Soft extraction tissue production line balance system based on digital twinning

Abstract

The invention discloses a digital twinning-based soft tissue production line balance system, which comprises a data acquisition and integration subsystem, a data processing subsystem, a prediction subsystem, an early warning and recommendation subsystem and a tissue production line, the data acquisition and integration subsystem comprises a sensor, a data communication network and a database; and the prediction subsystem is used for judging whether the current state of the folding machine is normal and stable in speed or not, and if so, predicting the change condition of the paper storage rate. According to the soft extraction tissue production line balance system based on digital twinning, firstly, a simulation model which meets actual prediction requirements and is low in complexity is constructed around a prediction target for a soft extraction production line with a plurality of complex devices; 2, according to the prediction result, the opportunity and the speed value of the folding machine speed adjustment are recommended at the same time, so that field workers can control the equipment conveniently.

Description

Technical Field

[0001] This invention relates to the field of soft tissue paper production technology, specifically a balancing system for a soft tissue paper production line based on digital twins. Background Technology

[0002] With increasing environmental awareness and the demand for sustainable development, the paper industry is paying more and more attention to green production and cost reduction and efficiency improvement, which also puts forward higher and higher requirements for the precise control of soft tissue paper production lines. At the same time, with the deep integration and application of new-generation information technologies such as the Internet of Things, big data, artificial intelligence, and digital twins with manufacturing technologies, the manufacturing industry is developing towards digitalization, networking, and intelligence in order to achieve deep integration of people, production systems, and information systems.

[0003] Existing production line balancing optimization methods for soft tissue paper production mainly rely on real-time collected production status data and industry experience formulas to predict the time it takes for the outer circulation of the paper rack to reach full capacity at regular intervals, calculate the recommended speed of the folding machine, and provide early warnings and suggestions. The production status data used mainly includes paper storage rate, folding machine operating speed, and the effective total number of cuts by the cutting machine. The industry experience formulas are generally as follows: Time it takes for the outer circulation of the paper rack to reach full capacity = Remaining capacity of the outer circulation of the paper rack / (Folding machine operating speed - Increase in the effective total number of cuts by the cutting machine within t time prior to the prediction time / t); Recommended speed of the folding machine = Increase in the effective total number of cuts by the cutting machine within a certain period prior to the calculation time / time interval.

[0004] The existing production line balancing optimization methods in the production of soft tissue paper have the following main drawbacks: First, the material running speed in the later stage of the paper rack external circulation has strong fluctuations. The increase in the effective total number of cuts by the cutting machine over a period of time cannot accurately reflect the running characteristics of the material. Using only this value to represent the output speed of the paper rack external circulation results in low accuracy of the predicted results. Second, no suggestions are given on when to adjust the speed of the folding machine, which cannot clearly guide the on-site workers to operate the equipment.

[0005] To overcome the above-mentioned shortcomings, the purpose of this invention is to provide a new method for production line balancing optimization in the production process of soft tissue paper based on digital twins, providing on-site workers with a stable and reliable early warning and equipment parameter adjustment suggestions. Summary of the Invention

[0006] The purpose of this invention is to provide a digital twin-based balancing system for a soft tissue paper production line, in order to solve the problems mentioned in the background art, namely: firstly, the material running speed in the later stage of the external circulation of the paper rack has strong fluctuations; secondly, the increase in the effective total number of cuts by the cutting machine over a period of time cannot accurately reflect the running characteristics of the material; and thirdly, the accuracy of the predicted results is not high when using this value to represent the output speed of the external circulation of the paper rack.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a digital twin-based balancing system for a soft tissue paper production line, comprising: a data acquisition and integration subsystem, a data processing subsystem, a prediction subsystem, an early warning and recommendation subsystem, and a tissue paper production line;

[0008] The data acquisition and integration subsystem includes sensors, a data communication network, and a database. It collects data from different points on the production line at the same time frequency through various sensors, transmits the data through the data communication network, and stores it in the database.

