Intelligent doffing method and system for polyester filament yarn production

Abstract

The invention relates to an intelligent doffing method and system for polyester filament yarn production, and relates to the technical field of textile production, and the method comprises the following steps: obtaining yarn cylinder monitoring parameters of each yarn cylinder; determining predicted operation duration according to the wire cylinder monitoring parameters, and defining to-be-selected wire cylinders and predicted to-be-selected duration according to the predicted operation duration; determining a to-be-selected winding position according to the to-be-selected yarn cylinder, and determining doffing processing duration according to the to-be-selected winding position; according to the to-be-selected yarn cylinders, random sequential sorting is carried out to determine a simulated doffing sequence, and according to the to-be-selected winding positions, adjacent moving distances are determined; determining the moving duration of the gripper according to the adjacent moving distance and the moving speed of the gripper, and determining the overall simulation duration according to the simulated doffing sequence, the doffing processing duration, the moving duration of the gripper and the predicted to-be-selected duration; and defining an effective doffing sequence according to the overall simulation duration with the minimum numerical value, and performing doffing processing according to the effective doffing sequence. The device has the effect of improving the overall production efficiency of polyester filament yarn production.

Description

Technical Field

[0001] The present application relates to the field of textile production technology, and in particular to an intelligent bobbin dropping method and system for polyester filament production. Background Art

[0002] In the process of polyester filament production, "bobbin dropping" refers to the process of removing the fully wound bobbin from the winding position on the spinning machine and replacing it with an empty bobbin tube to continue production. Currently, in order to improve production efficiency and product quality, the use of intelligent technology has been introduced.

[0003] Currently, information such as the bobbin diameter and weight is monitored in real time through high-precision sensors to determine whether the bobbin has been fully wound. When the bobbin is fully wound, the automatic bobbin dropping machine moves to the corresponding winding position to remove the cake and place it on the temporary storage rack, and continues to use the automatic bobbin dropping machine to place an empty bobbin tube at the position where the bobbin was removed to continue the winding operation. In order to reduce operating costs, generally multiple winding positions correspond to one automatic bobbin dropping machine as a whole. During production, when it is detected that the bobbin winding is completed, a corresponding instruction is output to control the automatic bobbin dropping machine to move to the corresponding position for operation. If there are other bobbins that complete the operation during the operation of the automatic bobbin dropping machine, they will wait in sequence according to the completion time for the automatic bobbin dropping machine to perform the bobbin dropping operation.

[0004] In the above related technology, although the bobbin dropping operation can be achieved, since the operation sequence of the automatic bobbin dropping machine is arranged according to the bobbin winding completion sequence, when there is a concentrated dropping of thick filaments, there may be some time wasted in the moving process of the automatic bobbin dropping machine between the temporary storage rack and the winding position, resulting in some bobbins needing to wait for a relatively long time, thereby affecting the overall production efficiency of polyester filament production, and there is still room for improvement. Summary of the Invention

[0005] In order to improve the overall production efficiency of polyester filament production, the present application provides an intelligent bobbin dropping method and system for polyester filament production.

[0006] In a first aspect, the present application provides an intelligent bobbin dropping method for polyester filament production, adopting the following technical solution: An intelligent bobbin dropping method for polyester filament production includes: Obtaining the bobbin monitoring parameters of each bobbin; Inputting the bobbin monitoring parameters into a preset operation prediction model to determine the predicted operation duration, defining the bobbin with a predicted operation duration less than the preset reference analysis duration as a candidate bobbin, and defining the predicted operation duration corresponding to the candidate bobbin as the predicted candidate duration; Determining the candidate winding positions according to the candidate bobbins, and determining the bobbin dropping processing duration corresponding to the candidate winding positions according to a preset processing matching relationship; Randomly sort the candidate spools in sequence to determine the simulated spool - dropping sequence, and determine the adjacent moving distance according to the candidate winding positions of adjacent candidate spools in the simulated spool - dropping sequence; Calculate according to the adjacent moving distance and the preset moving speed of the gripper to determine the gripper moving duration, and calculate according to the simulated spool - dropping sequence, the spool - dropping processing duration, the gripper moving duration, and the predicted candidate duration to determine the overall simulation duration; Determine the overall simulation duration with the smallest value according to the preset sorting rule, define the simulated spool - dropping sequence corresponding to this overall simulation duration as the effective spool - dropping sequence, and perform spool - dropping processing on each candidate spool according to the effective spool - dropping sequence.

[0007] Optionally, after the candidate spools are determined, the intelligent spool - dropping method for polyester filament production further includes: Obtain the number of available grippers; Judge whether the number of available grippers is one; If the number of available grippers is one, randomly sort the candidate spools in sequence to determine the simulated spool - dropping sequence; If the number of available grippers is not one, construct an equal number of initially empty induction sets according to the number of available grippers, randomly distribute each candidate spool into an induction set, and generate an overall distribution plan according to each induction set after all candidate spools are allocated; Sort the candidate spools in sequence in each induction set to determine the local simulation sequence, and determine the local simulation duration according to the local simulation sequence; Analyze according to the local simulation sequence and the local simulation duration in each induction set under each overall distribution plan to determine the rationality value; Define the overall distribution plan corresponding to the rationality value with the largest value as the effective distribution plan, define the local simulation sequence corresponding to the rationality value with the largest value under the effective distribution plan as the local effective sequence, and perform spool - dropping processing on each corresponding candidate spool according to each local effective sequence.

