significance to improve production efficiency, reduce material waste, and ensure product quality.

[0003] At present, the tail length control of sanitary product production line mainly adopts two traditional ways. One way is to use the tail detection proximity switch combined with program delay setting to control. In actual production, due to the fact that the thickness of the paper tube of the material roll is not fixed and will fluctuate. This thickness change will lead to inaccurate tail length control according to the original setting, which is prone to cause the tail material to be set too long, thereby causing material waste; it may also appear to be set too short, thereby causing the problem of material shortage automatic shutdown, which seriously affects the continuity and efficiency of production. Another way is to use the control method of matching the shaft rotation pulse with the line speed program calculation. However, this method also has obvious defects. In the actual production scene, when the control parameters need to be set or adjusted, due to the fact that the control position and the production operation position may be far apart, it is difficult for the operator to operate conveniently and quickly, which leads to untimely adjustment, affecting the production progress and the accurate control of the tail length. Moreover, these two traditional control methods cannot well adapt to the influence of the change of the thickness of the paper tube, and it is difficult to realize the accurate and stable control of the tail length, resulting in a high rate of waste in the production process and increasing the production cost.

[0004] There is a lack of a comprehensive control method that can effectively integrate parameter setting, real-time measurement, intelligent calculation, execution control, and feedback optimization functions in the prior art. SUMMARY

[0005] Therefore, in view of the above problems, the present application provides a material tail length intelligent control method, which solves the technical problems of poor accuracy of tail length control of existing sanitary product production line and inability to realize intelligent control.

[0006] To achieve the above purpose, the present application adopts the following technical scheme: a material tail length intelligent control method, comprising the following steps:

[0007] S1, parameter setting step: setting the outer diameter of the paper tube, the material thickness coefficient and the splicing delay strategy in the independent control touch screen system, wherein the splicing delay strategy includes delay time or delay piece number;

[0008] S2, real-time measurement step: using high-precision sensors to measure the outer diameter of the paper tube and the thickness of the material in real time during the production process, and transmitting the measurement data to the control system;

[0009] S3, intelligent calculation step: the control system receives real-time data from the high-precision sensor, and according to the set material thickness coefficient and splicing delay strategy, automatically calculates and adjusts the delay time length or delay number of materials during splicing through the intelligent control algorithm module;

[0010] S4, execution control step: the control system generates and executes control instructions according to the results of the intelligent calculation step, drives the production equipment to splice materials and adjust the tail length, and ensures accurate control of the tail length;

[0011] S5, feedback optimization step: record key data in the production process, including tail length set value, actual tail length, production speed, use data analysis tools for trend analysis, and optimize control parameters according to the analysis results.

[0012] Preferably, in the intelligent calculation step, the intelligent control algorithm module automatically calculates and adjusts the delay time length or delay number of materials during splicing according to the set material thickness coefficient and splicing delay strategy, combined with the real-time measured paper tube outer diameter and material thickness, in the following way:

[0013] When the splicing delay strategy is the delay time length, the intelligent control algorithm module first calculates the basic time required for unit length material splicing according to the material thickness coefficient and the real-time measured material thickness, then combines the real-time measured paper tube outer diameter, and modifies the basic time through a pre-set correction coefficient related to the paper tube outer diameter, to finally obtain the delay time length during material splicing;

[0014] When the splicing delay strategy is the delay number, the intelligent control algorithm module determines the corresponding splicing basic number of each piece of material according to the material thickness coefficient and the real-time measured material thickness, and at the same time considers the influence of the real-time measured paper tube outer diameter on the splicing number, adjusts the basic number through a pre-set adjustment formula associated with the paper tube outer diameter, to obtain the delay number during material splicing.

[0015] Preferably, in the intelligent calculation step, the intelligent control algorithm module also introduces production environment temperature and humidity parameters in the process of automatically calculating and adjusting the delay time length or delay number of materials during splicing; the intelligent control algorithm module pre-stores a table of influence coefficients of material splicing delay time length or delay number under different temperature and humidity conditions, according to the real-time obtained production environment temperature and humidity data, finds the corresponding influence coefficient from the influence coefficient table, and combines the set material thickness coefficient, splicing delay strategy, and real-time measured paper tube outer diameter and material thickness, to comprehensively calculate and adjust the delay time length or delay number of materials during splicing.