[0009] The data processing subsystem retrieves relevant data from the database at certain time intervals according to the prediction needs, processes the data, and then pushes the data to the prediction subsystem and the early warning and recommendation subsystem.

[0010] The prediction subsystem determines whether the current state of the folding machine is operating normally and at a stable speed. If it is, it predicts the change in the paper retention rate.

[0011] The warning and recommendation subsystem determines whether there is a risk of the machine being forced to stop due to full capacity. If so, it performs a full capacity risk warning and calculates and recommends adjustments to the folding machine speed.

[0012] If there is no risk of the machine being forced to stop due to full capacity, then determine whether the speed of the folding machine needs to be increased. If so, issue a warning that the folding machine speed is too slow and make a calculation and recommendation for adjusting the folding machine speed.

[0013] The tissue paper production line includes a folding section of a folding machine, a conveying section of a folding machine, an external circulation system for the paper storage rack, an internal circulation system for the paper storage rack, a cutting channel, a cutting machine, a small packaging channel, a small package machine, a package storage rack channel, a package storage rack, a case packing machine channel, and a case packing machine.

[0014] Preferably, it includes the following steps:

[0015] S1, Data Acquisition and Integration: Through the data acquisition and integration subsystem, data from different points on the production line can be acquired and stored at the same time frequency.

[0016] S2, Data Processing and Push: Based on the set processing frequency, when the processing time arrives, the data acquisition and integration subsystem obtains the actual speed of the folding machine, paper (packing) rate, paper rack external circulation to internal circulation count, effective cuts of each cutting channel, actual speed of each small package (packing) machine, and current value or values ​​of film length of each small package machine from the current time to the previous time period. The corresponding sum, average value, minimum value, maximum value, median, etc. are calculated in the data processing subsystem, and these values ​​are then pushed to the prediction subsystem and the early warning and recommendation subsystem.

[0017] S3, the folding machine's operating status and whether the paper storage rate is greater than the threshold are judged. When the folding machine is running normally and at a steady speed, the speed is greater than a certain value and fluctuates within a small range. Therefore, if the minimum value of the actual speed of the folding machine from the current time to the previous time is not less than the set value, and the difference between the maximum value and the minimum value is not greater than the set fluctuation value, then the folding machine is judged to be running normally and at a steady speed.

[0018] The paper storage rate threshold is generally set to a value above 0, taking into account the characteristics of the production line. When the paper storage rate is greater than the threshold, it ensures that the material storage link from the inlet to the outlet of the paper storage rack is full, and that the subsequent simulation calculations are meaningful.

[0019] S4, Prediction of paper retention rate changes, mainly adopts discrete event simulation method, soft-drawing production line simulation model;

[0020] In the simulation model, the current remaining length lx of the original paper roll x changes over time.

[0021] in This represents the remaining length of the original paper roll X at the start of the simulation, and v is the operating speed of the folding machine (unit: meters per minute). This represents the simulation runtime.

[0022] The folding section of the folding machine is modeled as a generator of long strips of paper. Let t be the time required to generate one long strip of paper.

[0023]

[0024] Where C is the number of tissues in a single pack of soft tissues, L is the length of each tissue (in meters), and v is the operating speed of the folding machine (in meters per minute).

[0025] S5. Based on simulation predictions of a risk of the paper rack's external circulation reaching full capacity, the following different handling methods are adopted according to the predicted time of full capacity:

[0026] ①When t′<=a, an alarm is triggered, “Immediately adjust the speed of the folding machine to the lower limit speed v3”;

[0027] ②When a<t′<=b, an alarm is triggered, “Immediately adjust the speed of the folding machine to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper storage rack”;

[0028] ③ When t′>b, an alarm is triggered, “At (T0+t′-b), the speed of the folding machine is adjusted to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper storage rack”;

[0029] Where T0 represents the predicted start time, t′ represents the predicted time difference of the full rack relative to the initial time, the values ​​of a and b are obtained based on the actual operation of the production line, and v4 is the average speed of the paper rack circulating out of the long strip of paper during the simulation period, converted into the speed of consuming raw paper (unit: meters / minute).