[0008] Optionally, the step of analyzing according to the local simulation sequence and the local simulation duration in each induction set under each overall distribution plan to determine the rationality value includes: Determine the local simulation duration with the largest value among all local simulation durations and define it as the completion requirement duration; Calculate according to the spool - dropping processing duration and the gripper moving duration of each candidate spool in each local simulation sequence to determine the theoretical processing duration; Calculate according to the theoretical processing duration and the predicted candidate duration to determine the monomer lag duration, and perform a summation calculation according to each monomer lag duration to determine the overall lag duration; Calculate according to the completion requirement duration, the overall lag duration, and the preset fixed calculation parameters to determine the rationality value.

[0009] Optionally, after the rationality value is determined, the intelligent bobbin dropping method for polyester filament production further includes: Determine the type lag duration corresponding to the bobbin monitoring parameter according to the preset type matching relationship; Judge whether there is a situation where the monomer lag duration of each candidate bobbin corresponding to the current rationality value is greater than the corresponding type lag duration; If there is no situation where the monomer lag duration of each candidate bobbin corresponding to the current rationality value is greater than the corresponding type lag duration, then maintain the determined rationality value; If there is a situation where the monomer lag duration of each candidate bobbin corresponding to the current rationality value is greater than the corresponding type lag duration, then calculate the difference between the current rationality value and the preset correction value to update the rationality value.

[0010] Optionally, it further includes the step of determining the correction value, and this step includes: Judge whether there is at least one situation where the monomer lag duration of a candidate bobbin is greater than the corresponding type lag duration in all overall allocation schemes; If there is no situation where at least one candidate bobbin's monomer lag duration is greater than the corresponding type lag duration in all overall allocation schemes, then determine the preset fixed value as the correction value; If there is at least one situation where the monomer lag duration of a candidate bobbin is greater than the corresponding type lag duration in all overall allocation schemes, then define the candidate bobbin with the monomer lag duration greater than the corresponding type lag duration as a lag bobbin; Calculate according to the monomer lag duration of the lag bobbin and the corresponding type lag duration to determine the excess lag ratio; Determine the lag impact coefficient corresponding to the bobbin monitoring parameter of the lag bobbin according to the preset impact matching relationship; Calculate according to all excess lag ratios and the corresponding lag impact coefficients to determine the overall impact parameter; Determine the correction value corresponding to the overall impact parameter according to the preset correction matching relationship.

[0011] Optionally, after the overall simulation duration is determined, the intelligent bobbin dropping method for polyester filament production further includes: Judge whether there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration; If there are no at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, then define the effective bobbin dropping sequence according to the minimum overall simulation duration; If there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, the bobbin dropping sequence corresponding to the minimum overall simulation duration is defined as the alternative bobbin dropping sequence; Among the alternative bobbin dropping sequences, the lagging bobbin is determined according to each bobbin to be selected, and the difference is calculated based on the individual lagging duration and the corresponding type lagging duration of the lagging bobbin to determine the excess lagging duration; The overall delay duration is determined by summing up all the excess lagging durations; According to the sorting rule, the overall delay duration with the minimum value is determined, and the alternative bobbin dropping sequence corresponding to the overall delay duration is defined as the effective bobbin dropping sequence.

[0012] Optionally, after the bobbin dropping process for each bobbin to be selected, the intelligent bobbin dropping method for polyester filament production further includes: Obtaining the actual operation duration and the theoretical operation duration; Calculating according to the actual operation duration and the theoretical operation duration to determine the duration deviation coefficient; According to the preset evaluation matching relationship, the operation evaluation value corresponding to the duration deviation coefficient is determined, and the operation evaluation value is output to the preset management terminal.

[0013] In a second aspect, the present application provides an intelligent bobbin dropping system for polyester filament production, adopting the following technical solutions: An intelligent bobbin dropping system for polyester filament production includes: An acquisition module, configured to acquire the bobbin monitoring parameters of each bobbin; A processing module, connected to the acquisition module, for storing and processing information; The processing module inputs the bobbin monitoring parameters into a preset operation prediction model to determine the predicted operation duration, defines the bobbin with the predicted operation duration less than the preset reference analysis duration as the bobbin to be selected, and defines the predicted operation duration corresponding to the bobbin to be selected as the predicted selection duration; The processing module determines the candidate winding positions according to the bobbins to be selected, and determines the bobbin dropping processing duration corresponding to the candidate winding positions according to the preset processing matching relationship; The processing module randomly sorts the bobbins to be selected in sequence to determine the simulation bobbin dropping sequence, and determines the adjacent moving distance according to the candidate winding positions of the adjacent bobbins to be selected in the simulation bobbin dropping sequence; The processing module calculates according to the adjacent moving distance and the preset gripper moving speed to determine the gripper moving duration, and calculates according to the simulation bobbin dropping sequence, the bobbin dropping processing duration, the gripper moving duration, and the predicted selection duration to determine the overall simulation duration; The processing module determines the overall simulation duration with the smallest value according to a preset sorting rule, defines the simulation bobbin dropping order corresponding to the overall simulation duration as the effective bobbin dropping order, and performs bobbin dropping processing on each candidate bobbin according to the effective bobbin dropping order.