[0016] Preferably, in the intelligent calculation step, the intelligent control algorithm module has a self-learning function; after each material splicing is completed, the intelligent control algorithm module records the parameters set during this splicing, the real-time measured data, and the relevant data of the actual splicing effect; through the analysis and learning of multiple sets of historical data, the calculation model and parameters inside the intelligent control algorithm module are continuously optimized to improve the accuracy and adaptability of subsequent automatic calculation and adjustment of the delay time or the number of delay pieces during material splicing.

[0017] Preferably, in the execution control step, when the control system generates control instructions based on the results of the intelligent calculation step, a hierarchical control strategy is adopted. For critical control instructions that affect the control of the material tail length, such as core action instructions that drive the production equipment to splice materials, the control system processes them with high priority to ensure that they are sent to the production equipment in a timely and accurate manner. For auxiliary control instructions, such as equipment status monitoring instructions, they are processed with low priority. At the same time, the control system also has an instruction caching and conflict detection mechanism. When multiple control instructions are generated at the same time, they are sent in sequence according to priority to avoid instruction conflicts that may cause abnormal execution of the production equipment.

[0018] Preferably, during the execution control step, when the control system drives the production equipment to perform material splicing and material tail length adjustment, it monitors the operating status of the production equipment in real time. If an abnormality is detected in the production equipment during the execution process, such as motor failure or transmission component jamming, the control system immediately stops sending the current control command and generates corresponding emergency control commands according to the preset emergency handling strategy, such as starting backup equipment, adjusting equipment operating parameters to reduce the impact of the failure, and issuing alarm information to the operator to prompt equipment inspection and maintenance.

[0019] Preferably, in the execution control step, after the control system drives the production equipment to splice materials and adjust the material tail length, it verifies the execution result of the production equipment; by using a high-precision sensor to measure the actual material tail length again and comparing it with the expected material tail length in the intelligent calculation step; if the deviation between the actual material tail length and the expected material tail length is within the preset allowable range, the execution result is determined to be qualified; if the deviation exceeds the allowable range, the control system recalculates the adjusted delay duration or number of delay pieces based on the current real-time data and preset parameters through the intelligent control algorithm module, and generates new control commands to drive the production equipment to adjust until the actual material tail length meets the requirements.

[0020] Preferably, in the feedback optimization step, when using data analysis tools for trend analysis, a combination of multiple analysis methods is adopted; in addition to drawing and analyzing basic trend charts for key data such as the set value of the material tail length, the actual material tail length, and the production speed, regression analysis is also used to explore the intrinsic relationship between various parameters and establish a parameter correlation model; through this model, the changing trend of the material tail length under different production conditions is predicted, providing a more accurate basis for further optimization of control parameters.

[0021] Preferably, in the feedback optimization step, when optimizing the control parameters based on the data analysis results, a stepwise approximation optimization algorithm is adopted; firstly, based on the preliminary conclusions drawn from the data analysis, the control parameters are adjusted slightly, and then the production process is recorded and analyzed again; through multiple iterative adjustments and analyses, the deviation between the actual material tail length and the set value is gradually minimized, while ensuring the stability and efficiency of the production process; after each adjustment, the adjusted parameters, adjustment range, and corresponding production effects are recorded in detail for subsequent traceability and summarization of optimization experience.

[0022] Preferably, in the feedback optimization step, the key data recorded in the production process and the optimized control parameters are stored in a database; the database has data classification storage and fast retrieval functions, which facilitates subsequent comparative analysis of data under different production batches and different material types; at the same time, the database also supports data export function, which can export data to external data analysis software for more in-depth analysis and mining, so as to continuously improve and perfect the intelligent control method for material tail length.