[0030] S6, Determining whether the folding machine needs to be sped up: If at a certain moment the speed of the folding machine is less than the threshold, and the current paper storage rate is lower than the paper storage rate before the set time, then the speed needs to be sped up.

[0031] S7, based on the simulation prediction, suggests that when the folding machine needs to be accelerated, the speed of the folding machine should be adjusted to be balanced with the speed of the later stage of the outer circulation of the paper storage rack at T1. T1 is the moment when the folding machine meets the acceleration conditions in the simulation prediction.

[0032] Preferably, the paper storage rack has multiple cutting channels in its internal circulation, and each cutting channel is equipped with its own cutting machine, small packaging channel, small package machine and storage rack channel.

[0033] Preferably, in step S3, when the operating status of the folding machine matches the paper storage rate, the process proceeds to S4, that is, the change in the paper storage rate is predicted; otherwise, it is not necessary to proceed to S4.

[0034] Preferably, in step S3, the conveyor section of the folding machine is modeled as a conveyor belt. The length of the conveyor belt is obtained by measurement, and the average speed of the long paper stack is obtained by IOT data analysis. The distance interval between each long paper stack is set to be not less than 0. If the outer circulation of the paper rack is not full, the long paper stack will enter the outer circulation when it reaches the end of the conveyor belt.

[0035] Preferably, in step S3, the outer circulation segment of the paper storage rack is modeled as a buffer, and the average value of the change in paper storage rate caused by the change of a single long strip of paper is obtained based on historical IOT data;

[0036] The inner circulation segment of the paper rack is modeled as a buffer. The timing for the long strip to enter each cutting channel from the inner circulation is when the cutting machine is running normally and the cutting channel allows the addition of a long strip. The priority of entering each channel is consistent with the actual situation.

[0037] Preferably, in step S3, the cutting channel is modeled as a buffer, and the maximum capacity of each channel is obtained through historical IoT data analysis and actual production line observation.

[0038] The cutting machine is modeled as a small paper stack generator. As long as there is a long paper stack in a certain cutting channel and the corresponding small package entry channel is allowed to run normally, a small paper stack will be generated according to the cutting machine's running speed (unit: package / minute).

[0039] Preferably, in step S3, each small package receiving channel is modeled as a conveyor belt;

[0040] Each small paper machine is modeled as a processor. When the actual speed of the small paper machine is m packages / minute, the time to process a small stack of paper is 60 / m seconds.

[0041] Preferably, in step S3, each storage rack channel is modeled as a conveyor belt. In the model, each small package is either stationary or running at the highest speed on the channel. The minimum distance between adjacent small packages is 0. The length of a single small package along the channel and the length of each channel are consistent with the actual length.

[0042] The storage rack is modeled as a buffer, and the average value of the change in paper storage rate caused by the addition or removal of a single storage material cell is obtained based on historical IoT data.

[0043] Preferably, in step S3, on the actual physical production line, the channel corresponding to a case packing machine often consists of multiple channels connecting to the storage rack before converging or splitting. In the model, the channel to the case packing machine is designed as follows: parallel channels are connected in series, so that there is only one channel between the storage rack and a case packing machine. The length l2 of a single small package along the channel is consistent with the actual length. The minimum distance between adjacent small packages is 0. The running speed v2 of the small package (unit: meters / minute) is calculated based on the set speed k (unit: packages / minute) of the case packing machine.

[0044] v2 = l2 * k * p.

[0045] The case packer is modeled as a recycling unit; if the case packer malfunctions, small packages will accumulate in the channel.

[0046] Compared with the prior art, the beneficial effects of the present invention are: the digital twin-based soft tissue paper production line balancing system has the following advantages: for soft tissue paper production lines with multiple complex devices, a simulation model that meets the actual prediction requirements and has low complexity is constructed around the prediction target.

