Superfine fiber yarn production process simulation test platform

Abstract

The invention discloses a superfine fiber yarn production process simulation test platform, and relates to the technical field of fiber yarn manufacturing, and the platform comprises a task collection module which is used for obtaining production task information; the arrangement scheme acquisition module is used for acquiring a production line arrangement scheme; the model construction module is used for obtaining a cellosilk production line model; the long time sequence acquisition module is used for acquiring a plurality of index value sequence sets; the analysis and authentication module is used for performing a static accumulation test; the target optimization module is used for obtaining a plurality of optimized production equipment position identification sets; and the test result output module is used for outputting the simulation test result. The technical problems of low production efficiency and static accumulation caused by the fact that production line layout and equipment control parameters cannot be quickly optimized due to production task change or process condition fluctuation are solved, and the technical effects of optimizing the production process and the equipment layout through the analogue simulation system, reducing static accumulation and improving the production efficiency and the product quality are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of fiber filament manufacturing, and in particular to a simulation test platform for the production process of ultrafine fiber filaments. Background Art

[0002] Microfiber yarn is a textile material with excellent properties and is widely used in various industrial and consumer products. However, the production process of microfiber yarn is complex and requires high precision control of equipment. Existing production line layout and control systems usually rely on manual debugging and empirical parameters, which are difficult to adapt to changes in production tasks and fluctuations in process conditions. Especially in the production process, the rationality of equipment layout and static electricity accumulation have a significant impact on product quality. The accumulation of static electricity not only affects the formation of the fiber, but may also cause equipment failure during the production process. In addition, when faced with complex production processes and changing process conditions, traditional production control systems lack effective simulation testing methods and cannot optimize production equipment and adjust parameters in a timely manner, which makes it difficult to ensure production line efficiency and product consistency. Summary of the invention

[0003] This application solves the technical problem of low production efficiency and static electricity accumulation caused by the inability to quickly optimize the production line layout and equipment control parameters due to changes in production tasks or fluctuations in process conditions by providing a simulation test platform for the ultrafine fiber production process. It achieves the technical effect of optimizing the production process and equipment layout through a simulation system, reducing static electricity accumulation, and improving production efficiency and product quality.

[0004] The present application provides a simulation test platform for the production process of ultrafine fiber yarns, the platform comprising: a task collection module, used to perform production task collection of target ultrafine fiber yarns and obtain production task information; a layout scheme acquisition module, used to perform process flow analysis and production equipment configuration based on the production task information, and obtain a production line layout scheme, wherein the production line layout scheme includes multiple process flow nodes, multiple production equipment sets and multiple production equipment control parameter sets, each process flow node corresponds to a production equipment set, and the multiple production equipment sets include multiple production equipment location identification sets; a model construction module, used to use a simulation system to construct a three-dimensional model according to the multiple process flow nodes, multiple production equipment sets, multiple production equipment control parameter sets and multiple production equipment location identification sets to obtain a fiber yarn production line model; a long time series acquisition module, Used to call the fiber filament production line model to perform a production simulation test, and perform long-term time series acquisition of indicator values ​​in the test process according to a preset monitoring indicator set, to obtain multiple indicator value sequence sets, wherein each indicator value sequence set corresponds to a monitoring indicator; an analysis and certification module, used to use the multiple indicator value sequence sets as preliminary certification analysis data for the simulation test, and perform analysis and certification. If the certification is passed, an electrostatic accumulation test is performed in combination with the fiber filament production line model; a target optimization module, used to perform target optimization on the distribution positions of multiple production equipment sets of the fiber filament production line model based on the electrostatic accumulation test results if the electrostatic accumulation test fails, to obtain multiple optimized production equipment position identification sets; a test result output module, used to output multiple process flow nodes, multiple production equipment sets and multiple optimized production equipment position identification sets as simulation test results.