[0014] In summary, the present application includes at least one of the following beneficial technical effects: When performing bobbin winding operations, it is possible to predict the winding completion time of each bobbin and reasonably plan the bobbin dropping operations for each bobbin, thereby shortening the overall bobbin dropping duration required for the same batch of products and improving the overall production efficiency of polyester filament production; It is possible to analyze the allowable lag time for each type of bobbin to determine a reasonable bobbin dropping plan for use. Description of the Drawings

[0015] Figure 1 is a flowchart of an intelligent bobbin dropping method for polyester filament production.

[0016] Figure 2 is a module flowchart of an intelligent bobbin dropping method for polyester filament production. Detailed Embodiments

[0017] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the following is combined with Figure 1 - Figure 2 and embodiments to further elaborate on the present application. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0018] The following further describes the embodiments of the present application in detail with reference to the accompanying drawings of the specification.

[0019] The embodiments of the present application disclose an intelligent bobbin dropping method for polyester filament production. Referring to Figure 1 , the method flow of the intelligent bobbin dropping method for polyester filament production includes the following steps: Step S100: Obtain the bobbin monitoring parameters of each bobbin.

[0020] The bobbin monitoring parameters are parameter values obtained after monitoring the bobbin, such as the bobbin winding diameter, the bobbin winding weight, the bobbin winding type, etc. Data can be obtained through various types of sensors. For example, for the winding diameter, it can be obtained through a distance sensor.

[0021] Step S101: Input the bobbin monitoring parameters into a preset operation prediction model to determine the predicted operation duration, define the bobbin with a predicted operation duration less than the preset reference analysis duration as a candidate bobbin, and define the predicted operation duration corresponding to the candidate bobbin as the predicted candidate duration.

[0022] The operation prediction model is a model pre-constructed for staff to analyze and predict the winding situation of the bobbin. Through this operation prediction model, it can be obtained when the bobbin can be fully wound under the current situation. This model can be pre-constructed through deep learning. When the bobbin monitoring parameters are input into the operation prediction model, the specific winding situation of the current bobbin can be known, so that the predicted operation duration can be obtained. The predicted operation duration is the duration still required when the bobbin is fully wound; the benchmark analysis duration is the maximum predicted operation duration set by the staff when the bobbin-changing analysis needs to be carried out. The candidate bobbin is defined to distinguish the bobbins that need to be bobbin-changed in a short time, and at the same time, the predicted candidate duration is defined to distinguish the corresponding predicted operation duration, which is convenient for subsequent analysis.

[0023] Step S102: Determine the candidate winding position according to the candidate bobbin, and determine the bobbin-changing processing duration corresponding to the candidate winding position according to the preset processing matching relationship.

[0024] The candidate winding position is the position point where the candidate bobbin is located. The bobbin-changing processing duration is the duration required for winding the bobbin at the candidate winding position. This duration includes the total duration required for the automatic bobbin-changer to move the fully wound bobbin from the candidate winding position to the placement point and then pick up an empty bobbin and place it at the candidate winding position. Different candidate winding positions correspond to different placement points, so the corresponding bobbin-changing processing durations are also different. The processing matching relationship between the two is determined by the staff in advance and will not be elaborated here.

[0025] Step S103: Randomly sort the candidate bobbins in sequence to determine the simulated bobbin-changing order, and determine the adjacent moving distance according to the candidate winding positions of adjacent candidate bobbins in the simulated bobbin-changing order.

[0026] The simulated bobbin-changing order is the simulated order for bobbin-changing of the bobbins obtained by randomly sorting the candidate bobbins. The adjacent moving distance is the distance value that the automatic bobbin-changer needs to move from the previous candidate winding position to the next candidate winding position in the simulated bobbin-changing order, that is, the distance value that the automatic bobbin-changer needs to move to the next candidate winding position after placing the empty bobbin at the previous candidate winding position for bobbin-changing operation.

[0027] Step S104: Calculate according to the adjacent moving distance and the preset gripper moving speed to determine the gripper moving duration, and calculate according to the simulated bobbin-changing order, the bobbin-changing processing duration, the gripper moving duration, and the predicted candidate duration to determine the overall simulated duration.