[0047] Secondly, based on the forecast results, the timing and speed values ​​for adjusting the folding machine speed are also recommended to facilitate on-site workers' operation of the equipment. Attached Figure Description

[0048] Figure 1 This is a schematic diagram of the early warning system for a soft tissue paper production line based on digital twins according to the present invention;

[0049] Figure 2 This is a schematic diagram of a simulation model of the tissue paper production line of the present invention. Detailed Implementation

[0050] Please see Figure 1-2 The present invention provides a technical solution: a balancing system for a soft tissue paper production line based on digital twins, comprising: a data acquisition and integration subsystem, a data processing subsystem, a prediction subsystem, an early warning and recommendation subsystem, and a tissue paper production line;

[0051] The data acquisition and integration subsystem includes sensors, a data communication network, and a database. It collects data from different points on the production line at the same time frequency through various sensors, transmits the data through the data communication network, and stores it in the database.

[0052] The data processing subsystem retrieves relevant data from the database at certain time intervals according to the prediction needs, processes the data, and then pushes the data to the prediction subsystem and the early warning and recommendation subsystem.

[0053] The prediction subsystem determines whether the current state of the folding machine is operating normally and at a stable speed. If it is, it predicts the change in the paper retention rate.

[0054] The warning and recommendation subsystem determines whether there is a risk of the machine being forced to stop due to full capacity. If so, it performs a full capacity risk warning and calculates and recommends adjustments to the folding machine speed.

[0055] If there is no risk of the machine being forced to stop due to full capacity, then determine whether the speed of the folding machine needs to be increased. If so, issue a warning that the folding machine speed is too slow and make a calculation and recommendation for adjusting the folding machine speed.

[0056] The tissue paper production line includes a folding section of a folding machine, a conveying section of a folding machine, an external circulation system for the paper storage rack, an internal circulation system for the paper storage rack, a cutting channel, a cutting machine, a small packaging channel, a small package machine, a package storage rack channel, a package storage rack, a case packing machine channel, and a case packing machine.

[0057] The raw paper roll is folded by a folding machine, then conveyed to the outer circulation of the paper storage rack via the conveyor section of the folding machine, and then enters the inner circulation of the paper storage rack. It then enters the cutting channel and is cut by the cutting machine. The cut soft tissues are conveyed to the small package machine via the small package machine channel for packaging. The packaged soft tissues are then conveyed to the storage rack via the storage rack channel, and then conveyed to the case packer via the case packer channel for case packing. Data from each operating device is collected by sensors.

[0058] Includes the following steps:

[0059] S1, Data Acquisition and Integration: Through the data acquisition and integration subsystem, data from different points on the production line can be acquired and stored at the same time frequency.

[0060] S2, Data Processing and Push: Based on the set processing frequency, when the processing time arrives, the data acquisition and integration subsystem obtains the actual speed of the folding machine, paper (packing) rate, paper rack external circulation to internal circulation count, effective cuts of each cutting channel, actual speed of each small package (packing) machine, and current value or values ​​of film length of each small package machine from the current time to the previous time period. The corresponding sum, average value, minimum value, maximum value, median, etc. are calculated in the data processing subsystem, and these values ​​are then pushed to the prediction subsystem and the early warning and recommendation subsystem.

[0061] S3, the folding machine's operating status and whether the paper storage rate is greater than the threshold are judged. When the folding machine is running normally and at a steady speed, the speed is greater than a certain value and fluctuates within a small range. Therefore, if the minimum value of the actual speed of the folding machine from the current time to the previous time is not less than the set value, and the difference between the maximum value and the minimum value is not greater than the set fluctuation value, then the folding machine is judged to be running normally and at a steady speed.

[0062] The paper storage rate threshold is generally set to a value above 0, taking into account the characteristics of the production line. When the paper storage rate is greater than the threshold, it ensures that the material storage link from the inlet to the outlet of the paper storage rack is full, and that the subsequent simulation calculations are meaningful.

[0063] When the folding machine's operating status matches the paper retention rate, it switches to S4, which predicts changes in the paper retention rate; otherwise, it does not need to switch to S4.