[0023] By adopting the aforementioned technical solution, the beneficial effects of the present invention are:

[0024] 1. By independently controlling the touchscreen system to set key parameters, combined with real-time measurement by high-precision sensors, calculation and adjustment by intelligent control algorithm modules, execution control by the control system, and feedback optimization, a complete intelligent control system for material tail length is formed. This system can effectively overcome the problem of inaccurate tail length setting caused by changes in paper tube thickness in existing technologies, reduce the probability of downtime failure during the splicing process, improve equipment uptime, and directly reduce the scrap rate. At the same time, the convenient operation of the touchscreen improves the ease of operation for production personnel, simplifies parameter modification / setting, and reduces material waste caused by excessive tail length setting.

[0025] 2. The intelligent control algorithm module performs precise calculations based on different splicing delay strategies, combined with real-time measurements of the paper tube's outer diameter and material thickness. When using a delay duration strategy, the base time is adjusted by considering a correction factor for the paper tube's outer diameter; when using a delay number of pieces strategy, the base number of pieces is adjusted according to an adjustment formula related to the paper tube's outer diameter. This method can more accurately adapt to material splicing requirements under different paper tube outer diameters and material thicknesses, further improving the accuracy of material tail length control and reducing material waste and production problems caused by inaccurate material tail length.

[0026] 3. By incorporating environmental temperature and humidity parameters, the intelligent control algorithm module calculates and adjusts the delay duration or number of delay pieces based on a preset influence coefficient table and other parameters. Since changes in the production environment can affect material properties and thus the splicing effect, this method considers environmental factors, making calculations and adjustments more comprehensive and accurate. It can achieve better control of material tail length under different environmental conditions, enhancing the adaptability and stability of the control method.

[0027] 4. The intelligent control algorithm module has a self-learning function, recording relevant data for each splicing operation. By analyzing and learning from multiple sets of historical data, it continuously optimizes the internal calculation model and parameters. As production data accumulates, the algorithm can automatically adapt to different materials and production conditions, improving the accuracy and adaptability of subsequent automatic calculations and adjustments. This makes material tail length control more intelligent and precise, reducing the frequency of manual intervention and debugging.

[0028] 5. The control system adopts a hierarchical control strategy, processing critical control commands with high priority and auxiliary commands with low priority, and has command caching and conflict detection mechanisms. This ensures that critical commands drive the production equipment in a timely and accurate manner, avoids equipment malfunctions due to command conflicts, improves the stability and reliability of the production process, and guarantees that the material tail length control can proceed smoothly as expected.

[0029] 6. The control system monitors the operating status of the production equipment in real time. If an abnormality is detected, it immediately stops the current command, generates an emergency control command, and issues an alarm. This mechanism can promptly detect equipment failures, prevent the failure from escalating and causing greater impact on production, and ensure production safety through emergency handling, reducing errors in material tail length control and production interruptions caused by equipment malfunctions.

[0030] 7. After adjusting the drive equipment, the control system verifies the execution results by re-measuring the actual material tail length and comparing it with the expected length. If the deviation exceeds the allowable range, the system recalculates and adjusts, generating new instructions to drive the equipment to adjust until the requirements are met. This method ensures that the material tail length ultimately reaches the expected length, improving product quality consistency and preventing product performance from being affected by non-compliant material tail lengths.

[0031] 8. The feedback optimization process employs multiple analytical methods. In addition to basic trend chart analysis, regression analysis is used to establish a parameter correlation model to predict the trend of material tail length changes under different production conditions. This approach allows for a deeper understanding of the relationships between various parameters, providing a more accurate and comprehensive basis for optimizing control parameters. This enables material tail length control to better adapt to different production conditions, improving the scientific nature and effectiveness of the control.

[0032] 9. Employing a successive approximation optimization algorithm, parameters are adjusted incrementally based on data analysis results. Multiple iterations of adjustment and analysis minimize the deviation between the actual material tail length and the set value, while ensuring stable and efficient production. Each adjustment is meticulously recorded, detailing the parameters, magnitude, and effects for easy traceability and experience summarization. This method systematically and scientifically optimizes control parameters, avoiding the risks of blind adjustments and gradually improving the quality of material tail length control.