[0028] The gripper moving speed is the speed value that the automatic bobbin discharging machine can reach when moving between different positions. The gripper moving duration is the duration required for the gripper to switch the selected winding positions, which is determined by dividing the adjacent moving distance by the gripper moving speed. The overall simulation duration is the duration value required after processing all the selected bobbins according to the simulated bobbin discharging sequence. By predicting the selected duration, the earliest duration when each selected bobbin can be processed can be known. Then, based on the bobbin discharging processing duration and the gripper moving duration, the duration for the gripper to move to each bobbin can be known, so as to determine whether it is necessary to wait for the bobbins that have not completed the winding operation, and further analyze the specific time points for each selected bobbin to perform bobbin discharging under theoretical conditions, so that the overall simulation duration can be determined.

[0029] Step S105: Determine the overall simulation duration with the smallest value according to the preset sorting rule, define the simulation bobbin discharging sequence corresponding to this overall simulation duration as the effective bobbin discharging sequence, and perform bobbin discharging processing on each selected bobbin according to the effective bobbin discharging sequence.

[0030] The sorting rule is a method set by the staff to sort the numerical values, such as the bubble sort method. Through the sorting rule, the overall simulation duration with the smallest value can be determined, that is, under the corresponding simulation bobbin discharging sequence at this time, the bobbin discharging of this batch of bobbins can be completed in the least time. Therefore, it is defined as the effective bobbin discharging sequence and the bobbin discharging operation is performed on each selected bobbin according to this sequence to improve the overall production efficiency. Among them, the automatic bobbin discharging machine communicates with the winding machine in real time through 5G wireless communication. The full-winding position number sends information to the Master main cabinet through Profibus-DP communication. The Master main cabinet gives the winding position number, and the bobbin discharging machine automatically runs to the full-winding position number to automatically drop the silk. The batch number and data are automatically uploaded to the bobbin discharging machine, and the uploaded data includes operation time, operation task, batch number traceability, fault information, maintenance information, shutdown information, etc.

[0031] After the selected bobbins are determined, the intelligent bobbin discharging method for polyester filament production further includes: Step S200: Obtain the number of available grippers.

[0032] The number of available grippers is the number of automatic bobbin discharging machines that are currently in the idle state and can be used to grab and process the subsequent selected bobbins. The gripper status can be determined by analyzing the task library of each automatic bobbin discharging machine. After the automatic bobbin discharging machine completes the bobbin discharging operation on all the selected bobbins it has chosen, it returns to the idle state, and after all the selected bobbins in the same batch have completed the bobbin discharging operation, the analysis of the selected bobbins is performed again.

[0033] Step S201: Determine whether the number of available grippers is one.

[0034] The purpose of the determination is to know whether only one gripper can be used.

[0035] Step S2011: If the number of available grippers is one, randomly sort the candidate spools in sequence to determine the simulated spool dropping order.

[0036] When the number of available grippers is one, it means that only one gripper is available, that is, each candidate spool can only be dropped on one gripper. At this time, the analysis of the simulated spool dropping order can be carried out normally.

[0037] Step S2012: If the number of available grippers is not one, construct an equal number of initially empty inductive sets according to the number of available grippers, randomly assign each candidate spool to an inductive set, and generate an overall allocation plan according to each inductive set after all candidate spools are allocated.

[0038] When the number of available grippers is not one, it means that there are multiple grippers that can perform the spool dropping operation. Therefore, it is necessary to allocate the spools that each gripper needs to operate; construct an inductive set and randomly allocate each candidate spool to construct an overall allocation plan that each gripper can operate, which is convenient for subsequent analysis.

[0039] Step S202: Sort the candidate spools in sequence in each inductive set to determine the local simulation order, and determine the local simulation duration according to the local simulation order.

[0040] The local simulation order is the simulation order for dropping the candidate spools in the inductive set. The local simulation duration is the duration value required for the automatic spool dropping machine to process each candidate spool according to the local simulation order and complete the processing. The determination method is the same as the above overall simulation duration and will not be elaborated here.

[0041] Step S203: Analyze according to the local simulation order and local simulation duration in each inductive set under each overall allocation plan to determine the rationality value.

[0042] The rationality value is a parameter reflecting the rationality of the current overall allocation plan. The larger this value is, the more reasonable the determined overall allocation plan is, that is, the more conducive it is to improving the overall production efficiency. The specific determination method refers to Step S300 - Step S303.

[0043] Step S204: Define the overall allocation plan corresponding to the largest rationality value as the effective allocation plan, define the local simulation order corresponding to the largest rationality value under the effective allocation plan as the local effective order, and perform spool dropping processing on each corresponding candidate spool according to each local effective order.

[0044] Define an effective allocation plan to determine the most reasonable overall allocation plan. At this time, define a local effective order to distinguish the bobbin dropping order that each gripper needs to execute, so as to facilitate subsequent control of each gripper to perform bobbin dropping operations on the candidate bobbins, thereby meeting the production efficiency requirements.