[0005] The ultrafine fiber production process simulation test platform proposed in this application is intended to include a task collection module for executing the production task collection of the target ultrafine fiber to obtain production task information; based on the production task information, process flow analysis and production equipment configuration are performed to obtain a production line layout plan, wherein the production line layout plan includes multiple process flow nodes, multiple production equipment sets, and multiple production equipment control parameter sets, each process flow node corresponds to a production equipment set, and the multiple production equipment sets include multiple production equipment location identification sets; a simulation system is used to construct a three-dimensional model based on the multiple process flow nodes, multiple production equipment sets, multiple production equipment control parameter sets, and multiple production equipment location identification sets to obtain a fiber production line model; The fiber filament production line model is called to perform a production simulation test, and the indicator value is collected in a long time series according to a preset monitoring indicator set during the test process to obtain multiple indicator value sequence sets, wherein each indicator value sequence set corresponds to a monitoring indicator; the multiple indicator value sequence sets are used as preliminary certification analysis data for the simulation test, and analysis and certification are performed. If the certification passes, an electrostatic accumulation test is performed in combination with the fiber filament production line model; if the electrostatic accumulation test fails, the distribution positions of multiple production equipment sets of the fiber filament production line model are targeted for optimization based on the electrostatic accumulation test results to obtain multiple optimized production equipment location identification sets; multiple process flow nodes, multiple production equipment sets, and multiple optimized production equipment location identification sets are output as simulation test results. The technical problem of low production efficiency and electrostatic accumulation caused by changes in production tasks or fluctuations in process conditions is solved, and the technical effect of optimizing production processes and equipment layouts, reducing electrostatic accumulation, and improving production efficiency and product quality through simulation systems is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] In order to more clearly illustrate the technical solution of the embodiment of the present disclosure, the accompanying drawings of the embodiment of the present disclosure are briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the platform according to the embodiment of the present application. It should be understood that the previous or following operations are not necessarily performed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0007] Figure 1 A schematic diagram of the structure of a simulation test platform for the production process of ultrafine fibers provided in an embodiment of the present application; Figure 2 A schematic diagram of the execution process of the mixed crop matching module in the ultrafine fiber production process simulation test platform provided in an embodiment of the present application.

[0008] Explanation of the accompanying drawings: task collection module 1, arrangement plan acquisition module 2, model construction module 3, long time series collection module 4, analysis and authentication module 5, target optimization module 6, test result output module 7. DETAILED DESCRIPTION

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0010] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0011] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0012] The present application embodiment provides a simulation test platform for the production process of ultra-fine fibers, such as Figure 1 As shown, the platform includes: The task collection module is used to collect the production tasks of the target ultra-fine fiber yarn and obtain the production task information.

[0013] In one embodiment, in the task collection module, the system terminal collects the task requirement information of the current production target ultra-fine fiber yarn. This information includes the target specifications of the production batch, the production quantity, the type of material, and the required process parameters. By collecting this data, it can provide a reference for the subsequent process flow design and equipment configuration, ensuring that the equipment and process in the production process can meet the specific production goals, thereby realizing the automation and optimization of production tasks.

[0014] A layout plan acquisition module is used to perform process flow analysis and production equipment configuration based on the production task information to obtain a production line layout plan, wherein the production line layout plan includes multiple process flow nodes, multiple production equipment sets and multiple production equipment control parameter sets, each process flow node corresponds to a production equipment set, and the multiple production equipment sets include multiple production equipment location identification sets.

[0015] In one embodiment, in the arrangement scheme acquisition module, the system terminal performs data mining based on production task information, such as spinning temperature, winding angle, component pressure and other key parameters, and preliminarily generates multiple production line arrangement schemes. Each arrangement scheme includes different process nodes, production equipment sets and production equipment control parameter sets, and each production equipment set includes a production equipment location identification set to ensure that it meets the requirements of different tasks. For example, in the spinning stage of producing 10D series ultrafine filaments, the equipment set includes spinning machines, temperature control systems, pressure sensors and other equipment, and the control parameters include spinning temperature (such as 290-292°C), winding angle control, component pressure (such as 80-150BAR), etc. At the drafting node, the equipment set may include wire guide equipment and drafting machines, and the control parameters include drafting speed and winding angle (6°-8°), etc. Subsequently, a feasibility evaluation is performed based on these production line arrangement schemes, and multiple scheme feasibility factors are calculated. These factors evaluate the stability, production efficiency and failure rate of each scheme during the production process. For example, by combining the precise control parameters such as temperature, pressure, and flow controlled by PLC, the actual operability of each layout scheme and its impact on fiber quality are evaluated. Afterwards, based on the feasibility factors of the scheme, derivative optimization is performed for each production line layout scheme. The production line layout scheme is further optimized by combining the operating efficiency, layout rationality, and synergy between different production equipment. The derived optimized scheme ensures the stability of the production process and reduces the downtime and failure rates. The final production line layout scheme can maximize the satisfaction of current production task requirements while ensuring production efficiency and product quality.