[0064] The paper storage rack has multiple cutting channels, and each cutting channel is equipped with its own cutting machine, small packaging channel, small package machine and storage rack channel.

[0065] S4, Prediction of paper retention rate changes, mainly adopts discrete event simulation method, soft-drawing production line simulation model;

[0066] In the simulation model, the current remaining length lx of the original paper roll x changes over time.

[0067] in This represents the remaining length of the original paper roll X at the start of the simulation, and v is the operating speed of the folding machine (unit: meters per minute). This represents the simulation runtime.

[0068] The folding section of the folding machine is modeled as a generator of long strips of paper. Let t be the time required to generate one long strip of paper.

[0069]

[0070] Where C is the number of tissues in a single pack of soft tissues, L is the length of each tissue (in meters), and v is the operating speed of the folding machine (in meters per minute).

[0071] In step S3, the conveyor section of the folding machine is modeled as a conveyor belt. The length of the conveyor belt is obtained by measurement, and the average speed of the long paper stack is obtained by IOT data analysis. The distance interval between each long paper stack is set to be no less than 0. If the outer circulation of the paper rack is not full, the long paper stack will enter the outer circulation when it reaches the end of the conveyor belt, which facilitates the transfer of long paper.

[0072] In step S3, the outer circulation segment of the paper rack is modeled as a buffer. The average value of the change in paper storage rate caused by the change of a single long strip of paper is obtained based on historical IOT data. The timing of the long strips in the outer circulation entering the inner circulation is affected by the operation of the inner circulation and its subsequent segments. Here, the following processing is performed: the relationship between the current storage of the inner circulation and the time interval between a single long strip entering the inner circulation from the outer circulation is statistically analyzed based on historical IOT data to determine the timing of the long strip entering the inner circulation.

[0073] The inner circulation segment of the paper rack is modeled as a buffer. The timing for the long strip to enter each cutting channel from the inner circulation is when the cutting machine is running normally and the cutting channel allows the addition of a long strip. The priority of entering each channel is consistent with the actual situation.

[0074] In step S3, the cutting channel is modeled as a buffer, and the maximum capacity of each channel is obtained through historical IoT data analysis and actual production line observation.

[0075] The cutting machine is modeled as a small paper stack generator. As long as there is a long paper stack in a certain cutting channel and the corresponding small package entry channel is allowed to run normally, a small paper stack will be generated according to the cutting machine's running speed (unit: package / minute).

[0076] For the same product, a long strip of paper generates the same number of n smaller paper stacks. After the n smaller paper stacks are generated, the long strip of paper disappears from the cutting channel.

[0077] In step S3, each small paper machine channel is modeled as a conveyor belt. The actual running speed of the small paper stacks on the channel will be adjusted according to the density of the paper stacks on the channel. In the model, each paper stack is set to either be stationary or run at the highest speed on the channel. The minimum distance between adjacent small paper stacks is 0. The length of a single paper stack on the channel and the length of each channel are consistent with the actual length.

[0078] Each small packaging machine is modeled as a processor. When the actual speed of the small packaging machine is m packages / minute, the time to process a small stack of paper is 60 / m seconds. When a small packaging machine processes a small stack of paper, the packaging film decreases accordingly. If it runs out, the small packaging machine stops working for a period of time t1. The average time of each film replacement is extracted from historical IoT data to obtain t1.

[0079] In step S3, each storage rack channel is modeled as a conveyor belt. In the model, each small package is either stationary or running at the highest speed on the channel. The minimum distance between adjacent small packages is 0. The length of a single small package along the channel and the length of each channel are consistent with the actual length. On each channel, a small package that reaches the capacity of one storage cell occupies one storage cell upon arrival.

[0080] The storage rack is modeled as a buffer. Based on historical IoT data, the average change in paper storage rate caused by the addition or removal of individual storage compartments is obtained. When the storage rate is 0, the material storage path from the rack's inlet to outlet is assumed to be full, which has almost no impact on paper storage rate prediction. If a case packing machine channel is operating normally and there is free space at the front, small packages from the storage rack can enter the channel.