[0045] The steps of analyzing according to the local simulation order and local simulation duration in each induction set under each overall allocation plan to determine the rationality degree value include: Step S300: Determine the local simulation duration with the largest value among all local simulation durations and define it as the completion requirement duration.

[0046] Define the completion requirement duration to distinguish the local simulation duration with the largest value in the overall allocation plan. This duration is also the duration value required when processing all candidate bobbins.

[0047] Step S301: Calculate according to the bobbin dropping processing duration and gripper movement duration of each candidate bobbin in each local simulation order to determine the theoretical processing duration.

[0048] The theoretical processing duration is the duration value between the start time point and the current time point when processing a single candidate bobbin according to the order under theoretical circumstances.

[0049] Step S302: Calculate according to the theoretical processing duration and the predicted candidate duration to determine the monomer lag duration, and sum up each monomer lag duration to determine the overall lag duration.

[0050] The monomer lag duration is the duration value required from when the bobbin is full to when a gripper performs bobbin dropping processing on it, which is determined by subtracting the predicted candidate duration from the theoretical processing duration. When this value is negative, it is set to zero; the overall lag duration is the sum of all monomer lag durations.

[0051] Step S303: Calculate according to the completion requirement duration, the overall lag duration, and the preset fixed calculation parameters to determine the rationality degree value.

[0052] The fixed calculation parameter is the parameter value used for calculation. The calculation formula for the rationality degree value is , where is the completion requirement duration, is the overall lag duration, is the rationality degree value, , and are all preset fixed calculation parameters used for calculation.

[0053] After the rationality degree value is determined, the intelligent bobbin dropping method for polyester filament production further includes: Step S400: Determine the type lag duration corresponding to the spool monitoring parameter according to the preset type matching relationship.

[0054] The type lag duration is the maximum single lag duration allowed for the type of the wire on the currently wound spool. This duration can be determined by the degree of need for the wire in subsequent processes, and the type matching relationship between the two can be determined by the staff input.

[0055] Step S401: Determine whether there is a situation where the single lag duration of each candidate spool corresponding to the current reasonable degree value is greater than the corresponding type lag duration.

[0056] The purpose of the determination is to find out whether the bobbin dropping operations of each spool are all relatively timely.

[0057] Step S4011: If there is no situation where the single lag duration of each candidate spool corresponding to the current reasonable degree value is greater than the corresponding type lag duration, then maintain the currently determined reasonable degree value.

[0058] When there is no situation where the single lag duration of each candidate spool corresponding to the current reasonable degree value is greater than the corresponding type lag duration, it indicates that the bobbin dropping operations of each spool are all relatively timely. At this time, maintaining the current reasonable degree value can be used to analyze the current processing plan.

[0059] Step S4012: If there is a situation where the single lag duration of each candidate spool corresponding to the current reasonable degree value is greater than the corresponding type lag duration, then calculate the difference between the current reasonable degree value and the preset correction degree value to update the reasonable degree value.

[0060] When there is a situation where the single lag duration of each candidate spool corresponding to the current reasonable degree value is greater than the corresponding type lag duration, it indicates that there is at least one spool with an untimely bobbin dropping operation, that is, the rationality of the plan is relatively poor. At this time, subtracting the correction degree value from the current reasonable degree value can update the reasonable degree value. The correction degree value can be a fixed value set by the staff or a variable value adjusted according to the actual situation. Specifically, refer to Step S500 - Step S504.

[0061] It also includes the determination step of the correction degree value, and this step includes: Step S500: Determine whether there is a situation where the single lag duration of at least one candidate spool in all overall allocation plans is greater than the corresponding type lag duration.

[0062] The purpose of the determination is to find out whether there is a plan that can avoid the situation of delayed bobbin dropping operations for each spool.

[0063] Step S5001: If there is no case where the monomer lag duration of at least one candidate spool is greater than the corresponding type lag duration in all overall allocation schemes, then determine the preset fixed degree value as the correction degree value.

[0064] When there is no case where the monomer lag duration of at least one candidate spool is greater than the corresponding type lag duration in all overall allocation schemes, it indicates that there is a scheme that enables each candidate spool not to be delayed. At this time, it is only necessary to use the fixed degree value as the correction degree value to correct the reasonable degree value of the inappropriate scheme. The fixed degree value is a fixed value set by the staff.

[0065] Step S5002: If there is at least one candidate spool whose monomer lag duration is greater than the corresponding type lag duration in all overall allocation schemes, then define the candidate spool with the monomer lag duration greater than the corresponding type lag duration as a lag spool.

[0066] When there is at least one candidate spool whose monomer lag duration is greater than the corresponding type lag duration in all overall allocation schemes, it means that the condition that there is no spool delayed in dropping tubes cannot be met in all schemes. Therefore, if the correction degree values are the same, it is meaningless to set the correction degree value. Therefore, further analysis is required; defining the lag spool is to distinguish the candidate spools that will be delayed, which is convenient for subsequent analysis.