[0016] Further, if Figure 2 As shown, the present application provides a method for performing process flow analysis and production equipment configuration based on the production task information to obtain a production line arrangement plan, wherein the production line arrangement plan includes multiple process flow nodes, multiple production equipment sets, and multiple production equipment control parameter sets, including: Based on the production task information, data mining is performed to determine multiple mining production line arrangement schemes; the multiple mining production line arrangement schemes are traversed to perform production log retrieval to obtain multiple mining production line production log sets; based on the multiple mining production line production log sets, scheme feasibility evaluation is performed to obtain multiple scheme feasibility factors; based on the multiple scheme feasibility factors and multiple mining production line arrangement schemes, derivative optimization is performed to determine the production line arrangement scheme.

[0017] Preferably, the system terminal obtains multiple possible production line layout schemes from historical operation logs through data mining technology based on production task information. These schemes are preliminary layouts obtained by analyzing existing process flows and equipment resources for different production task requirements and equipment configurations. Subsequently, these generated production line layout schemes are traversed, and the historical production data corresponding to each scheme is retrieved to form multiple production log sets. These logs record key information such as equipment operation status, process parameter adjustment, and production results in previous production processes. Afterwards, based on these production log sets, feasibility evaluation is performed on each layout scheme. Specifically, the system terminal extracts key data from the production log set corresponding to each generated production line layout scheme. These data include the operation status of the equipment, process parameters (such as temperature, pressure, speed, etc.), production time, product quality results, and equipment failure information during the production process. Then determine the key indicators for feasibility evaluation, which can be production efficiency (such as production speed, output), product quality (such as qualified rate, defective rate), equipment operation stability (such as number of failures, failure duration), and resource utilization (such as energy consumption, material loss), etc. Different layout schemes may perform differently on these indicators. Subsequently, the data extracted from the log are quantified. For example, the production speed can be expressed as the ratio of output to time, the qualified rate can be expressed as the ratio of the number of qualified products to the total number of products, and the failure rate can be expressed as the ratio of failure time to total production time. Through these quantitative methods, the original production log data is converted into a set of quantifiable indicators. Then, based on the quantified indicators, the feasibility of the scheme is evaluated. Each layout scheme will calculate a feasibility factor based on its performance on each indicator. These feasibility factors are given different weights according to the importance of each indicator, and then calculated by weighted average. These factors reflect the stability, efficiency and failure rate of each scheme in actual production, and evaluate which schemes are more suitable for the current production task. Then, the system terminal combines these feasibility factors and multiple layout schemes for derivative optimization. Through the further adjustment of the equipment layout and control parameters by the scheme evaluation identifier, a final optimized production line layout scheme is formed to ensure that it can adapt to the needs of the current production task and maximize production efficiency and product quality.

[0018] Furthermore, the present application provides a method for performing derivative optimization based on the feasibility factors of the multiple schemes and the multiple mining production line arrangement schemes to determine the production line arrangement scheme, including: A solution evaluation identifier is constructed based on the plurality of mining production line production log sets and the plurality of solution feasibility factors.

