A sizing device and control system for a sliver sizing machine

Abstract

The embodiment of the present specification provides a sizing device and a control system on a sizing machine. The device comprises a plurality of groups of transmission rollers, a sizing tank, a storage component, a sizing concentration monitoring device, a sizing temperature monitoring device, a gravity monitoring device and a microprocessor. Wherein the plurality of groups of transmission rollers are respectively configured to transport yarn between a first yarn spool and the sizing tank, and to deliver the yarn to a pressing device, a drying device and a second yarn spool. The sizing tank is configured to store sizing liquid required for sizing. The storage component is configured to deliver at least one of pre-dispensing sizing liquid and dispensing liquid, and to adjust the sizing concentration in the sizing tank. The sizing concentration monitoring device and the sizing temperature monitoring device are configured to monitor the sizing concentration and the sizing temperature of the sizing liquid in the sizing tank. The gravity monitoring device is configured to monitor the weight of the yarn. The microprocessor is configured to evaluate the sizing rate and the estimated change range of the sizing rate, and to control the delivery amount of the pre-dispensing sizing liquid and / or the dispensing liquid.

Description

Technical Field

[0001] This manual relates to the textile industry, and in particular to a sizing device and control system for a sizing machine. Background Technology

[0002] Sizing machines are commonly used in the processing of synthetic fiber fabrics. Their function is to penetrate sizing solution into the fabric fibers and form a sizing film on the yarn surface, making the yarn smooth and abrasion-resistant, preventing static electricity and linting during weaving, and improving the yarn's breakage resistance. Maintaining uniformity in yarn sizing is crucial for ensuring textile quality. Furthermore, the concentration and viscosity of the sizing solution in the sizing tank may fluctuate during the weaving process, affecting the uniformity of yarn sizing. Current technologies often improve sizing uniformity by adjusting the sizing press and drying devices, but this approach is relatively passive and can easily impact production efficiency.

[0003] Therefore, it is desirable to provide a sizing device and control system for a sizing machine that can effectively improve the uniformity of sizing and increase production efficiency. Summary of the Invention

[0004] This specification provides a sizing device for a yarn sizing machine through one or more embodiments. The device includes: multiple sets of drive rollers, a sizing tank, a storage component, a sizing concentration monitoring device, a sizing temperature monitoring device, a gravity monitoring device, and a microprocessor. The multiple sets of drive rollers are respectively configured to convey yarn to be sizing from a first yarn spool to the sizing tank, and to sequentially convey sizing yarn to a sizing press, a drying device, and a second yarn spool. The sizing tank is configured to store the sizing solution required for yarn sizing, and includes a stirring component that operates at different stirring power levels based on the control of the microprocessor. The storage component is mechanically connected to the sizing tank via a feed pipe equipped with a solenoid valve. The storage component is configured to convey at least one of a pre-mixed sizing solution and a conditioning liquid to adjust the sizing concentration in the sizing tank. The sizing concentration monitoring device and the sizing temperature monitoring device are disposed on the inner wall of the sizing tank and configured to monitor the sizing concentration and temperature of the sizing solution in the sizing tank. The gravity monitoring device is disposed in the first yarn bobbin and the second yarn bobbin and is configured to monitor the yarn weight. The microprocessor is communicatively connected to the storage component, the sizing concentration monitoring device, the sizing temperature monitoring device, and the gravity monitoring device, respectively. The microprocessor is configured to: assess the sizing rate and the estimated variation range of the sizing rate based on the yarn weight of the first yarn bobbin and the second yarn bobbin; and control the solenoid valve to control the delivery volume of the pre-mixed sizing solution and / or the blending solution based on the sizing rate and the estimated variation range of the sizing rate.

[0005] This specification provides one or more embodiments of a sizing machine control system, implemented based on a microprocessor of the sizing machine control device. The system includes: an evaluation module configured to evaluate the sizing rate and an estimated variation range of the sizing rate based on the yarn weights of the first yarn bobbin and the second yarn bobbin; and a control module configured to control the solenoid valve to control the delivery volume of the pre-mixed sizing solution and / or the blending solution based on the sizing rate and the estimated variation range of the sizing rate.

[0006] This specification provides one or more embodiments of a sizing machine control method, implemented using a microprocessor based on the sizing machine control device. The method includes: assessing the sizing rate and the estimated variation range of the sizing rate based on the yarn weight of the first yarn bobbin and the second yarn bobbin; and controlling the solenoid valve based on the sizing rate and the estimated variation range of the sizing rate to control the delivery volume of the pre-mixed sizing solution and / or the blending solution.

[0007] This specification provides one or more embodiments of a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the sizing machine sizing control method described in any embodiment of this specification. Attached Figure Description

[0008] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0009] Figure 1 These are exemplary schematic diagrams of a sizing machine apparatus according to some embodiments of this specification;

[0010] Figure 2 These are exemplary block diagrams of a sizing machine control system according to some embodiments of this specification;

[0011] Figure 3 This is an exemplary flowchart of a sizing machine control method according to some embodiments of this specification;

[0012] Figure 4 This is an exemplary flowchart of temperature control according to some embodiments of this specification;

[0013] Figure 5 This is an exemplary flowchart illustrating the control of the delivery volume according to some embodiments of this specification;

[0014] Figure 6 This is an exemplary schematic diagram of a control model shown in some embodiments of this specification. Detailed Implementation

[0015] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0016] It should be understood that the terms “system,” “device,” “unit,” and / or “module” used herein are one way to distinguish different components, elements, parts, sections, or assemblies at different levels. However, if other terms can achieve the same purpose, they may be replaced by other expressions.

[0017] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0019] Figure 1 This is an exemplary schematic diagram of a sizing machine apparatus according to some embodiments of this specification.

[0020] In some embodiments, the sizing device 100 of the sizing machine may include multiple sets of drive rollers 110, sizing trough 120, storage component 130, sizing concentration monitoring device 140, sizing temperature monitoring device 150, gravity monitoring device 160, and microprocessor 170.

[0021] Multiple sets of drive rollers 110 are configured to convey yarn to be sized and / or sized yarn. Drive rollers are devices used to transport items in a workshop and have fixed conveyor tracks. In some embodiments, the sizing machine sizing device 100 may include multiple sets of drive rollers 110, each set of drive rollers being used to convey different batches of yarn to be sized and / or sized yarn.

[0022] The yarn to be sized is unsized fabric fiber. In some embodiments, multiple sets of drive rollers 110 can transport the yarn to be sized from the first yarn spool to the sizing tank 120 to complete the sizing process, thereby obtaining sized yarn. The sized yarn can be processed into products of a certain fineness (e.g., silk thread) for use in weaving, rope making, thread making, knitting, and embroidery.

[0023] In some embodiments, the multiple sets of drive rollers 110 can also sequentially convey the sized yarn to a sizing device, a drying device, and a second yarn spool. The sizing device is a device for sizing the sized yarn, such as a sizing roller. Sizing refers to the process of applying a high sizing force to the yarn as it passes through the sizing roller to process it. The sized yarn, after sizing, can be dried by the drying device. The first yarn spool and the second yarn spool are used to wind and hold the yarn to be sized and the sized yarn, respectively. The dried sized yarn can be conveyed to the second yarn spool for further processing.

[0024] The sizing tank 120 is configured to store the sizing solution required for sizing the yarn. In order to increase the abrasion resistance, smoothness, and antistatic properties of the yarn to be sized, improve its strength and cohesion, and enhance its weavability, the yarn to be sized needs to be sized before weaving.

[0025] In some embodiments, the sizing solution required for yarn sizing may include natural sizing agents such as starch, wild starch, seaweed gum, resin, etc., chemical sizing agents such as polyvinyl alcohol (PVA), polyacrylic acid (PAA), cellulose preparations such as carboxymethyl cellulose (CMC), polyesters, etc. In some embodiments, other components may also be added to the sizing solution, such as preservatives, softeners, hygroscopic agents, abrasion reducers, etc.