[0081] In step S3, on the actual physical production line, the channel corresponding to a packing machine is often first connected to the storage rack by multiple channels, and then converged or diverged. In the model, the channel to the packing machine is designed as follows: parallel channels are connected in series so that there is only one channel between the storage rack and a packing machine. The length l2 of a single small package in the channel direction is consistent with the actual length. The minimum distance between adjacent small packages is 0. The running speed v2 of the small package is calculated based on the set speed k of the packing machine (unit: packages / minute), v2 = l2 * k * p.

[0082] The case packer is modeled as a recycling unit; if the case packer malfunctions, small packages will accumulate in the channel.

[0083] During production line operation, a predictive simulation is initiated at fixed time intervals. The initial state of the simulation is consistent with the current state of the production line. If data such as paper inventory rate has corresponding IOT points, the corresponding data is directly transferred from the data processing subsystem to the simulation model. If data such as the current stored material on each conveyor belt does not have corresponding IOT points, it is obtained through IOT data feature analysis. The simulation ends when a certain paper roll is consumed to the point where it meets the replacement conditions, or when the paper inventory rate reaches 100%.

[0084] S5. Based on simulation predictions of a risk of the paper rack's external circulation reaching full capacity, the following different handling methods are adopted according to the predicted time of full capacity:

[0085] ①When t′<=a, an alarm is triggered, “Immediately adjust the speed of the folding machine to the lower limit speed v3”;

[0086] ②When a<t′<=b, an alarm is triggered, “Immediately adjust the speed of the folding machine to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper storage rack”;

[0087] ③ When t′>b, an alarm is triggered, “At (T0+t′-b), the speed of the folding machine is adjusted to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper storage rack”;

[0088] Where T0 represents the predicted start time, t′ represents the predicted time difference of the full rack relative to the initial time, the values ​​of a and b are obtained based on the actual operation of the production line, and v4 is the average speed of the paper rack circulating out of the long strip of paper during the simulation period, converted into the speed of consuming raw paper (unit: meters / minute).

[0089] S6, Determining whether the folding machine needs to be sped up: If at a certain moment the speed of the folding machine is less than the threshold, and the current paper storage rate is lower than the paper storage rate before the set time, then the speed needs to be sped up.

[0090] S7, based on the simulation prediction, suggests that when the folding machine needs to be accelerated, the speed of the folding machine should be adjusted to be balanced with the speed of the later stage of the outer circulation of the paper storage rack at T1. T1 is the moment when the folding machine meets the acceleration conditions in the simulation prediction.