[0019] Optionally, the system terminal divides the obtained multiple mining production line production log sets and the corresponding multiple scheme feasibility factors into a training set and a validation set, and trains the scheme evaluation identifier through the training set. The scheme evaluation identifier can be constructed based on decision trees, random forests, support vector machines, etc. Taking random forest as an example, a scheme evaluation identifier is constructed using a random forest model. The identifier includes an input layer, multiple decision trees, a voting mechanism, etc. The system terminal inputs the mining production log set of the production line in the training set into the input layer of the scheme evaluation identifier. In the initialization stage, the scheme evaluation identifier constructs multiple decision trees according to the preset parameters such as the number of trees and the maximum depth. Each decision tree is trained based on a randomly selected feature subset and a data subset, starting from the root node, and gradually splitting the features until the leaf node outputs the corresponding scheme feasibility factor prediction result. Each tree makes a prediction independently. Subsequently, the output results of all decision trees are combined using the majority voting mechanism to obtain the final predicted value of the scheme feasibility factor. In order to optimize the model, the mean square error (MSE) is used as the loss function to calculate the error between the prediction result and the scheme feasibility factor. The performance of the model is further optimized by adjusting the tree structure (such as the depth of the tree, the minimum number of split samples, etc.) and the sample division method of the training set. During the training process, the cross-validation method is used to ensure the generalization ability of the solution evaluation identifier and avoid overfitting. At each validation, the random forest trains the solution evaluation identifier on the training set and tests its prediction effect on the validation set. The system terminal will continuously adjust the parameters through these steps to minimize the value of the loss function and improve the prediction accuracy of the solution evaluation identifier. After multiple rounds of training, the performance of the solution evaluation identifier is tested using the validation set to evaluate its prediction accuracy for the feasibility factor of the production line arrangement scheme. If the prediction error of the solution evaluation identifier on the validation set meets the expected requirements, the current solution evaluation identifier is output for solution evaluation in actual production. If the performance does not meet expectations, adjust the hyperparameters of the identifier, such as the number of decision trees, the maximum depth of the tree, the minimum number of sample splits, etc., and retrain until the identifier performs optimally.

[0020] The multiple mining production line arrangement schemes are hierarchically clustered according to the sizes of the scheme feasibility factors to obtain multiple cluster mining production line arrangement scheme sets, wherein the multiple cluster mining production line arrangement scheme sets include multiple cluster scheme feasibility factors; the cluster mining production line arrangement scheme set corresponding to the maximum value of the multiple cluster scheme feasibility factors is used as a derived object set; the derived object set is subjected to scheme derivation optimization in combination with the scheme evaluation identifier to determine the production line arrangement scheme.

[0021] Optionally, the system terminal organizes all mining production line arrangement schemes and their corresponding scheme feasibility factors into a feature matrix for cluster analysis. Each mining production line arrangement scheme is used as a data point, and its corresponding feasibility factor will be used for subsequent clustering processing. Subsequently, the distance between each production line arrangement scheme is calculated, that is, based on the scheme feasibility factor of each scheme, the similarity distance between any two schemes is calculated using the Euclidean distance formula. The smaller the distance, the closer the feasibility factors of the two schemes are, and the more similar their performance in the production task is. Then, the schemes with similarity distances less than or equal to the similarity distance threshold are merged to generate initial clusters, each of which includes a clustering scheme feasibility factor, which is obtained by calculating the mean of the scheme feasibility factors in the cluster. After that, the distance between the new cluster and other schemes or clusters is recalculated, and the schemes or clusters with the closest distance are continued to be found and merged. This process is repeated until the similarity distances between all schemes or clusters are greater than the similarity distance threshold. At this time, the system terminal merges all cluster clusters to form a set of multiple cluster mining production line arrangement schemes. Then, the clustering scheme feasibility factors of all clustering mining production line arrangement scheme sets are compared, and the clustering mining production line arrangement scheme set with the largest clustering scheme feasibility factor is selected as the derived object set. This means that the schemes in this set perform well in all aspects and are the most promising scheme set. Then, combined with the scheme evaluation identifier, the selected derived object set is optimized for scheme derivation. In this process, the system terminal will make further adjustments and optimizations based on the schemes in the derived object set, involving adjusting the equipment layout, optimizing control parameters, etc., to further improve the feasibility of these schemes. The evaluation identifier will continue to evaluate the optimized schemes and ensure that the final production line arrangement scheme performs best in various key indicators. Through scheme derivation optimization, an optimized production line arrangement scheme is finally formed. This scheme has the largest feasibility factor among the derived schemes, has the best process flow layout, equipment configuration and control parameter settings, can maximize production efficiency and ensure product quality.