[0026] In some embodiments, the slurry tank 120 may include a stirring component, which may operate at different stirring power based on the control of the microprocessor 170 to make the slurry flow within the slurry tank 120 to prevent the slurry from coagulating.

[0027] Storage component 130 is configured to store and / or prepare sizing solution and to deliver at least one of the pre-mixed sizing solution and prepare sizing solution to adjust the sizing concentration in sizing tank 120. The pre-mixed sizing solution is a pre-prepared sizing solution for yarn sizing, and the prepare sizing solution is a liquid used to adjust the sizing concentration, such as water, a low-concentration sizing solution, etc. In some embodiments, storage component 130 may deliver either the pre-mixed sizing solution or the prepare sizing solution alone, or simultaneously.

[0028] In some embodiments, the storage component 130 is mechanically connected to the slurry tank 120 via a feed pipe. The feed pipe is a conduit for conveying pre-mixed slurry and / or conditioning liquid. In some embodiments, the feed pipe is equipped with a solenoid valve, which can control the delivery rate of the pre-mixed slurry and / or conditioning liquid based on the control of the microprocessor 170. The solenoid valve can be opened to varying degrees to adjust the delivery rate of the pre-mixed slurry and / or conditioning liquid.

[0029] The slurry concentration monitoring device 140 and the slurry temperature monitoring device 150 are installed on the inner wall of the slurry tank 120 and are configured to monitor the slurry concentration and temperature in the slurry tank 120. The slurry concentration monitoring device 140 and the slurry temperature monitoring device 150 are installed at a low position on the inner wall of the slurry tank 120 so that monitoring can be performed even when the slurry level is too low.

[0030] In some embodiments, the slurry concentration monitoring device 140 may include a liquid concentration detection instrument, a concentration monitoring sensor, an ultrasonic sensor, a spectrometer, a vibration monitoring instrument, etc., and the slurry temperature monitoring device 150 may include a temperature sensor, a temperature detection instrument, etc. In some embodiments, the slurry concentration monitoring device 140 and the slurry temperature monitoring device 150 are respectively communicatively connected to the microprocessor 170 to send the monitoring results (including slurry concentration and slurry temperature) to the microprocessor 170 respectively.

[0031] A gravity monitoring device 160 is disposed in a first yarn bobbin and a second yarn bobbin and is configured to monitor the weight of the yarn. For example, a gravity monitoring device 160 may be disposed in both the first yarn bobbin and the second yarn bobbin. In some embodiments, the gravity monitoring device 160 may include a pressure sensor, a gravity measuring instrument, etc. In some embodiments, the gravity monitoring device 160 is communicatively connected to a microprocessor 170 to transmit the measured yarn weight to the microprocessor 170.

[0032] The microprocessor 170 is communicatively connected to the storage unit 130, the slurry concentration monitoring device 140, the slurry temperature monitoring device 150, and the gravity monitoring device 160, respectively. In some embodiments, the microprocessor 170 can process data and / or information obtained from other devices / components or components. The microprocessor 170 can execute program instructions based on this data, information, and / or processing results to perform one or more functions described in the embodiments of this specification. By way of example only, the microprocessor 170 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or any combination thereof.

[0033] In some embodiments, the microprocessor 170 may be configured to: assess the sizing rate and the estimated variation range of the sizing rate based on the yarn weights of the first and second yarn spools; and control the solenoid valve to control the delivery volume of the pre-mixed sizing solution and / or the conditioning solution based on the sizing rate and the estimated variation range of the sizing rate. For more information on the sizing rate, the estimated variation range of the sizing rate, and how the microprocessor 170 specifically implements the sizing machine control method described in the embodiments of this specification, please refer to [link to relevant documentation]. Figure 2-6 And its related descriptions.

[0034] In some embodiments, the sizing device 100 of the sizing machine may further include a temperature control component (not shown in the figure). The temperature control component is mechanically connected to the sizing temperature monitoring device 150 and is configured to control the sizing temperature in the sizing tank 120. In some embodiments, the microprocessor 170 can control the sizing temperature in the sizing tank 120 based on the sizing temperature monitored by the sizing temperature monitoring device 150 by controlling the temperature control component. More information on temperature control can be found in [link to relevant documentation]. Figure 4 And its related descriptions.

[0035] In some embodiments, the sizing device 100 of the sizing machine may further include a flow meter (not shown). The flow meter is configured to acquire flow rate data of the pre-mixed sizing solution and / or the conditioning solution. The flow meter may be configured on the feed pipe. In some embodiments, the microprocessor 170 may regulate the delivery rate of the pre-mixed sizing solution and / or the conditioning solution based on the sizing concentration monitored by the sizing concentration monitoring device 140 and the flow rate data acquired by the flow meter. Further details on how to regulate the delivery rate can be found in [link to relevant documentation]. Figure 5 And its related descriptions.

[0036] In the embodiments of this specification, the microprocessor 170 obtains the yarn weight of the first yarn spool and the second yarn spool through a communication connection with other components in the sizing device 100 of the sizing machine. It further evaluates the sizing rate and the estimated variation range of the sizing rate to control the delivery amount of the pre-mixed sizing solution and / or the mixing solution. It comprehensively considers the sizing situation of the yarn sizing process, avoids the sizing process of the yarn being affected by the sizing rate being too high or too low, and assists the operator in monitoring the uniformity of yarn sizing in the sizing machine.

[0037] Figure 2 This is an exemplary block diagram of a sizing machine control system according to some embodiments of this specification.

[0038] In some embodiments, the sizing control system 200 of the sizing machine can be implemented based on the microprocessor 170, and the sizing control system 200 may include an evaluation module 210 and a control module 220.

[0039] Evaluation module 210 is configured to acquire the yarn weight of the first yarn spool and the second yarn spool, and based on the yarn weight of the first yarn spool and the second yarn spool, evaluate the sizing rate and the estimated variation of the sizing rate. In some embodiments, evaluation module 210 may monitor the yarn weight using a gravity monitoring device 160 disposed in the first yarn spool and the second yarn spool.

[0040] Control module 220 is configured to control a solenoid valve to control the delivery rate of pre-mixed slurry and / or blending liquid based on the slurry coating rate and its estimated variation. In some embodiments, control module 220 is further configured to determine slurry temperature change information based on slurry temperature; determine the storage temperature of the storage component based on ambient temperature; and control the adjustment misalignment time of the temperature control component based on the slurry temperature change information, slurry coating rate, storage temperature, and slurry concentration. In some embodiments, control module 220 may also be configured to determine slurry concentration fluctuation information based on slurry concentration at multiple consecutive time points; determine the degree of deviation between flow rate data and slurry concentration fluctuation information; and adjust the delivery rate of pre-mixed slurry and / or blending liquid in response to the degree of deviation not meeting a preset condition. In some embodiments, control module 220 may also be configured to control the delivery speed of at least one of the pre-mixed slurry and the blending liquid based on the solenoid valve, wherein the delivery speed is determined based on the slurry temperature change information and the degree of deviation.

[0041] It should be understood that Figure 2 The system and its modules shown can be implemented in various ways.

[0042] It should be noted that the above description of the sizing control system and its modules for the sizing machine is for convenience only and should not be construed as limiting this specification to the scope of the embodiments described. It is understood that those skilled in the art, after understanding the principle of the system, may arbitrarily combine the various modules or construct subsystems connected to other modules without departing from this principle. In some embodiments, Figure 2 The evaluation module and control module disclosed herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, modules can share a single storage module, or each module can have its own separate storage module. Such variations are all within the scope of protection of this specification.

[0043] Figure 3 This is an exemplary flowchart of a sizing machine control method according to some embodiments of this specification. In some embodiments, process 300 may be executed by microprocessor 170. Figure 3 As shown, process 300 includes the following steps:

[0044] Step 310: Obtain the yarn weight of the first yarn spool and the second yarn spool.