[0033] 10. Key data and optimized control parameters are stored in a database with categorized storage and rapid retrieval capabilities, facilitating comparative analysis of data from different batches and material types. Data can also be exported to external software for in-depth analysis and mining. This approach fully utilizes production data, providing rich data support for further improvement and refinement of control methods, and promoting continuous optimization and upgrading of control methods. Attached Figure Description

[0034] Figure 1 This is a flowchart of the control steps of the present invention. Detailed Implementation

[0035] The present invention will now be further described in conjunction with the accompanying drawings and specific embodiments.

[0036] refer to Figure 1 This embodiment provides a method for intelligent control of material tail length, including the following steps:

[0037] S1. Parameter setting steps: Set the outer diameter of the paper tube, the material thickness coefficient, and the splicing delay strategy in the independent control touch screen system. The splicing delay strategy includes the delay duration or the number of delay pieces.

[0038] S2. Real-time measurement steps: Use high-precision sensors to measure the outer diameter of the paper tube and the thickness of the material in each roll of material during the production process in real time, and transmit the measurement data to the control system.

[0039] S3. Intelligent Calculation Steps: The control system receives real-time data from high-precision sensors and automatically calculates and adjusts the delay time or number of delay pieces during material splicing based on the set material thickness coefficient and splicing delay strategy through the intelligent control algorithm module.

[0040] S4. Execution control steps: Based on the results of the intelligent calculation steps, the control system generates and executes control commands to drive the production equipment to splice materials and adjust the length of the material tail, ensuring precise control of the material tail length.

[0041] S5. Feedback and optimization steps: Record key data in the production process, including the set value of the material tail length, the actual material tail length, and the production speed. Use data analysis tools to conduct trend analysis and optimize control parameters based on the analysis results.

[0042] The independent control touchscreen system can utilize commercially available industrial-grade touchscreens, such as Siemens' SIMATICHMI series, which offer high-precision touch sensing and stable data processing capabilities. The outer diameter of the paper tube can be set according to the actual specifications of the paper tube being used. The material thickness coefficient can be set based on the characteristics of different materials; for example, the thickness coefficient of the surface material of sanitary napkins may be between 0.8 and 1.2. The selection of the splicing delay strategy can be determined based on production needs and experience. If the production speed is high, a delay duration strategy can be selected; if there are strict requirements on the number of spliced ​​pieces, a delay piece count strategy can be selected.

[0043] High-precision sensors can be selected from laser displacement sensors, such as Keyence's LK-G5000 series laser displacement sensors, which feature high precision, high speed, and non-contact measurement, enabling accurate measurement of paper tube outer diameter and material thickness. The sensor can be installed in a suitable location on the production equipment, such as near the material unwinding area, ensuring real-time acquisition of measurement data. Data transmission can be achieved via wired communication, such as RS485 bus, to stably and quickly transmit the sensor-measured data to the control system; wireless communication can also be used.

[0044] The intelligent control algorithm module can be implemented using an embedded system-based algorithm, such as an ARM processor paired with a corresponding algorithm program. When the splicing delay strategy is a delay duration, the basic time required for splicing a unit length of material is first calculated based on the material thickness coefficient and the real-time measured material thickness. Assuming the material thickness coefficient is k and the real-time measured material thickness is d, the basic time T0 = k * d. Then, combined with the real-time measured outer diameter of the paper tube, the basic time is corrected using a preset correction coefficient related to the outer diameter of the paper tube. If the outer diameter of the paper tube is D and the correction coefficient is f(D), then the final delay duration for material splicing is T = T0 * f(D). When the splicing delay strategy is the number of delay pieces, the number of basic splicing pieces corresponding to each piece of material is determined based on the material thickness coefficient and the material thickness measured in real time. Assuming the number of basic pieces is N0 = k / d, the influence of the paper tube outer diameter measured in real time on the number of splicing pieces is also considered. The number of basic pieces is adjusted by a preset adjustment formula related to the paper tube outer diameter. If the adjustment formula is N = N0 + g(D), then the number of delay pieces N when splicing materials is obtained.