[0067] Step S501: Calculate according to the monomer lag duration of the lag spool and the corresponding type lag duration to determine the excess lag ratio.

[0068] The excess lag ratio is the ratio obtained by subtracting the type lag duration from the monomer lag duration and then dividing by the type lag duration.

[0069] Step S502: Determine the lag impact coefficient corresponding to the spool monitoring parameter of the lag spool according to the preset impact matching relationship.

[0070] The lag impact coefficient is the degree of impact on subsequent processes when the type corresponding to the lag spool has a lag situation. The larger this value is, the greater the impact of the lag situation. Different spool monitoring parameters correspond to different wire types, so the corresponding lag impact coefficients are also different. The impact matching relationship between the two is pre-entered and stored by the staff.

[0071] Step S503: Calculate according to all excess lag ratios and the corresponding lag impact coefficients to determine the overall impact parameter.

[0072] The overall impact parameter is the sum of the values obtained by multiplying all excess lag ratios by the corresponding lag impact coefficients.

[0073] Step S504: Determine the correction degree value corresponding to the overall influence parameter according to the preset correction matching relationship.

[0074] In different overall influence parameter description schemes, the influence degree brought by hysteresis is different, so the corresponding correction degree values are also different. The correction matching relationship between the two is determined by the staff, and it is necessary to ensure that the larger the overall influence parameter, the larger the corresponding correction degree value.

[0075] After determining the overall simulation duration, the intelligent bobbin dropping method for polyester filament production further includes: Step S600: Determine whether there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration.

[0076] The purpose of the determination is to find out whether there are multiple simulation bobbin dropping sequences that meet the requirements, so as to determine the effective bobbin dropping sequence for use.

[0077] Step S6001: If there are not at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, define the effective bobbin dropping sequence according to the overall simulation duration with the minimum value.

[0078] When there are not at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, it means that there is only one simulation bobbin dropping sequence that meets the requirements. At this time, it can be defined as the effective bobbin dropping sequence.

[0079] Step S6002: If there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, define the simulation bobbin dropping sequence corresponding to the overall simulation duration with the minimum value as the alternative bobbin dropping sequence.

[0080] When there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, it means that there are multiple simulation bobbin dropping sequences that meet the requirements. At this time, they are defined as alternative bobbin dropping sequences for distinction, which is convenient for subsequent analysis.

[0081] Step S601: Determine the lagging bobbin among the alternative bobbin dropping sequences according to each bobbin to be selected, and calculate the difference between the single lagging duration of the lagging bobbin and the corresponding type lagging duration to determine the excess lagging duration.

[0082] The excess lagging duration is the duration value of the additional delay based on the permitted delay, and is determined by subtracting the corresponding type lagging duration from the single lagging duration.

[0083] Step S602: Calculate the sum of all the excess lagging durations to determine the overall delay duration.

[0084] The overall delay duration is the sum of all the excess lagging durations.

[0085] Step S603: Determine the overall delay duration with the smallest value according to the sorting rule, and define the alternative bobbin-changing order corresponding to this overall delay duration as the effective bobbin-changing order.

[0086] Through the sorting rule, the overall delay duration with the smallest value can be determined, that is, the delay duration that occurs under the corresponding alternative bobbin-changing order at this time is the shortest. At this time, it can be defined as the effective bobbin-changing order.

[0087] After the bobbin-changing process for each candidate bobbin, the intelligent bobbin-changing method for polyester filament production further includes: Step S700: Obtain the actual operation duration and the theoretical operation duration.

[0088] The actual operation duration is the duration value consumed for completely performing the bobbin-changing operation on this batch of bobbins, and the theoretical operation duration is the duration value required for completely performing the bobbin-changing operation on this batch of bobbins obtained through theoretical analysis.

[0089] Step S701: Calculate according to the actual operation duration and the theoretical operation duration to determine the duration deviation coefficient.

[0090] The duration deviation coefficient is a coefficient value reflecting the prediction accuracy. The larger this value, the less accurate the prediction. Calculate the difference between the actual operation duration and the theoretical operation duration and take the absolute value to obtain the difference between the two, and then divide this difference by the actual operation duration to obtain the duration deviation coefficient.

[0091] Step S702: Determine the operation evaluation value corresponding to the duration deviation coefficient according to the preset evaluation matching relationship, and output this operation evaluation value to the preset management terminal.

[0092] The operation evaluation value is a parameter value for evaluating the prediction situation. The larger this value, the more accurate the prediction. The larger the duration deviation coefficient, the smaller the corresponding operation evaluation value. The evaluation matching relationship between the two is determined by the staff in advance. By outputting the operation evaluation value to the management terminal, the management personnel can know the overall operation accuracy of the current system, so that when a large deviation occurs, they can intervene in time to repair the system.