[0045] The first yarn bobbin is used to wind and hold the yarn to be sized, and the second yarn bobbin is used to wind and hold the sized yarn. Changes in the weight of the first and second yarn bobbins reflect the weight of the yarn to be sized and the weight of the sized yarn, respectively. In some embodiments, the microprocessor 170 can monitor the weight of the yarn to be sized and the weight of the sized yarn using gravity monitoring devices 160 respectively disposed in the first and second yarn bobbins. The microprocessor 170 can acquire the weights of the first and second yarn bobbins monitored by the gravity monitoring devices 160 at multiple time points, thereby obtaining the weights of the yarn to be sized and the weights of the sized yarn.

[0046] When sizing the yarn to be sized, it is gradually removed from the first yarn bobbin, and the weight of the first yarn bobbin gradually decreases. Therefore, the decrease in the weight of the first yarn bobbin can reflect the weight of the yarn to be sized. In some embodiments, the microprocessor 170 can subtract the weight of the first yarn bobbin from the weight of the first yarn bobbin at the previous time point from the weight of the first yarn bobbin at a later time point, as the weight change of the first yarn bobbin. Since the weight of the first yarn bobbin gradually decreases, the weight change of the first yarn bobbin is negative, and the microprocessor 170 can use the absolute value of the weight change of the first yarn bobbin as the weight of the yarn to be sized.

[0047] Similarly, after the sizing process is complete, the sized yarn will gradually wind onto the second yarn bobbin, and the weight of the second yarn bobbin will gradually increase. This increase in weight reflects the weight of the sized yarn. In some embodiments, the microprocessor 170 can subtract the weight of the second yarn bobbin from the weight of the second yarn bobbin at the previous time point from the weight at a later time point to obtain the weight change of the second yarn bobbin. Since the weight of the second yarn bobbin gradually increases, the weight change is positive, and the microprocessor 170 can directly use the weight change of the second yarn bobbin as the weight of the sized yarn.

[0048] Step 320: Based on the yarn weights of the first and second yarn spools, assess the sizing rate and the estimated variation of the sizing rate.

[0049] Sizing rate is the ratio of the weight of sizing solution in the yarn to the weight of the yarn after sizing. If the sizing rate is too high, it means there is too much sizing solution on the surface of the yarn, which can easily lead to decreased yarn elasticity and increased elongation. This can cause sizing slippage and brittle yarn breakage during weaving. At the same time, the large amount of sizing material consumed increases sizing costs. If the sizing rate is too low, it can easily cause light sizing and pilling, unclear weaving on the loom, and increased warp yarn breakage, thus affecting production.

[0050] In some embodiments, the microprocessor 170 may use the ratio of the yarn weight difference to the weight of the unsized yarn as the sizing rate, and the yarn weight difference may be determined by subtracting the weight of the unsized yarn from the weight of the sized yarn.

[0051] As previously mentioned, the absolute value of the weight change of the first yarn spool can be used as the yarn weight of the yarn to be sized, and the weight change of the second yarn spool can be used as the yarn weight of the sized yarn. In some embodiments, the microprocessor 170 can determine the sizing rate by the absolute values ​​of the weight changes of the second yarn spool and the first yarn spool.

[0052] The microprocessor 170 can first subtract the absolute value of the weight change of the first yarn spool from the weight change of the second yarn spool, and then divide by the absolute value of the weight change of the first yarn spool to obtain the sizing rate. The difference between the weight change of the second yarn spool and the weight change of the first yarn spool can be considered as the weight of the sized yarn minus the weight of the yarn to be sized, i.e., the weight difference of the yarns, which gives the weight of the sizing solution contained in the yarn after sizing.

[0053] The estimated change in sizing rate is the predicted change in sizing rate over a future period. In some embodiments, the microprocessor 170 can determine the estimated change in sizing rate based on historical changes in sizing rate. For example, the microprocessor 170 can estimate the estimated change in sizing rate using a nonlinear fitting method based on historical changes in sizing rate at multiple time points.

[0054] In some embodiments, the microprocessor 170 can determine the historical variation range of multiple time points based on the sizing rate at multiple historical time points and the sizing rate at a reference time point. The sizing rate at the reference time point can be the sizing rate when the sizing process has stabilized after its commencement. The microprocessor 170 can acquire the sizing rates at n time points, the sizing rate at the reference time point, and the corresponding times for the n time points. For each of the n time points, the microprocessor 170 can subtract the sizing rate at the reference time point from the sizing rate at that time point, and then divide by the time period length to determine the historical variation range at that time point. The time period length can be determined by the absolute value of the difference between the time point and the reference time point.

[0055] Step 320: Based on the sizing rate and the estimated variation range of the sizing rate, control the solenoid valve to control the delivery volume of the pre-mixed slurry and / or the blending liquid.

[0056] In the sizing device 100 of the sizing machine, the storage component 130 is mechanically connected to the sizing tank 120 via a feed pipe. The feed pipe is a conduit for conveying pre-mixed sizing solution and / or conditioning liquid. A solenoid valve, mounted on the feed pipe, is a device for controlling the conveying volume of the pre-mixed sizing solution and / or conditioning liquid, and can further regulate the sizing concentration in the sizing tank 120. The pre-mixed sizing solution is a pre-prepared sizing solution for yarn sizing, and the conditioning liquid is a liquid used to adjust the sizing concentration.

[0057] In some embodiments, the microprocessor 170 can determine whether the sizing rate meets preset delivery conditions. Preset delivery conditions may include a sizing rate that is below or above a preset normal range for sizing rates. When the sizing rate is within the preset normal range, it indicates that the quality of the yarn sizing is guaranteed, and normal sizing processing can be performed. When the sizing rate meets the preset delivery conditions, it indicates that the sizing rate exceeds the preset normal range, and pre-mixed sizing solution and / or conditioning solution need to be delivered to adjust the sizing concentration in the sizing tank 120. In some embodiments, in response to the sizing rate meeting the preset delivery conditions, the microprocessor 170 can control a solenoid valve to deliver the pre-mixed sizing solution and / or conditioning solution respectively.

[0058] In some embodiments, the microprocessor 170 can also adjust the delivery rate of the pre-mixed slurry and / or the blending liquid in advance based on the estimated change in the slurry rate. For example, if the estimated change in the slurry rate is negative, indicating that the slurry rate will gradually decrease, the microprocessor 170 can increase the delivery rate of the pre-mixed slurry in advance, for example, by opening the solenoid valve.

[0059] In some embodiments, the microprocessor 170 can also adjust the delivery rate of the pre-mixed slurry and / or the conditioning solution based on the degree of deviation between the flow rate data and the slurry concentration fluctuation information. For more information on how to adjust the delivery rate, please refer to [link to relevant documentation]. Figure 5 The details and related descriptions will not be repeated here.

[0060] In the embodiments of this specification, by evaluating the sizing rate of the yarn and the estimated change range of the sizing rate, the delivery volume of the pre-mixed sizing solution and / or the blending solution can be controlled. The sizing status of the yarn can be monitored in real time, and the sizing concentration in the sizing tank 120 can be adjusted in a timely manner to ensure the sizing quality of the yarn.

[0061] In some embodiments, the microprocessor 170 may also determine the conveying speed based on slurry temperature change information and deviation, and control the conveying speed of at least one of the pre-mixed slurry and the blending liquid based on the solenoid valve.

[0062] The conveying speed is the amount of pre-mixed slurry and / or conditioning liquid conveyed per unit time. In some embodiments, the conveying speed can be monitored by a flow meter configured on the conveying pipe. In some embodiments, the microprocessor 170 can obtain the current conveying speed of the pre-mixed slurry and / or conditioning liquid via the flow meter.