[0045] In the intelligent calculation step, the intelligent control algorithm module, based on the set material thickness coefficient and splicing delay strategy, and combined with the real-time measured outer diameter of the paper tube and material thickness, automatically calculates and adjusts the delay duration or number of delay pieces during material splicing in the following manner:

[0046] When the splicing delay strategy is a delay duration, the intelligent control algorithm module first calculates the basic time required for splicing a unit length of material based on the material thickness coefficient and the material thickness measured in real time. Then, it combines the paper tube outer diameter measured in real time and corrects the basic time through a preset correction coefficient related to the paper tube outer diameter to finally obtain the delay duration for material splicing.

[0047] When the splicing delay strategy is the number of delay pieces, the intelligent control algorithm module determines the number of basic splicing pieces corresponding to each piece of material based on the material thickness coefficient and the material thickness measured in real time. At the same time, it considers the influence of the paper tube outer diameter measured in real time on the number of splicing pieces, and adjusts the number of basic pieces through a preset adjustment formula related to the paper tube outer diameter, thereby obtaining the number of delay pieces when splicing materials.

[0048] In the intelligent calculation step, the intelligent control algorithm module also incorporates production environment temperature and humidity parameters during the automatic calculation and adjustment of the delay duration or number of delay pieces during material splicing. The intelligent control algorithm module pre-stores an influence coefficient table on the material splicing delay duration or number of delay pieces under different temperature and humidity conditions. Based on the real-time acquired production environment temperature and humidity data, it looks up the corresponding influence coefficient from the influence coefficient table and, in combination with the set material thickness coefficient, splicing delay strategy, and real-time measured paper tube outer diameter and material thickness, comprehensively calculates and adjusts the delay duration or number of delay pieces during material splicing.

[0049] The temperature and humidity parameters of the production environment can be obtained through temperature and humidity sensors, such as Honeywell's HMC5883L temperature and humidity sensor. The influence coefficient table can be formulated based on experimental data and experience. For example, the influence coefficients on delay duration or the number of delay elements can be determined for different temperature ranges (e.g., 20-25℃, 25-30℃, etc.) and humidity ranges (e.g., 40%-60%, 60%-80%, etc.). In the calculation process, assuming the temperature influence coefficient is Ct and the humidity influence coefficient is Ch, the comprehensive calculation is as follows: for delay duration T = T0 * f(D) * Ct * Ch; for the number of delay elements N = N0 + g(D) * Ct * Ch.

[0050] In the intelligent computing step, the intelligent control algorithm module has a self-learning function. After each material splicing is completed, the intelligent control algorithm module records the parameters set during this splicing, the real-time measured data, and the relevant data of the actual splicing effect. Through the analysis and learning of multiple sets of historical data, the intelligent control algorithm module continuously optimizes the internal calculation model and parameters to improve the accuracy and adaptability of subsequent automatic calculation and adjustment of the delay time or the number of delay pieces during material splicing.

[0051] The intelligent control algorithm module can employ algorithms with machine learning capabilities, such as neural network algorithms. Recorded data can be stored in the control system's memory, such as Flash memory. Through analysis and learning from multiple sets of historical data, the neural network algorithm can automatically adjust its internal weights and parameters, optimizing the computational model. For example, after multiple stitching operations, the algorithm can adjust parameters such as material thickness coefficients and correction coefficients based on the difference between the actual stitching effect and the expected effect, making subsequent calculations more accurate.

[0052] In the execution control step, when the control system generates control commands based on the results of the intelligent calculation step, it adopts a hierarchical control strategy. For critical control commands that affect the control of the material tail length, such as core action commands that drive the production equipment to splice materials, the control system processes them with high priority to ensure that they are sent to the production equipment in a timely and accurate manner. For auxiliary control commands, such as equipment status monitoring commands, they are processed with low priority. At the same time, the control system also has a command caching and conflict detection mechanism. When multiple control commands are generated at the same time, they are sent in sequence according to priority to avoid command conflicts that may cause abnormal execution of the production equipment.

[0053] The control system can employ a programmable logic controller (PLC), such as Mitsubishi's FX series PLC, which boasts powerful logic control capabilities and stability. The generated core action instructions for driving the production equipment to perform material splicing can be commands to control the motor's start, stop, and speed adjustment; for example, controlling the operation of a servo motor via pulse signals output by the PLC. Auxiliary equipment status monitoring instructions can monitor parameters such as equipment temperature and pressure.