[0022] Furthermore, the present application provides a method for performing scheme derivation optimization based on the derived object set to determine the production line arrangement scheme, including: Extract the derivative scheme feasibility factor set corresponding to the derivative object set, and use the derivative object corresponding to the maximum value in the derivative scheme feasibility factor set as the direction derivative object, and use the remaining derivative objects as the follow-up derivative object set; use the direction derivative object as the target of scheme derivation optimization, optimize and adjust the follow-up derivative object set according to the preset adjustment range, and obtain multiple adjustment follow-up derivative object sets; use the scheme evaluation identifier to evaluate the scheme feasibility of the multiple adjustment follow-up derivative object sets, and obtain multiple adjustment follow-up derivative scheme feasibility factors; determine whether there is a derivative scheme feasibility factor greater than the directional derivative object among the multiple adjustment follow-up derivative scheme feasibility factors, and if so, update the adjustment follow-up derivative object corresponding to the maximum value among the multiple adjustment follow-up derivative scheme feasibility factors as the directional derivative object, and continue to perform scheme derivation optimization until the preset number of times is met, and use the directional derivative object corresponding to the maximum value of the derivative scheme feasibility factor during the optimization process as the production line arrangement scheme.

[0023] Optionally, the system terminal extracts the feasibility factor of the derivative scheme corresponding to each scheme from the derivative object set. These factors reflect the performance of each scheme in production. Then find the derivative object with the largest value among these feasibility factors and set it as the directional derivative object. This directional derivative object is the scheme with the best performance at present. The remaining derivative objects will be used as the follow-up derivative object set, waiting for subsequent optimization. Subsequently, the directional derivative object is used as the optimization target, and the schemes in the follow-up derivative object set are adjusted according to the preset adjustment range. These adjustments may involve fine-tuning of control parameters, optimization of equipment position, etc., with the purpose of improving the overall performance of the follow-up object. For example, in the spinning process, the spinning temperature in the production line arrangement scheme is set to 290°C, and the preset adjustment range can be ±2°C each time. The current spacing between the guide wire machine and the drawing machine is 100cm, and the preset adjustment range is ±5cm. After optimization, multiple new adjusted follow-up derivative object sets are formed. Afterwards, these adjusted follow-up derivative object sets are re-evaluated using the constructed scheme evaluation identifier. The scheme evaluation identifier will calculate new derivative scheme feasibility factors for each adjusted scheme, and these factors will reflect the production performance of the scheme after adjustment. Then compare the feasibility factor of the adjusted follow-up derivative scheme with the feasibility factor of the current direction derivative object. If it is found that the feasibility factor of one of the adjusted follow-up schemes is higher than that of the current direction object, then the scheme with better performance will be replaced by the new direction derivative object. Continue to perform the above steps, optimize and adjust the follow-up object, and continuously update the direction derivative object until the preset number of optimizations is reached. In this process, the result of each optimization will be evaluated by the scheme evaluation identifier to ensure that each round of optimization is gradually improving the performance of the scheme. When the optimization process is completed, select the direction derivative object corresponding to the maximum value of the feasibility factor of the derivative scheme in the optimization process and use it as the final production line layout plan. This means that this scheme performs best among all optimization iterations and can most effectively improve production efficiency, reduce energy consumption and ensure product quality.

[0024] The model building module is used to use a simulation system to build a three-dimensional model according to the multiple process flow nodes, multiple production equipment sets, multiple production equipment control parameter sets and multiple production equipment location identification sets to obtain a fiber production line model.