[0063] In some embodiments, the microprocessor 170 can determine the conveying speed based on the fluctuation range and deviation of the slurry temperature. The conveying speed is determined by the current conveying speed and the magnitude of any decrease or increase in the current conveying speed. The magnitude of the decrease or increase in the current conveying speed is positively correlated with the absolute values ​​of the fluctuation range and deviation of the slurry temperature. More information on the fluctuation range and deviation of the slurry temperature can be found in [link to relevant documentation]. Figure 5 The relevant descriptions will not be repeated here. As an example only, the greater the fluctuation range and / or deviation of the slurry temperature, the greater the possible decrease or increase in the conveying speed, and vice versa. If the conveying speed is determined based on the fluctuation range of the slurry temperature, the conveying speed will decrease; if the deviation is positive, the conveying speed will decrease; if the deviation is negative, the conveying speed will increase.

[0064] In some embodiments, in response to the presence of a temperature unevenness problem, the microprocessor 170 may determine the conveying speed based on the historical duration of the temperature unevenness problem. The historical duration of the temperature unevenness problem may also be referred to as the duration of historical temperature unevenness. For example, the microprocessor 170 may reduce the conveying speed by a magnitude positively correlated with the historical duration of the temperature unevenness problem. More information about temperature unevenness problems and the duration of historical temperature unevenness can be found in [link to relevant documentation]. Figure 4 The details and related descriptions will not be repeated here.

[0065] In some embodiments, in response to the presence of temperature unevenness, the conveying speed determined based on slurry temperature change information and the degree of deviation can be further multiplied by a temperature unevenness coefficient. The result of this multiplication is the final determined conveying speed. The temperature unevenness coefficient is a value that quantifies the degree of temperature unevenness. The longer the temperature unevenness problem persists, the smaller the corresponding temperature unevenness coefficient. For example, the temperature unevenness coefficient can be expressed as... T h The value represents the historical duration of the temperature unevenness problem, and C represents the preset coefficient.

[0066] In the embodiments of this specification, when there is a problem of uneven temperature, considering the large fluctuations in the slurry temperature, the conveying speed is determined based on the historical duration of the uneven temperature problem. This allows for the determination of a more reasonable and effective conveying speed, ensuring uniform slurry application.

[0067] In some embodiments, the microprocessor 170 can adjust the conveying speed of at least one of the pre-mixed slurry and the conditioning liquid based on a solenoid valve. For example, based on a determined conveying speed, the microprocessor 170 can adjust the opening degree of the solenoid valve according to the feed pipe corresponding to the storage component storing the pre-mixed slurry and the corresponding solenoid valve, thereby adjusting the conveying speed of the pre-mixed slurry. As another example, based on a determined conveying speed, the microprocessor 170 can adjust the opening degree of the solenoid valve according to the feed pipe corresponding to the storage component storing the conditioning liquid and the corresponding solenoid valve, thereby adjusting the conveying speed of the conditioning liquid.

[0068] When the conveying speed is too high, the temperature of the pre-mixed slurry and / or conditioning liquid stored in the storage component 130 may fluctuate significantly with the temperature of the slurry in the slurry tank 120 due to the inconsistency between the two temperatures. In the embodiments of this specification, a more reasonable and effective conveying speed can be determined based on the slurry temperature change data and the degree of deviation in slurry concentration to ensure uniform slurry application.

[0069] It should be noted that the above description of process 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0070] Figure 4 This is an exemplary flowchart of temperature control according to some embodiments of this specification. In some embodiments, process 300 may be executed by microprocessor 170. Figure 4 As shown, process 400 includes the following steps:

[0071] Step 410: Determine the slurry temperature change information based on the slurry temperature.

[0072] The slurry temperature refers to the temperature of the slurry stored in the slurry tank 120. The slurry temperature can be obtained through a slurry temperature monitoring device 150 installed on the inner wall of the slurry tank 120. The slurry temperature may vary at different locations within the slurry tank 120, including different horizontal positions and different vertical layers. The slurry temperature may also differ before and after adding pre-mixed slurry and / or conditioning liquid to the slurry tank 120. Therefore, temperature control can be implemented by comprehensively considering slurry temperature changes to avoid the impact of abnormal temperatures on the slurry application process.

[0073] Slurry temperature change information reflects the changes in slurry temperature at different points in time. In some embodiments, the microprocessor 170 can acquire the slurry temperature detected in real time by the slurry temperature monitoring device 150 at multiple consecutive time points, and determine the slurry temperature change information based on the slurry temperature at multiple consecutive time points.

[0074] Step 420: Determine the storage temperature of the storage component based on the ambient temperature.

[0075] The storage temperature is the temperature of the pre-mixed slurry and / or blending liquid stored in storage component 130. Since storage component 120 is located in a production workshop environment, the storage temperature is affected by the ambient temperature. Ambient temperature refers to the ambient temperature of the production workshop where storage component 120 is located. In some embodiments, the ambient temperature can be obtained by a temperature detection device (e.g., a temperature sensor) installed in the production workshop. In some embodiments, microprocessor 170 can obtain the ambient temperature through the temperature detection device and determine the storage temperature based on the ambient temperature. For example, the ambient temperature can be determined as the storage temperature.

[0076] Step 430: Based on the slurry temperature change information, slurry application rate, storage temperature and slurry concentration, control the adjustment misalignment time of the temperature control component.

[0077] The slurry concentration refers to the concentration of the slurry stored in the slurry tank 120. The slurry concentration can be obtained through a slurry concentration monitoring device 140 installed on the inner wall of the slurry tank 120. For more information on slurry application rate, please refer to [link to relevant documentation]. Figure 3 The details and related descriptions will not be repeated here.

[0078] The adjustment misalignment time refers to the time difference between advancing or delaying temperature adjustment. Since temperature adjustment often has a certain lag, determining a reasonable adjustment misalignment time can, to some extent, reduce the fluctuation range of slurry temperature and improve its stability. In some embodiments, the microprocessor 170 can control the temperature adjustment component to regulate the slurry temperature in the slurry tank 120 based on a determined adjustment misalignment time. For example, the temperature adjustment component can perform heating or cooling.

[0079] In some embodiments, the microprocessor 170 can control the adjustment misalignment time of the temperature control component based on slurry temperature change information, slurry application rate, storage temperature, and slurry concentration. In some embodiments, the microprocessor 170 can establish a vector database based on historical slurry experience data and determine the corresponding adjustment misalignment time based on matching vector retrieval. In some embodiments, the microprocessor 170 can construct a matching vector based on slurry temperature change information, slurry application rate, storage temperature, and slurry concentration. There are various ways to construct the matching vector. For example, a feature vector p can be constructed based on slurry features (x, y, m, n), where the slurry features (x, y, m, n) can represent the corresponding slurry temperature data as x, slurry application rate as y, storage temperature as m, and slurry concentration as n.

[0080] The vector database can include multiple reference vectors and corresponding reference control misalignment times. Reference vectors can be constructed based on reference slurry temperature variation information, reference slurry application rate, reference storage temperature, and reference slurry concentration. The reference slurry temperature variation information, reference slurry application rate, reference storage temperature, and reference slurry concentration can be determined based on historical slurry experience data. The construction method of reference vectors is similar to that of the vectors to be matched. The reference control misalignment time can be determined through the historical control experience corresponding to the reference vector.

[0081] In some embodiments, the microprocessor 170 can determine the adjustment misalignment time based on the similarity between the vector to be matched and multiple reference vectors in a vector database. For example, a reference vector whose similarity to the vector to be matched meets a preset vector condition can be used as the target vector, and the reference adjustment misalignment time corresponding to the target vector can be used as the adjustment misalignment time. The preset vector condition can be set according to the situation. For example, maximum similarity or similarity greater than a threshold.