[0054] During the execution control steps, when the control system drives the production equipment to splice materials and adjust the length of the material tail, it monitors the operating status of the production equipment in real time. If an abnormality is detected in the production equipment during the execution process, such as motor failure or jamming of transmission components, the control system immediately stops sending the current control command and generates corresponding emergency control commands according to the preset emergency handling strategy. For example, it starts the backup equipment, adjusts the equipment operating parameters to reduce the impact of the failure, and sends an alarm message to the operator to prompt the equipment to be inspected and maintained.

[0055] Real-time monitoring of production equipment operation can be achieved by installing sensors on key components of the equipment. For example, current and vibration sensors can be installed on motors to detect their operating status; position sensors can be installed on transmission components to detect any transmission jamming. Pre-set emergency response strategies can be stored in the control system's program. When a motor failure is detected, a backup motor is activated; when a transmission component jamming is detected, the motor's speed or torque is adjusted. Alarm information can be provided via audible and visual alarms or by connecting to the operator's mobile terminal.

[0056] In the execution control step, after the control system drives the production equipment to splice materials and adjust the tail length, it verifies the execution result of the production equipment. The actual tail length is measured again using a high-precision sensor and compared with the expected tail length in the intelligent calculation step. If the deviation between the actual tail length and the expected tail length is within the preset allowable range, the execution result is deemed qualified. If the deviation exceeds the allowable range, the control system recalculates the adjusted delay duration or number of delay segments based on the current real-time data and preset parameters through the intelligent control algorithm module, and generates new control commands to drive the production equipment to adjust until the actual tail length meets the requirements.

[0057] The high-precision sensor reused is the same as the one used in step S2, ensuring the consistency and accuracy of the measurement. The preset allowable range can be set according to production requirements and experience, for example, an allowable deviation of ±5mm. When the deviation exceeds the allowable range, the control system reacquires the current real-time data, such as the outer diameter of the paper tube and the material thickness, and, in combination with the preset parameters, recalculates through the intelligent control algorithm module to obtain the adjusted delay duration or number of delay pieces, and generates new control commands to drive the production equipment to make adjustments.

[0058] In the feedback optimization step, when using data analysis tools for trend analysis, a combination of multiple analysis methods is adopted. In addition to drawing and analyzing basic trend charts for key data such as the set value of the material tail length, the actual material tail length, and the production speed, regression analysis is also used to explore the intrinsic relationship between various parameters and establish a parameter correlation model. This model is used to predict the changing trend of the material tail length under different production conditions, providing a more accurate basis for further optimizing control parameters.

[0059] In the feedback optimization step, when optimizing control parameters based on data analysis results, a stepwise approximation optimization algorithm is adopted. First, based on the preliminary conclusions drawn from the data analysis, the control parameters are adjusted slightly, and then the production process is recorded and analyzed again. Through multiple iterative adjustments and analyses, the deviation between the actual material tail length and the set value is gradually minimized, while ensuring the stability and efficiency of the production process. After each adjustment, the adjusted parameters, adjustment range, and corresponding production effects are recorded in detail for subsequent traceability and summarization of optimization experience.

[0060] In the feedback optimization step, key data recorded during the production process and optimized control parameters are stored in a database. The database has data classification storage and fast retrieval functions, which facilitates subsequent comparative analysis of data under different production batches and different material types. At the same time, the database also supports data export function, which can export data to external data analysis software for more in-depth analysis and mining, so as to continuously improve and perfect the intelligent control method for material tail length.

[0061] Key recorded data can be stored in the control system's database, which can be a relational database such as SQL Server. Data analysis tools can include Excel or specialized data analysis software such as SPSS. By creating and analyzing basic trend charts for data such as the setpoint for the material tail length, the actual material tail length, and the production speed, the changing trends of the data can be intuitively understood. For example, plotting the trend charts of the setpoint for the material tail length and the actual material tail length over time can help analyze the differences between the two.