[0093] Refer to Figure 2 , based on the same inventive concept, an embodiment of the present invention provides an intelligent bobbin-changing system for polyester filament production, including: An acquisition module, configured to acquire the bobbin monitoring parameters of each bobbin; A processing module, connected to the acquisition module, for storing and processing information; The processing module inputs the filament bobbin monitoring parameters into a preset operation prediction model to determine the predicted operation duration, defines the filament bobbins with a predicted operation duration less than the preset reference analysis duration as candidate filament bobbins, and defines the predicted operation duration corresponding to the candidate filament bobbins as the predicted candidate duration; The processing module determines the candidate winding positions according to the candidate filament bobbins, and determines the bobbin dropping processing duration corresponding to the candidate winding positions according to a preset processing matching relationship; The processing module performs a random order sorting on each candidate filament bobbin to determine the simulated bobbin dropping order, and determines the adjacent moving distance according to the candidate winding positions of adjacent candidate filament bobbins in the simulated bobbin dropping order; The processing module calculates according to the adjacent moving distance and a preset gripper moving speed to determine the gripper moving duration, and calculates according to the simulated bobbin dropping order, the bobbin dropping processing duration, the gripper moving duration, and the predicted candidate duration to determine the overall simulation duration; The processing module determines the overall simulation duration with the smallest value according to a preset sorting rule, defines the simulated bobbin dropping order corresponding to the overall simulation duration as the effective bobbin dropping order, and performs bobbin dropping processing on each candidate filament bobbin according to the effective bobbin dropping order; The gripper allocation module is used to allocate candidate filament bobbins in the case of multiple grippers to improve the overall operation efficiency; The reasonable degree value determination module is used to determine a relatively accurate reasonable degree value for analysis; The reasonable degree value correction module corrects the determined reasonable degree value according to the actual situation; The correction degree value determination module determines the correction degree value according to the actual situation; The simulated bobbin dropping order screening module determines a unique effective bobbin dropping order from multiple simulated bobbin dropping orders that meet the requirements; The bobbin dropping evaluation module is used to evaluate the overall situation of the bobbin dropping operation.

[0094] Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above division of each functional module is used as an example for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. The specific working processes of the above-described system, device, and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

Claims

1. An intelligent bobbin dropping method for polyester filament production, characterized in that, Including: Obtaining the spool monitoring parameters of each spool; Inputting the spool monitoring parameters into a preset operation prediction model to determine the predicted operation duration, defining the spools with a predicted operation duration less than the preset reference analysis duration as candidate spools, and defining the predicted operation duration corresponding to the candidate spools as the predicted candidate duration; Determining the candidate winding positions according to the candidate spools, and determining the bobbin dropping processing duration corresponding to the candidate winding positions according to the preset processing matching relationship; Randomly sorting the candidate spools in sequence to determine the simulated bobbin dropping order, and determining the adjacent moving distance according to the candidate winding positions of adjacent candidate spools in the simulated bobbin dropping order; Calculating according to the adjacent moving distance and the preset gripper moving speed to determine the gripper moving duration, and calculating according to the simulated bobbin dropping order, the bobbin dropping processing duration, the gripper moving duration, and the predicted candidate duration to determine the overall simulation duration; Determining the overall simulation duration with the smallest value according to the preset sorting rule, defining the simulated bobbin dropping order corresponding to the overall simulation duration as the effective bobbin dropping order, and performing bobbin dropping processing on each candidate spool according to the effective bobbin dropping order.

2. The intelligent bobbin doffing method for producing polyester filament according to claim 1, wherein After the candidate spools are determined, the intelligent bobbin dropping method for polyester filament production further includes: Obtaining the number of available grippers; Judging whether the number of available grippers is one; If the number of available grippers is one, randomly sorting the candidate spools in sequence to determine the simulated bobbin dropping order; If the number of available grippers is not one, constructing an equal number of initially empty induction sets according to the number of available grippers, randomly allocating each candidate spool to one induction set, and generating an overall allocation plan according to each induction set after all candidate spools are allocated; Sequencing the candidate spools in each induction set in sequence to determine the local simulation order, and determining the local simulation duration according to the local simulation order; Analyzing according to the local simulation order and the local simulation duration in each induction set under each overall allocation plan to determine the rationality value; Defining the overall allocation plan corresponding to the rationality value with the largest value as the effective allocation plan, defining the local simulation order corresponding to the rationality value with the largest value under the effective allocation plan as the local effective order, and performing bobbin dropping processing on each corresponding candidate spool according to each local effective order.

3. The intelligent bobbin doffing method for producing polyester filament according to claim 2, characterized in that, The steps of analyzing according to the local simulation order and the local simulation duration in each induction set under each overall allocation plan to determine the rationality value include: Determining the local simulation duration with the largest value among all local simulation durations and defining it as the completion requirement duration; Calculating according to the bobbin dropping processing duration and the gripper moving duration of each candidate spool in each local simulation order to determine the theoretical processing duration; Calculating according to the theoretical processing duration and the predicted candidate duration to determine the monomer lag duration, and performing a summation calculation on each monomer lag duration to determine the overall lag duration; Calculating according to the completion requirement duration, the overall lag duration, and the preset fixed calculation parameters to determine the rationality value.