[0091] Working Principle: The system uses sensors in the data acquisition and integration subsystem to collect real-time operating data (speed, count, paper storage rate, film length, etc.) from various equipment in the production process (such as folding machines, cutting machines, small package machines, paper storage racks, and case packers). This data is transmitted to the database via a data communication network, enabling the integration and storage of production data. Based on forecasting needs, the system retrieves and processes relevant data from the database at regular intervals, then pushes this data to the forecasting subsystem and the early warning and recommendation subsystem. The main data pushed includes the minimum and maximum values ​​of the folding machine's actual speed over a previous period, the current value of the paper storage rate, the total number of counts from the outer to inner circulation of the paper storage rack over a previous period, the total number of effective cuts from each cutting channel over a previous period, and the actual speed of each small package (case packer) over a previous period. The distribution of time, the current value of the film length of each small package machine, and the prediction subsystem determine whether there is a risk of the shelf being full and forcing a shutdown. If there is, a full shelf risk warning is issued and a speed adjustment calculation and recommendation for the folding machine is made. If there is no risk of the shelf being full and forcing a shutdown, it is then determined whether the speed of the folding machine needs to be increased. If so, a slow folding machine speed warning is issued and a speed adjustment calculation and recommendation for the folding machine is made. The warning and recommendation subsystem determines whether there is a risk of the shelf being full and forcing a shutdown. If there is, a full shelf risk warning is issued and a speed adjustment calculation and recommendation for the folding machine is made. If there is no risk of the shelf being full and forcing a shutdown, it is then determined whether the speed of the folding machine needs to be increased. If so, a slow folding machine speed warning is issued and a speed adjustment calculation and recommendation for the folding machine is made. This is the entire working process of the balancing system for the soft tissue paper production line based on digital twins. The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0092] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A balancing system for a soft-pack tissue paper production line based on digital twins, characterized in that, include: The system comprises a data acquisition and integration subsystem, a data processing subsystem, a prediction subsystem, an early warning and recommendation subsystem, and a tissue paper production line. The data acquisition and integration subsystem includes sensors, a data communication network, and a database. It collects data from different points on the production line at the same time frequency through various sensors, transmits the data through the data communication network, and stores it in the database. The data processing subsystem retrieves relevant data from the database at certain time intervals according to the prediction needs, processes the data, and then pushes the data to the prediction subsystem and the early warning and recommendation subsystem. The prediction subsystem determines whether the current state of the folding machine is operating normally and at a stable speed. If it is, it predicts the change in the paper retention rate. The warning and recommendation subsystem determines whether there is a risk of the machine being forced to stop due to full capacity. If so, it performs a full capacity risk warning and calculates and recommends adjustments to the folding machine speed. If there is no risk of the machine being forced to stop due to full capacity, then determine whether the speed of the folding machine needs to be increased. If so, issue a warning that the folding machine speed is too slow and make a calculation and recommendation for adjusting the folding machine speed. The tissue paper production line includes a folding section of a folding machine, a conveying section of a folding machine, an external circulation system for the paper storage rack, an internal circulation system for the paper storage rack, a cutting channel, a cutting machine, a small packaging channel, a small package machine, a package storage rack channel, a package storage rack, a case packing machine channel, and a case packing machine.

2. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 1, characterized in that: Includes the following steps: S1, Data Acquisition and Integration: Through the data acquisition and integration subsystem, data from different points on the production line can be acquired and stored at the same time frequency. S2, Data Processing and Push: Based on the set processing frequency, when the processing time arrives, the data acquisition and integration subsystem obtains the actual speed of the folding machine, paper (packing) rate, paper rack external circulation to internal circulation count, effective cuts of each cutting channel, actual speed of each small package (packing) machine, and current value or values ​​of film length of each small package machine from the current time to the previous time period. The corresponding sum, average value, minimum value, maximum value, median, etc. are calculated in the data processing subsystem, and these values ​​are then pushed to the prediction subsystem and the early warning and recommendation subsystem. S3, the folding machine's operating status and whether the paper storage rate is greater than the threshold are judged. When the folding machine is running normally and at a steady speed, the speed is greater than a certain value and fluctuates within a small range. Therefore, if the minimum value of the actual speed of the folding machine from the current time to the previous time is not less than the set value, and the difference between the maximum value and the minimum value is not greater than the set fluctuation value, then the folding machine is judged to be running normally and at a steady speed. The paper storage rate threshold is generally set to a value above 0, taking into account the characteristics of the production line. When the paper storage rate is greater than the threshold, it ensures that the material storage link from the inlet to the outlet of the paper storage rack is full, and that the subsequent simulation calculations are meaningful. S4, Prediction of paper retention rate changes, mainly adopts discrete event simulation method, soft-drawing production line simulation model; In the simulation model, the current remaining length lx of the original paper roll x changes over time. in This represents the remaining length of the original paper roll X at the start of the simulation, and v is the operating speed of the folding machine (unit: meters per minute). This represents the simulation runtime. The folding section of the folding machine is modeled as a generator of long strips of paper. Let t be the time required to generate one long strip of paper. Where C is the number of tissues in a single pack of soft tissues, L is the length of each tissue (in meters), and v is the operating speed of the folding machine (in meters per minute). S5. Based on simulation predictions of a risk of the paper rack's external circulation reaching full capacity, the following different handling methods are adopted according to the predicted time of full capacity: ①When t′<=a, an alarm is triggered, "Immediately adjust the speed of the folding machine to the lower limit speed v3"; ②When a<t′<=b, an alarm is triggered, "Immediately adjust the speed of the folding machine to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper rack"; ③ An alarm is triggered when t′>b, and "at (T0+t′-b), the speed of the folding machine is adjusted to a speed v4 that is balanced with the speed of the later stage of the external circulation of the paper storage rack"; Where T0 represents the predicted start time, t′ represents the predicted time difference of the full rack relative to the initial time, the values ​​of a and b are obtained based on the actual operation of the production line, and v4 is the average speed of the paper rack circulating out of the long strip of paper during the simulation period, converted into the speed of consuming raw paper (unit: meters / minute). S6, Determining whether the folding machine needs to be sped up: If at a certain moment the speed of the folding machine is less than the threshold, and the current paper storage rate is lower than the paper storage rate before the set time, then the speed needs to be sped up. S7, based on the simulation prediction, suggests that when the folding machine needs to be accelerated, "at T1, adjust the speed of the folding machine to be balanced with the speed of the later stage of the outer circulation of the paper storage rack", where T1 is the moment when the folding machine meets the acceleration conditions in the simulation prediction.

3. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 1, characterized in that: The paper storage rack has multiple cutting channels, and each cutting channel is equipped with its own cutting machine, small packaging channel, small package machine and storage rack channel.

4. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, when the folding machine's operating status matches the paper retention rate, the process proceeds to S4, which involves predicting changes in the paper retention rate; otherwise, it is not necessary to proceed to S4.

5. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, the conveyor section of the folding machine is modeled as a conveyor belt. The length of the conveyor belt is obtained by measurement, and the average speed of the long paper stack is obtained by IOT data analysis. The distance interval between each long paper stack is set to be no less than 0. If the outer circulation of the paper rack is not full, the long paper stack will enter the outer circulation when it reaches the end of the conveyor belt.

6. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, the outer circulation segment of the paper storage rack is modeled as a buffer, and the average value of the change in paper storage rate caused by the change of a single long strip of paper is obtained based on historical IOT data. The inner circulation segment of the paper rack is modeled as a buffer. The timing for the long strip to enter each cutting channel from the inner circulation is when the cutting machine is running normally and the cutting channel allows the addition of a long strip. The priority of entering each channel is consistent with the actual situation.

7. A balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, the cutting channel is modeled as a buffer, and the maximum capacity of each channel is obtained through historical IoT data analysis and actual production line observation. The cutting machine is modeled as a small paper stack generator. As long as there is a long paper stack in a certain cutting channel and the corresponding small package entry channel is allowed to run normally, a small paper stack will be generated according to the cutting machine's running speed (unit: package / minute).

8. The balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, each small package entry channel is modeled as a conveyor belt; Each small paper machine is modeled as a processor. When the actual speed of the small paper machine is m packages / minute, the time to process a small stack of paper is 60 / m seconds.

9. A balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, each storage rack channel is modeled as a conveyor belt. In the model, each small package is either stationary or running at the highest speed on the channel. The minimum distance between adjacent small packages is 0. The length of a single small package along the channel and the length of each channel are consistent with the actual length. The storage rack is modeled as a buffer, and the average value of the change in paper storage rate caused by the addition or removal of a single storage material cell is obtained based on historical IoT data.

10. A balancing system for a soft-pack tissue paper production line based on digital twins according to claim 2, characterized in that: In step S3, on the actual physical production line, the channel corresponding to a case packing machine often consists of multiple channels connecting to the storage rack before converging or splitting. The model designs the channel to the case packing machine as follows: parallel channels are connected in series, so that there is only one channel between the storage rack and a case packing machine. The length l2 of a single small package along the channel is consistent with the actual length. The minimum distance between adjacent small packages is 0. The running speed v2 of the small packages is calculated based on the set speed k (unit: packages / minute) of the case packing machine. v2 = l2 * k * p. The case packer is modeled as a recycling unit; if the case packer malfunctions, small packages will accumulate in the channel.

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