[0082] In some embodiments, the microprocessor 170 can further estimate the control misalignment time based on information about changes in slurry temperature, changes in slurry coating rate, fluctuations in storage temperature and slurry concentration, using a control model. The control model is a machine learning model, such as a convolutional neural network model. Further details about the control model can be found in [link to relevant documentation]. Figure 6 And its related descriptions.

[0083] The inputs to the control model can include information on changes in slurry temperature, changes in slurry coating rate, fluctuations in storage temperature and slurry concentration, multiple candidate control misalignment times, and the delivery volume of pre-mixed slurry and / or blending liquid. The output can include whether the predicted change sequence of slurry coating rate is within the preset normal range under the corresponding candidate control misalignment time.

[0084] The information on the change in sizing rate reflects the variation in sizing rate at different time points. In some embodiments, the microprocessor 170 can evaluate the sizing rate at multiple consecutive time points and determine the information on the change in sizing rate based on the sizing rate at multiple time points. The information on the fluctuation in slurry concentration reflects the variation in slurry concentration at different time points.

[0085] In some embodiments, the microprocessor 170 can acquire slurry concentrations at multiple consecutive time points and determine slurry concentration fluctuation information based on the slurry concentrations at multiple time points. Multiple sets of candidate control times are control misalignment times obtained based on historical data. In some embodiments, the microprocessor 170 can acquire multiple sets of candidate control times through a vector database established based on historical slurry experience data; for example, reference control misalignment times corresponding to multiple reference vectors can be used as multiple sets of candidate control times. The delivery volume of pre-mixed slurry and / or blending solution is the delivery volume at the current time point, where 0 can represent no delivery.

[0086] The predicted variation sequence of sizing rate is the change in sizing rate predicted by the control model. For information on the normal range of the preset sizing rate, please refer to [link / reference needed]. Figure 3 And related descriptions. If the predicted change sequence of sizing rate is within the preset normal range of sizing rate, it means that the sizing rate after temperature regulation based on the candidate regulation misalignment time meets the requirements and sizing processing can be carried out normally. In some embodiments, the microprocessor 170 can determine the candidate regulation misalignment time when the amplitude of the predicted change sequence of sizing rate is within the preset normal range of sizing rate as the regulation misalignment time to be executed.

[0087] In some embodiments, the control model can be trained using multiple training samples with training labels. In some embodiments, the training samples may include at least information on sample slurry temperature changes, sample slurry coating rate changes, sample slurry concentration fluctuations, sample storage temperature, sample control misalignment time, and sample delivery volume. The training label may be whether the slurry coating rate change sequence is within a preset normal range. In some embodiments, the training labels may be based on historical data or experimental data. For example, based on historical data or experimental data, under the same or similar conditions as the training samples, training samples whose subsequent slurry coating rate fluctuations are within the preset normal range are labeled as 1, otherwise labeled as 0.

[0088] In some embodiments, the structure of the initial regulation model may include an LSTM (Long Short-Term Memory Networks) model and a NN (Neural Network) model. More information on regulation models can be found in [link to relevant documentation]. Figure 6 And its related descriptions.

[0089] In the embodiments of this specification, by predicting the control misalignment time through the control model, the self-learning ability of the machine learning model can be utilized to find patterns from a large amount of historical data, and obtain the relationship between information such as changes in slurry temperature, changes in slurry loading rate, fluctuations in storage temperature and slurry concentration, thereby improving the accuracy and efficiency of predicting the control misalignment time.

[0090] In some embodiments, the microprocessor 170 can also determine how to implement temperature control measures based on slurry temperature change information. In some embodiments, the microprocessor 170 can further determine whether there is a temperature unevenness problem based on the slurry temperature change information.

[0091] Temperature unevenness is a description of the phenomenon where the temperature of the slurry within the slurry tank 120 is not uniform. Under normal circumstances, the temperature gradually rises until it stabilizes; however, the actual monitored slurry temperature may fluctuate significantly, leading to temperature unevenness. For example, the addition of pre-mixed slurry and / or conditioning solution may cause temperature unevenness within the slurry tank 120 (e.g., fluctuating temperatures). Similarly, stratification (e.g., slurry sedimentation) can also cause temperature unevenness.

[0092] In some embodiments, if the slurry temperature change information shows fluctuations, the microprocessor 170 can determine that a temperature unevenness problem exists. In some embodiments, if the temperature fluctuation amplitude of the slurry temperature change information exceeds a fluctuation threshold, the microprocessor 170 can determine that a temperature unevenness problem exists. The fluctuation threshold is a preset threshold used to determine whether a temperature unevenness problem exists. The fluctuation threshold can be set manually or adjusted based on historical data.

[0093] In some embodiments, in response to the presence of temperature unevenness, the microprocessor 170 may determine the updated stirring power and / or local heating intensity of the stirring component based on the duration of historical temperature unevenness.

[0094] The duration of historical temperature inhomogeneity refers to the duration of temperature inhomogeneity during the historical pulping process. In other words, it is the duration from the point at which the existence of temperature inhomogeneity is determined until the temperature stabilizes.

[0095] The stirring power affects the number of stirring revolutions of the stirring component per unit time. Even when temperature unevenness exists, the stirring component continues to operate, causing the slurry to flow within the slurry tank 120. Excessive stirring power may increase the risk of temperature fluctuations in the slurry within the slurry tank 120; therefore, a suitable stirring power needs to be determined when temperature unevenness exists. Updated stirring power is the stirring power of the stirring component re-determined based on the historical duration of temperature unevenness.

[0096] In some embodiments, the microprocessor 170 can determine the updated stirring power based on historical stirring power and the duration of historical temperature unevenness. In some embodiments, the microprocessor 170 can identify two historical temperature unevenness problems (e.g., those from the most recent time period) with temperature fluctuations greater than a preset fluctuation threshold and similar amplitudes as a first historical temperature unevenness problem and a second historical temperature unevenness problem. The microprocessor 170 can further determine the updated stirring power based on the historical stirring power corresponding to the first historical temperature unevenness problem (first historical stirring power), the historical stirring power corresponding to the second historical temperature unevenness problem (second historical stirring power), the duration of the first historical temperature unevenness problem (first duration), and the duration of the second historical temperature unevenness problem (second duration). The first historical stirring power corresponds to the first duration, and the second historical stirring power corresponds to the second duration. The second historical stirring power is greater than or equal to the first historical stirring power.

[0097] In some embodiments, the microprocessor 170 may further determine the updated stirring power according to the following formula (1): Where P represents the refresh stirring power, P h1 P represents the first historical stirring power. h2 Let T1 represent the first historical stirring power and T2 represent the second historical stirring duration. According to the above formula (1), the updated stirring power can be determined by the relationship between the historical stirring power and duration corresponding to the two historical temperature unevenness problems.

[0098] For the first and second historical stirring powers, the temperature fluctuation amplitudes corresponding to the temperature unevenness problems are similar to those corresponding to the temperature unevenness problem for which the current stirring power needs to be determined. Therefore, they can be used to determine the updated stirring power. The durations of the two temperature unevenness problems (i.e., the first duration and the second duration) are also similar. However, if the duration of the temperature unevenness problem corresponding to the greater stirring power is longer, it indicates that the greater stirring power may be over-stirring. Therefore, if the durations of temperature fluctuations (i.e., the durations of temperature unevenness problems) are different during the two historical stirring power adjustments, a stirring power that shortens the duration of temperature fluctuations can be used.

[0099] Localized heating involves heating only a portion of the sizing tank 120 to minimize the impact of uneven temperature. Localized heating intensity can include both the heated area and the localized heating power. The heated area is the part of the sizing tank 120 that is locally heated, and can include any part of the sizing tank 120, such as the upper half, lower half, or surface layer. Localized heating power is the amount of heat transferred per unit time during localized heating.