[0062] Regression analysis can be performed using the regression analysis function in data analysis software, such as the Data Analysis Toolkit in Excel or the Regression Analysis module in SPSS. The established parameter correlation model can be a linear regression model or a nonlinear regression model. For example, assuming the relationship between the material tail length L, the set material tail length L0, the actual material tail length L1, and the production speed V is L = a*L0 + b*L1 + c*V + d, the values ​​of coefficients a, b, c, and d can be determined through regression analysis.

[0063] The successive approximation optimization algorithm can be implemented programmatically, for example, by writing an optimization program using Python. Preliminary conclusions can be determined based on data analysis results. For example, if the actual value of the material tail length is found to be too large, it can be preliminarily determined that the splicing delay time or the number of delay pieces needs to be reduced. The adjustment range for each step can be set based on experience, for example, adjusting the splicing delay time by 0.1 seconds each time. Detailed records of the adjusted parameters, adjustment ranges, and corresponding production effects can be stored in a database for easy subsequent querying and analysis.

[0064] The database can be an open-source database such as MySQL, which features data categorization, storage, and fast retrieval. For example, data can be categorized and stored according to fields such as production batch and material type. Data export can be achieved through database export tools, such as MySQL's mysqldump tool, to export data to CSV or Excel format for import into external data analysis software, such as Tableau or Python's Pandas library, for more in-depth analysis and mining.

[0065] Although the invention has been specifically shown and described in conjunction with preferred embodiments, those skilled in the art should understand that various changes in form and detail may be made to the invention without departing from the spirit and scope of the invention as defined in the appended claims, all of which shall be within the scope of protection of the invention.

Claims

1. A method for intelligent control of material tail length, characterized in that, Includes the following steps: S1. Parameter setting steps: Set the outer diameter of the paper tube, the material thickness coefficient, and the splicing delay strategy in the independent control touch screen system. The splicing delay strategy includes the delay duration or the number of delay pieces. S2. Real-time measurement steps: Use high-precision sensors to measure the outer diameter of the paper tube and the thickness of the material in each roll of material during the production process in real time, and transmit the measurement data to the control system. S3. Intelligent Calculation Steps: The control system receives real-time data from high-precision sensors and automatically calculates and adjusts the delay time or number of delay pieces during material splicing based on the set material thickness coefficient and splicing delay strategy through the intelligent control algorithm module. S4. Execution control steps: Based on the results of the intelligent calculation steps, the control system generates and executes control commands to drive the production equipment to splice materials and adjust the length of the material tail, ensuring precise control of the material tail length. S5. Feedback and optimization steps: Record key data in the production process, including the set value of the material tail length, the actual material tail length, and the production speed. Use data analysis tools to conduct trend analysis and optimize control parameters based on the analysis results.

2. The intelligent control method for material tail length according to claim 1, characterized in that, In the intelligent calculation step, the intelligent control algorithm module, based on the set material thickness coefficient and splicing delay strategy, and combined with the real-time measured outer diameter of the paper tube and material thickness, automatically calculates and adjusts the delay duration or number of delay pieces during material splicing in the following manner: When the splicing delay strategy is a delay duration, the intelligent control algorithm module first calculates the basic time required for splicing a unit length of material based on the material thickness coefficient and the material thickness measured in real time. Then, it combines the paper tube outer diameter measured in real time and corrects the basic time through a preset correction coefficient related to the paper tube outer diameter to finally obtain the delay duration for material splicing. When the splicing delay strategy is the number of delay pieces, the intelligent control algorithm module determines the number of basic splicing pieces corresponding to each piece of material based on the material thickness coefficient and the material thickness measured in real time. At the same time, it considers the influence of the paper tube outer diameter measured in real time on the number of splicing pieces, and adjusts the number of basic pieces through a preset adjustment formula related to the paper tube outer diameter, thereby obtaining the number of delay pieces when splicing materials.