4. The intelligent bobbin doffing method for producing polyester filaments according to claim 3, characterized in that, After the rationality value is determined, the intelligent bobbin dropping method for polyester filament production further includes: Determine the type lag duration corresponding to the filament bobbin monitoring parameter according to the preset type matching relationship; Judge whether there is a situation where the monomer lag duration of each candidate filament bobbin corresponding to the current reasonable degree value is greater than the corresponding type lag duration; If there is no situation where the monomer lag duration of each candidate filament bobbin corresponding to the current reasonable degree value is greater than the corresponding type lag duration, then maintain the currently determined reasonable degree value; If there is a situation where the monomer lag duration of each candidate filament bobbin corresponding to the current reasonable degree value is greater than the corresponding type lag duration, then calculate the difference between the current reasonable degree value and the preset correction degree value to update the reasonable degree value.

5. The intelligent bobbin doffing method for producing polyester filaments according to claim 4, wherein, It also includes the determination step of the correction degree value, and this step includes: Judge whether there is at least one situation where the monomer lag duration of at least one candidate filament bobbin is greater than the corresponding type lag duration in all overall allocation schemes; If not all overall allocation schemes correspond to at least one situation where the monomer lag duration of at least one candidate filament bobbin is greater than the corresponding type lag duration, then determine the preset fixed degree value as the correction degree value; If all overall allocation schemes correspond to at least one situation where the monomer lag duration of at least one candidate filament bobbin is greater than the corresponding type lag duration, then define the candidate filament bobbin with the monomer lag duration greater than the corresponding type lag duration as the lag filament bobbin; Calculate according to the monomer lag duration of the lag filament bobbin and the corresponding type lag duration to determine the excess lag ratio; Determine the lag influence coefficient corresponding to the filament bobbin monitoring parameter of the lag filament bobbin according to the preset influence matching relationship; Calculate according to all excess lag ratios and the corresponding lag influence coefficients to determine the overall influence parameter; Determine the correction degree value corresponding to the overall influence parameter according to the preset correction matching relationship.

6. The intelligent bobbin doffing method for the production of polyester filaments according to claim 5, characterized in that, At After the overall simulation duration is determined, the intelligent bobbin dropping method for polyester filament production further includes: Judge whether there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration; If there are no at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, then define the effective bobbin dropping sequence according to the overall simulation duration with the minimum value; If there are at least two simulation bobbin dropping sequences with the same and minimum overall simulation duration, then define the simulation bobbin dropping sequence corresponding to the overall simulation duration with the minimum value as the alternative bobbin dropping sequence; Determine the lag filament bobbin according to each candidate filament bobbin in each alternative bobbin dropping sequence, and calculate the difference between the monomer lag duration of the lag filament bobbin and the corresponding type lag duration to determine the excess lag duration; Calculate the sum of all excess lag durations to determine the overall delay duration; Determine the overall delay duration with the minimum value according to the sorting rule, and define the alternative bobbin dropping sequence corresponding to this overall delay duration as the effective bobbin dropping sequence.

7. The intelligent bobbin doffing method for producing polyester filaments according to claim 1, characterized in that After the bobbin dropping process is performed on each candidate filament bobbin, the intelligent bobbin dropping method for polyester filament production further includes: Obtain the actual operation duration and the theoretical operation duration; Calculate according to the actual operation duration and the theoretical operation duration to determine the duration deviation coefficient; Determine the operation evaluation value corresponding to the duration deviation coefficient according to the preset evaluation matching relationship, and output the operation evaluation value to the preset management terminal.

8. An intelligent bobbin falling system for polyester filament production, characterized in that, Including: An acquisition module, configured to acquire the bobbin monitoring parameters of each bobbin; A processing module, connected to the acquisition module, for storing and processing information; The processing module inputs the bobbin monitoring parameters into a preset operation prediction model to determine the predicted operation duration, defines the bobbin with the predicted operation duration less than the preset reference analysis duration as the candidate bobbin, and defines the predicted operation duration corresponding to the candidate bobbin as the predicted candidate duration; The processing module determines the candidate winding positions according to the candidate bobbins, and determines the doffing processing duration corresponding to the candidate winding positions according to the preset processing matching relationship; The processing module performs a random sequence sorting on each candidate bobbin to determine the simulated doffing sequence, and determines the adjacent moving distance according to the candidate winding positions of adjacent candidate bobbins in the simulated doffing sequence; The processing module calculates according to the adjacent moving distance and the preset gripper moving speed to determine the gripper moving duration, and calculates according to the simulated doffing sequence, the doffing processing duration, the gripper moving duration, and the predicted candidate duration to determine the overall simulation duration; The processing module determines the overall simulation duration with the smallest value according to the preset sorting rule, defines the simulated doffing sequence corresponding to the overall simulation duration as the effective doffing sequence, and performs doffing processing on each candidate bobbin according to the effective doffing sequence.

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