[0100] In some embodiments, the microprocessor 170 can determine the local heating intensity of the stirring component based on the duration of historical temperature unevenness. The local heating intensity is related to (e.g., positively correlated with) the delivery rate of the pre-mixed slurry and / or formulation. The microprocessor 170 can further determine the local heating intensity of the stirring component based on the delivery rate of the pre-mixed slurry and / or formulation. A faster delivery rate of the pre-mixed slurry and / or formulation results in a greater local heating intensity. A slower delivery rate of the pre-mixed slurry and / or formulation results in a lower local heating intensity. In some embodiments, the delivery rate of the pre-mixed slurry and / or formulation and the local heating intensity can be divided into different levels, with each level corresponding to a specific delivery rate and local heating intensity.

[0101] For more information on the delivery speed of pre-mixed slurry and / or blending solution, please refer to [link / reference]. Figure 6 And its related descriptions.

[0102] In some embodiments, the microprocessor 170 may control the temperature control components to perform local heating to regulate the temperature of the slurry in the slurry tank 120 based on a determined local heating intensity.

[0103] In the embodiments of this specification, proper stirring can avoid uneven temperature of the slurry in the slurry tank. Based on the duration of historical temperature unevenness, a reasonable stirring power can be determined to ensure stable slurry temperature.

[0104] In the embodiments described in this specification, the sizing rate is affected not only by changes in sizing concentration but also by fluctuations in sizing temperature. Timely detection and targeted temperature control of the sizing tank 120 when changes in sizing concentration occur can effectively ensure the sizing rate of the yarn. Considering the lag problem in temperature control, by combining information on sizing temperature changes with the adjustment misalignment time of the temperature control component based on sizing rate, storage temperature, and sizing concentration, the amplitude of sizing temperature fluctuations can be reduced, improving stability.

[0105] It should be noted that the above description of process 400 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 400 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0106] Figure 5 This is an exemplary flowchart illustrating the flow rate control according to some embodiments of this specification. In some embodiments, process 500 may be executed by microprocessor 170. Figure 5 As shown, process 500 includes the following steps:

[0107] Step 510: Determine the slurry concentration fluctuation information based on the slurry concentration at multiple consecutive time points.

[0108] Slurry concentration fluctuation information reflects the changes in slurry concentration at different time points. Slurry concentration can be acquired by a slurry concentration monitoring device 140 installed on the inner wall of the slurry tank 120. In some embodiments, the microprocessor 170 can acquire slurry concentrations at multiple consecutive time points and determine slurry concentration fluctuation information based on the slurry concentrations at these multiple consecutive time points.

[0109] Step 520: Determine the degree of deviation between the flow rate data and the slurry concentration fluctuation information.

[0110] Flow data refers to the total amount of fluid passing through the pre-mixed slurry and / or conditioning solution within a certain time period. In some embodiments, flow data can be acquired by a flow meter configured on the delivery pipe. The flow meter can be communicatively connected to the microprocessor 170. In some embodiments, the microprocessor 170 can acquire flow data of the pre-mixed slurry and / or conditioning solution through the flow meter.

[0111] The degree of deviation reflects the extent to which changes in flow rate or slurry concentration match the preset flow rate or preset concentration. The preset flow rate and preset concentration are predetermined values ​​to ensure stable slurry application rate. The degree of deviation can be positive or negative. A high absolute value of the deviation indicates significant short-term fluctuations in flow rate and / or slurry concentration, which will further affect the fluctuation range of the slurry application rate.

[0112] For example, when adding pre-mixed slurry and / or conditioning solution into the slurry tank 120, it is necessary to ensure that the slurry concentration remains stable within a preset concentration risk range. The preset concentration risk range is a pre-determined range of slurry concentration used to ensure a stable slurry application rate. For example, the preset slurry concentration risk range may include a preset concentration and its ±3% range. If the slurry concentration remains stable within the preset concentration risk range, it indicates a low degree of deviation. If the slurry concentration is below the preset concentration risk range, the degree of deviation is negative; if the slurry concentration is above the preset concentration risk range, the degree of deviation is positive.

[0113] In some embodiments, the degree of deviation can be expressed as a percentage. For example, if the slurry concentration exceeds 10% of the preset concentration risk range, the deviation is 10%. If the slurry concentration is below 10% of the preset concentration risk range, the deviation is -10%.

[0114] In some embodiments, the microprocessor 170 can determine the degree of deviation based on flow data and / or slurry concentration fluctuation information and concentration risk range.

[0115] In some embodiments, the microprocessor 170 can estimate the change in slurry concentration based on flow rate data. For example, the change in slurry concentration can be estimated based on flow rate data and a lookup table. The lookup table is a pre-constructed data table that represents the flow rate data and the change in slurry concentration. The change in slurry concentration is the estimated change in slurry concentration over a future period.

[0116] The microprocessor 170 can further determine the degree of deviation based on the estimated change in slurry concentration and the concentration risk range. For example, it can determine the estimated slurry concentration based on the estimated change in slurry concentration, and then determine the degree of deviation based on the magnitude of the deviation of the estimated slurry concentration from the preset concentration risk range. Specifically, the estimated slurry concentration, i.e., the estimated slurry concentration at a future time point, can be determined based on the estimated change in slurry concentration and the current slurry concentration. If the estimated slurry concentration is within the preset concentration risk range, the degree of deviation is 0. If the estimated slurry concentration is lower or higher than the preset concentration risk range, the absolute value of the degree of deviation is greater than 0.

[0117] In some embodiments, the absolute value of the deviation can be divided into different levels based on the magnitude of the deviation. For example, the absolute value of the deviation can be divided into different deviation levels such as 10%, 20%, 30%, 40%...90%. A larger deviation level indicates a greater degree of deviation.

[0118] In some embodiments, the microprocessor 170 can determine the degree of deviation based on whether the slurry concentration fluctuation information at multiple consecutive time points exceeds a preset concentration risk range. If the slurry concentration at each time point in the slurry concentration fluctuation information is within the preset concentration risk range, the degree of deviation is 0. If the slurry concentration at one or more time points in the slurry concentration fluctuation information exceeds or falls below the preset concentration risk range, the degree of deviation is greater than 0 or less than 0. The more the slurry concentration exceeds the preset concentration risk range, the greater the absolute value of the degree of deviation.

[0119] To ensure a stable slurry coating rate, changes in slurry temperature also need to be considered. Therefore, in some embodiments, the microprocessor 170 can update the preset concentration risk range of the slurry concentration based on the adjusted slurry temperature.

[0120] The preset concentration risk range is negatively correlated with the amplitude and duration of slurry temperature fluctuations. The greater the amplitude and duration of slurry temperature fluctuations, the smaller the preset concentration risk range.

[0121] In some embodiments, the microprocessor 170 can update the preset concentration risk range based on the standard concentration risk range, the fluctuation amplitude coefficient, and the fluctuation duration coefficient. The standard concentration risk range is the standard slurry concentration range in which the sizing rate remains stable. In some embodiments, the standard concentration risk range can be determined based on the slurry concentration range in which the sizing rate maintains a preset degree of stability during the actual sizing process. The fluctuation amplitude coefficient and the fluctuation duration coefficient are coefficient values ​​quantified according to the magnitude of the fluctuation amplitude and the duration of the fluctuation in slurry temperature.

[0122] In some embodiments, the microprocessor 170 may further determine a preset concentration risk range according to the following formula (2): L = L p ×(A f ×W1+T f ×W2) (2)

[0123] Where L represents the preset concentration risk range, L p Indicates the standard concentration risk range, A f T represents the fluctuation amplitude coefficient. f The coefficient represents the duration of fluctuation, where W1 represents the first weight and W2 represents the second weight. The first and second weights are preset values, and the sum of the first and second weights is 1.