3. The intelligent control method for material tail length according to claim 1, characterized in that, In the intelligent calculation step, the intelligent control algorithm module also incorporates production environment temperature and humidity parameters during the automatic calculation and adjustment of the delay duration or number of delay pieces during material splicing. The intelligent control algorithm module pre-stores an influence coefficient table on the material splicing delay duration or number of delay pieces under different temperature and humidity conditions. Based on the real-time acquired production environment temperature and humidity data, it looks up the corresponding influence coefficient from the influence coefficient table and, in combination with the set material thickness coefficient, splicing delay strategy, and real-time measured paper tube outer diameter and material thickness, comprehensively calculates and adjusts the delay duration or number of delay pieces during material splicing.

4. The intelligent control method for material tail length according to claim 1, characterized in that, In the intelligent computing step, the intelligent control algorithm module has a self-learning function. After each material splicing is completed, the intelligent control algorithm module records the parameters set during this splicing, the real-time measured data, and the relevant data of the actual splicing effect. Through the analysis and learning of multiple sets of historical data, the intelligent control algorithm module continuously optimizes the internal calculation model and parameters to improve the accuracy and adaptability of subsequent automatic calculation and adjustment of the delay time or the number of delay pieces during material splicing.

5. The intelligent control method for material tail length according to claim 1, characterized in that, In the execution control step, when the control system generates control instructions based on the results of the intelligent calculation step, it adopts a hierarchical control strategy. For control instructions that are critical to the control of the material tail length, the control system processes them with high priority; for auxiliary control instructions, it processes them with low priority. At the same time, the control system also has an instruction caching and conflict detection mechanism. When multiple control instructions are generated at the same time, they are sent in sequence according to priority to avoid instruction conflicts that may cause abnormal execution of the production equipment.

6. The intelligent control method for material tail length according to claim 1, characterized in that, During the execution control steps, when the control system drives the production equipment to splice materials and adjust the length of the material tail, it monitors the operating status of the production equipment in real time. If an abnormality is detected in the production equipment during the execution process, the control system immediately stops sending the current control command and generates a corresponding emergency control command according to the preset emergency handling strategy. At the same time, it sends an alarm message to the operator to prompt the equipment to be inspected and maintained.

7. The intelligent control method for material tail length according to claim 1, characterized in that, In the execution control step, after the control system drives the production equipment to splice materials and adjust the tail length, it verifies the execution result of the production equipment. The actual tail length is measured again using a high-precision sensor and compared with the expected tail length in the intelligent calculation step. If the deviation between the actual tail length and the expected tail length is within the preset allowable range, the execution result is deemed qualified. If the deviation exceeds the allowable range, the control system recalculates the adjusted delay duration or number of delay segments based on the current real-time data and preset parameters through the intelligent control algorithm module, and generates new control commands to drive the production equipment to adjust until the actual tail length meets the requirements.

8. The intelligent control method for material tail length according to claim 1, characterized in that, In the feedback optimization step, when using data analysis tools for trend analysis, a combination of multiple analysis methods is adopted. In addition to drawing and analyzing basic trend charts for key data such as the set value of the material tail length, the actual material tail length, and the production speed, regression analysis is also used to explore the intrinsic relationship between various parameters and establish a parameter correlation model. This model is used to predict the changing trend of the material tail length under different production conditions, providing a more accurate basis for further optimizing control parameters.

9. The intelligent control method for material tail length according to claim 1, characterized in that, In the feedback optimization step, when optimizing control parameters based on data analysis results, a stepwise approximation optimization algorithm is adopted. First, based on the preliminary conclusions drawn from the data analysis, the control parameters are adjusted slightly, and then the production process is recorded and analyzed again. Through multiple iterative adjustments and analyses, the deviation between the actual material tail length and the set value is gradually minimized, while ensuring the stability and efficiency of the production process. After each adjustment, the adjusted parameters, adjustment range, and corresponding production effects are recorded in detail for subsequent traceability and summarization of optimization experience.

10. The intelligent control method for material tail length according to claim 1, characterized in that, In the feedback optimization step, key data recorded during the production process and optimized control parameters are stored in a database. The database has data classification storage and fast retrieval functions, which facilitates subsequent comparative analysis of data under different production batches and different material types. At the same time, the database also supports data export function, which can export data to external data analysis software for more in-depth analysis and mining, so as to continuously improve and perfect the intelligent control method for material tail length.

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