[0124] In some embodiments, the greater the fluctuation amplitude and the longer the fluctuation duration of the slurry temperature, the smaller the fluctuation amplitude coefficient and fluctuation duration coefficient. For example, the fluctuation amplitude coefficient can be expressed as A. f =e -a 'a' represents the fluctuation amplitude of the slurry temperature; the fluctuation duration coefficient can be expressed as T. f =e -t t represents the duration of the temperature fluctuation in the slurry.

[0125] In some embodiments, the first weight is related to the degree of deviation in slurry concentration. The greater the absolute value of the deviation (e.g., the higher the level of deviation), the greater the first weight. In other words, when the deviation in slurry concentration is large, the preset concentration risk range needs to take into account the impact of fluctuations in slurry temperature. In some embodiments, the microprocessor 170 can determine the first weight based on the level of deviation. For example, a lookup table can be used to determine different first weights based on the level of deviation.

[0126] In the embodiments of this specification, the sizing rate is affected by factors such as sizing concentration and sizing temperature. The sizing concentration is determined based on the amplitude and duration of sizing temperature fluctuations to maintain a stable sizing rate, thereby reducing the risk of uneven sizing. By updating the preset concentration risk range of the sizing concentration with sizing temperature, the impact of sizing temperature changes on the sizing rate is considered, ensuring a stable sizing rate and further guaranteeing the sizing quality of the yarn.

[0127] Step 530: In response to the deviation not meeting the preset conditions, adjust the delivery rate of pre-mixed slurry and / or blending liquid.

[0128] In some embodiments, the microprocessor 170 can determine whether the degree of deviation meets a preset condition. The preset condition is a pre-defined condition used to determine the degree of deviation. In some embodiments, the preset condition may include a deviation degree equal to 0 or not exceeding a deviation range. The deviation range may be a pre-defined range for determining the degree of deviation. For example, the deviation range may be set to less than 3%, 5%, 8%, etc.

[0129] The conveying volume is the total amount of fluid transported from the storage unit 130 to the slurry tank 120 through the conveying pipe. The conveying volume can be controlled by a solenoid valve configured on the conveying pipe.

[0130] In some embodiments, the microprocessor 170 can adjust the delivery rate of the pre-mixed slurry and / or the conditioning liquid based on the degree of deviation. For example, if the degree of deviation is positive, the microprocessor 170 can reduce the delivery rate; if the degree of deviation is negative, the microprocessor 170 can increase the delivery rate. The magnitude of the reduction or increase in the delivery rate is positively correlated with the absolute value of the degree of deviation.

[0131] In some embodiments, the microprocessor 170 can determine the adjustment amount of the delivery volume of the pre-mixed slurry and / or the conditioning liquid based on a reference delivery volume and the degree of deviation using a preset algorithm. The reference delivery volume is the total amount of standard fluid that needs to be delivered to the slurry tank 120, calculated using a concentration-related formula.

[0132] In some embodiments, the adjustment amount for conveying the pre-mixed slurry can be determined according to the following formula (3): V1 = V s ×(1-D) (3) Where V1 represents the adjustment amount for conveying the pre-mixed slurry, V s This represents the baseline delivery volume, and D represents the degree of deviation. For example, if the deviation is +10%, the adjustment amount for the pre-mixed slurry delivery is 90% of the baseline delivery volume.

[0133] In some embodiments, the delivery adjustment amount of the preparation solution can be determined according to the following formula (4): V2 = Vs ×(1+D) (4) Where V2 represents the adjustment amount of the prepared solution being delivered, V s This represents the baseline delivery volume, and D represents the degree of deviation. For example, if the deviation is +10%, the adjustment amount for the prepared liquid delivery is 110% of the baseline delivery volume.

[0134] In the embodiments of this specification, when adding pre-mixed slurry and / or conditioning liquid to the slurry tank, the change in the conveying volume will cause the slurry concentration in the slurry tank to fluctuate, thus affecting the slurry coating rate. By determining the degree of deviation, the conveying volume is adjusted to ensure that the slurry concentration fluctuation in the slurry tank is within a certain range, thereby ensuring the uniformity of slurry coating.

[0135] In the embodiments of this specification, the degree of deviation is determined by the slurry concentration fluctuation information, and then it is determined whether to adjust the delivery volume of pre-mixed slurry and / or blending liquid based on the degree of deviation, taking into account the fluctuation of slurry concentration to ensure the stability of the slurry application rate.

[0136] It should be noted that the above description of process 500 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to process 500 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0137] Figure 6 These are exemplary schematic diagrams of the control model shown in some embodiments of this specification. Figure 6 As shown, the regulation model 630 may include an extraction layer 630-1 and a prediction layer 630-2, used to achieve... Figure 4 The paper describes a regulatory model that predicts the timing of regulatory misalignment.

[0138] In some embodiments, extraction layer 630-1 can be used to determine slurry variation characteristics 640. Extraction layer 630-1 can be an LSTM model.

[0139] Slurry change characteristic 640 is a feature related to slurry. Slurry change characteristic 640 can reflect, to some extent, whether the slurry temperature and slurry concentration are abnormal.

[0140] like Figure 6 As shown, the inputs to extraction layer 630-1 may include slurry temperature change information 610 and slurry concentration fluctuation information 620, and the output of extraction layer 630-1 may include slurry change characteristics 640. Slurry change characteristics 640 can be further used to determine the control misalignment time, serving as input to prediction layer 630-2. More information on slurry temperature change information 610 and slurry concentration fluctuation information 620 can be found in [link to relevant documentation]. Figure 4The details and related descriptions will not be repeated here.

[0141] In some embodiments, the prediction layer 630-2 can be used to determine whether the predicted change sequence of sizing rate is within a preset normal range of sizing rate 690. The prediction layer 630-2 can be an NN model.

[0142] like Figure 6 As shown, the inputs to the prediction layer 630-2 may include multiple sets of candidate control misalignment times 650, the delivery rate of pre-mixed slurry and / or blending liquid 660, the storage temperature 670, the change information of sizing rate 680, and the slurry change characteristics 640. The output of the prediction layer 630-2 may include whether the predicted change sequence of sizing rate is within the preset normal range of sizing rate 690. More information regarding multiple sets of candidate control misalignment times 650, the delivery rate of pre-mixed slurry and / or blending liquid 660, the storage temperature 670, and the change information of sizing rate 680 can be found in [reference needed]. Figure 4 The details and related descriptions will not be repeated here.

[0143] In some embodiments, the output of extraction layer 630-1 can be used as the input of prediction layer 630-2, therefore, extraction layer 630-1 and prediction layer 630-2 in the control model 630 can be jointly trained. The training samples and training labels of the control model 630 can be found in [reference needed]. Figure 4 And its related descriptions.

[0144] In some embodiments, the joint training of the control model 630 may include inputting sample slurry temperature change and sample slurry concentration fluctuation information into the extraction layer 630-1 to obtain the sample slurry change features output by the extraction layer 630-1; using the sample slurry change features as training sample data, along with sample slurry rate change, sample storage temperature, sample candidate control misalignment time, and sample delivery volume, into the prediction layer 630-2 to determine whether the predicted slurry rate change sequence output by the prediction layer 630-2 is within the preset normal range of slurry rate. A loss function is constructed based on the sample slurry temperature change, sample slurry concentration fluctuation information, and sample slurry change features output by the extraction layer 630-1, and the parameters of the extraction layer 630-1 and the prediction layer 630-2 are updated synchronously. Through parameter updates, the trained extraction layer 630-1 and prediction layer 630-2 are obtained.

[0145] In the embodiments of this specification, by controlling the extraction layer and the prediction layer in the control model, the changes in slurry temperature and concentration during the sizing process can be comprehensively considered. Combined with relevant information such as changes in sizing rate, storage temperature, and delivery rate, it can be determined whether the predicted change sequence of the sizing rate is within the preset normal range for the sizing rate, thereby predicting the control misalignment time relatively quickly and accurately. Furthermore, jointly training the extraction layer and the prediction layer in the control model can obtain a control model with higher accuracy and better training efficiency. Simultaneously, it can solve the problem of difficulty in obtaining labels during individual training.

[0146] This specification also provides a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the sizing machine sizing control method as described in any of the above embodiments.

[0147] The basic concepts have been described above. Obviously, for those skilled in the art, the detailed disclosure above is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements, and corrections to this specification. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0148] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0149] Furthermore, unless expressly stated in the claims, the order of processing elements and sequences, the use of numbers and letters, or other names described in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on existing servers or mobile devices.

[0150] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0151] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples with the terms "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, numerical parameters should take into account specified significant digits and employ a general method of digit reservation. Although the numerical ranges and parameters used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such values ​​are set as precisely as feasible.

[0152] For each patent, patent application, patent application publication, and other material, such as articles, books, specifications, publications, and documents, referenced in this specification, the entire contents of which are incorporated herein by reference. This excludes historical application documents that are inconsistent with or conflict with the content of this specification, as well as documents that limit the broadest scope of the claims in this specification (currently or subsequently appended to this specification). It should be noted that in the event of any inconsistency or conflict between the descriptions, definitions, and / or terminology used in the supplementary materials to this specification and the content of this specification, the descriptions, definitions, and / or terminology used in this specification shall prevail.

[0153] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A sizing device for a sizing machine, characterized in that, include: The system includes multiple sets of drive rollers, a slurry tank, storage components, a slurry concentration monitoring device, a slurry temperature monitoring device, a gravity monitoring device, a temperature control assembly, and a microprocessor. The multiple sets of drive rollers are respectively configured to transport the yarn to be sized from the first yarn spool to the sizing tank, and to sequentially transfer the sized yarn to the sizing device, the drying device, and the second yarn spool; The sizing tank is configured to store the sizing liquid required for yarn sizing, and the sizing tank includes a stirring component that operates at different stirring powers based on the control of the microprocessor. The storage component is mechanically connected to the slurry tank via a conveying pipe. The conveying pipe is equipped with a solenoid valve. The storage component is configured to convey at least one of pre-mixed slurry and conditioning liquid to adjust the slurry concentration in the slurry tank. The slurry concentration monitoring device and the slurry temperature monitoring device are installed on the inner wall of the slurry tank and are configured to monitor the slurry concentration and slurry temperature in the slurry tank. The gravity monitoring device is installed in the first yarn bobbin and the second yarn bobbin and is configured to monitor the weight of the yarn; The temperature control component is mechanically connected to the slurry temperature monitoring device and is used to control the slurry temperature in the slurry tank. The microprocessor is communicatively connected to the storage component, the slurry concentration monitoring device, the slurry temperature monitoring device, the gravity monitoring device, and the temperature control component, respectively. The microprocessor is configured to: Based on the yarn weights of the first and second yarn spools, the sizing rate and the estimated variation in sizing rate are evaluated; and Based on the sizing rate and the estimated variation of the sizing rate, the solenoid valve is controlled to control the delivery amount of the pre-mixed slurry and / or the blending liquid; Based on the slurry temperature, determine the slurry temperature change information; The storage temperature of the storage component is determined based on the ambient temperature; Based on the slurry temperature change information, the slurry application rate, the storage temperature, and the slurry concentration, the adjustment misalignment time of the temperature control component is controlled; the adjustment misalignment time is the time difference between advancing or delaying the temperature control; wherein, based on the slurry temperature change information, the slurry application rate change information, and the fluctuation information of the storage temperature and slurry concentration, the adjustment misalignment time is estimated by a control model, and the control model is a machine learning model; The microprocessor also determines whether there is a temperature unevenness problem based on the slurry temperature change information; in response to the existence of a temperature unevenness problem, the microprocessor determines the updated stirring power and / or local heating intensity of the stirring component based on the duration of historical temperature unevenness.

2. The apparatus according to claim 1, characterized in that, The storage component further includes a flow meter configured to acquire flow data of the pre-mixed slurry and / or the blending solution, and the microprocessor is further configured to: Based on the slurry concentration at multiple consecutive time points, determine the slurry concentration fluctuation information; Determine the degree of deviation between the flow rate data and the slurry concentration fluctuation information; In response to the deviation not meeting the preset conditions, the delivery rate of the pre-mixed slurry and / or the blending liquid is adjusted.

3. The apparatus according to claim 2, characterized in that, The microprocessor is also configured to: The electromagnetic valve controls the conveying speed of at least one of the pre-mixed slurry and the blending liquid, wherein the conveying speed is determined based on slurry temperature change information and deviation degree.

4. A sizing machine control system, implemented based on a microprocessor of the sizing device for a sizing machine as described in claim 1, the system comprising: The evaluation module is configured to evaluate the sizing rate and the estimated variation of the sizing rate based on the yarn weight of the first yarn spool and the second yarn spool. as well as The control module is configured to control the solenoid valve to control the delivery amount of the pre-mixed slurry and / or the blending liquid based on the sizing rate and the estimated variation range of the sizing rate. Based on the slurry temperature, determine the slurry temperature change information; The storage temperature of the storage component is determined based on the ambient temperature; Based on the slurry temperature change information, the slurry application rate, the storage temperature, and the slurry concentration, the adjustment misalignment time of the temperature control component is controlled; the adjustment misalignment time is the time difference between advancing or delaying the temperature control; wherein, based on the slurry temperature change information, the slurry application rate change information, and the fluctuation information of the storage temperature and slurry concentration, the adjustment misalignment time is estimated by a control model, and the control model is a machine learning model; The microprocessor also determines whether there is a temperature unevenness problem based on the slurry temperature change information; in response to the existence of a temperature unevenness problem, the microprocessor determines the updated stirring power and / or local heating intensity of the stirring component based on the duration of historical temperature unevenness.

5. A sizing control method for a sizing machine, implemented using a microprocessor based on the sizing device for a sizing machine as described in claim 1; the method includes: The sizing rate and the estimated variation of the sizing rate are assessed based on the yarn weight of the first yarn bobbin and the second yarn bobbin. as well as The solenoid valve is controlled based on the sizing rate and the estimated variation of the sizing rate to control the delivery amount of the pre-mixed slurry and / or the blending liquid. Based on the slurry temperature, determine the slurry temperature change information; The storage temperature of the storage component is determined based on the ambient temperature; Based on the slurry temperature change information, the slurry application rate, the storage temperature, and the slurry concentration, the adjustment misalignment time of the temperature control component is controlled; the adjustment misalignment time is the time difference between advancing or delaying the temperature control; wherein, based on the slurry temperature change information, the slurry application rate change information, and the fluctuation information of the storage temperature and slurry concentration, the adjustment misalignment time is estimated by a control model, and the control model is a machine learning model; The microprocessor also determines whether there is a temperature unevenness problem based on the slurry temperature change information; in response to the existence of a temperature unevenness problem, the microprocessor determines the updated stirring power and / or local heating intensity of the stirring component based on the duration of historical temperature unevenness.

6. The method according to claim 5, characterized in that, The storage component further includes a flow meter configured to acquire flow data of the pre-mixed slurry and / or the blending liquid, respectively, and the method further includes: Based on the slurry concentration at multiple consecutive time points, determine the slurry concentration fluctuation information; Determine the degree of deviation between the flow rate data and the slurry concentration fluctuation information; In response to the deviation not meeting the preset conditions, the delivery rate of the pre-mixed slurry and / or the blending liquid is adjusted.

7. The method according to claim 6, characterized in that, The method further includes: The electromagnetic valve controls the conveying speed of at least one of the pre-mixed slurry and the blending liquid, wherein the conveying speed is determined based on slurry temperature change information and deviation degree.

8. A computer-readable storage medium storing computer instructions, wherein when a computer reads the computer instructions in the storage medium, the computer executes the sizing control method for a sizing machine as described in any one of claims 5 to 7.

Images