[0025] In one embodiment, in the model building module, the system terminal CAD software (such as AutoCAD, SolidWorks, etc.) constructs a three-dimensional model. A three-dimensional model of each device is created based on the obtained production equipment set. Ensure that each device in the model accurately reflects its physical size and structure, especially the spatial position and contact points between the devices. For example, the path of the fiber between the spinning machine and the winding machine needs to be accurately displayed in the model for subsequent simulation tests. In the CAD software, the equipment is laid out and configured according to the production process nodes. Each process node represents an important step in the production process, such as spinning, drafting, winding, etc. Ensure that each device is arranged in the three-dimensional model according to the arrangement of the actual production line. Each process node corresponds to a specific set of equipment, which should be arranged and connected in the logical order of the process flow to ensure that the model accurately reflects the actual production process. Subsequently, the production equipment control parameter set of each device is imported into the three-dimensional model. Dynamic control points of the equipment can be added to the model, such as the temperature control point of the spinning machine, the speed control point of the winding machine, etc. Through these control points, the production parameters can be monitored and adjusted in real time in the simulation test to ensure that the simulation results are consistent with the actual production. Then, based on the set of production equipment location identifiers, the exact location of each device is marked in the 3D model. These location identifiers help to clarify the physical location and relative distance of each device in the subsequent simulation process. For example, the distance between the spinning machine and the winding machine needs to be accurately marked to ensure that the material flow path in the simulation process is consistent with the actual production. After completing the construction of the 3D model, the system terminal imports the model into the SIMATIC PLCSIMAdvanced simulation system to conduct a full-process production process simulation test. SIMATIC PLCSIM Advanced can simulate the operating status and production process of the equipment through integration with the PLC controller. Through this system, the operation effect of the production line can be tested in a virtual environment, the control parameters of the production equipment can be adjusted, and the collaborative work between the equipment can be monitored. During the simulation test, the system terminal will run according to the set process flow and provide real-time feedback on whether the status and control parameters of each device meet the requirements. For example, test whether the temperature of the spinning machine is maintained between 290 and 292°C, and whether the speed of the winding machine is synchronized with the fiber production. Through simulation testing, the rationality of the layout of the production line, the accuracy of equipment control, and the coordination of each process node can be verified in a virtual environment. Afterwards, the equipment operation status and production effect are analyzed based on the test results of SIMATIC PLCSIM Advanced. If problems are found in certain process nodes or equipment configurations during the simulation, adjustments can be made in the model. For example, the relative positions of the spinning machine and the winding machine can be adjusted or the control parameters of the equipment can be modified.The purpose of simulation testing is to discover potential problems in advance through a virtual environment, optimize the layout of the production line, and ultimately improve the efficiency and product quality in actual production. After multiple rounds of simulation testing and optimization, a three-dimensional model of a fiber production line that meets the requirements is finally generated. The model can accurately reflect the layout, equipment configuration, and control parameters of the actual production line, ensuring the stability and efficiency of the production line. At the same time, the model can be used for subsequent production management and improvement, providing an important reference for fiber production.

[0026] The long time series acquisition module is used to call the fiber production line model to perform production simulation testing, and perform long time series acquisition of indicator values ​​according to a preset monitoring indicator set during the test process to obtain multiple indicator value sequence sets, wherein each indicator value sequence set corresponds to a monitoring indicator.

[0027] In one embodiment, in the long time series acquisition module, the system terminal uses the constructed three-dimensional fiber production line model to perform a production simulation test. Before the simulation test begins, a set of key indicators that need to be monitored is preset. These indicators are usually related to the efficiency of the production process, product quality and equipment performance. For example, the speed of the winding machine, tensile strength, etc. These preset monitoring indicators will be used to evaluate the stability of the production process and the quality of the product. Subsequently, the fiber production line model is started and the simulation is run according to the set process flow. In this process, all equipment operates according to the established control parameters, and the entire production line simulates the actual fiber manufacturing process. The operating status and production process of each device will be displayed in real time. During the production simulation test, the system terminal will perform long-term acquisition of the key parameters of the equipment and production process according to the preset monitoring indicators. Long-term acquisition refers to continuously collecting the changes in each monitoring indicator within the time range of the entire simulation test. For each monitoring indicator, data will be collected regularly throughout the production process, and a time series data set will be generated. The collected data of each preset monitoring indicator will form a separate indicator value sequence set. The data points in each sequence set represent the value of the monitoring indicator at a specific time point, providing data support for subsequent optimization and ensuring the stability of production line operation and consistency of product quality.

[0028] Furthermore, the present application provides a set of preset monitoring indicators, including: The preset monitoring index set includes tensile strength, winding speed and fiber diameter.

[0029] Preferably, the preset monitoring index set includes tensile strength, winding speed and fiber diameter. Among them, tensile strength is an important indicator to measure how much force a fiber material can withstand without breaking when subjected to tensile force. By monitoring the tensile strength, the quality and mechanical properties of the fiber during the production process can be evaluated. The higher the tensile strength, the better the toughness and durability of the fiber. This indicator can help ensure that the produced fiber meets the required strength requirements. The winding speed refers to the speed at which the fiber is wound onto the winder during the production process. The stability of this speed is crucial to the overall efficiency of the production line and the uniformity of the fiber. Winding speeds that are too fast or too slow will affect the quality of the fiber. For example, too fast speeds may cause the fiber to be too tight or broken, while too slow speeds may affect production efficiency. By real-time monitoring of the winding speed, it is possible to ensure that the production line is running in the best condition. Fiber diameter is an important parameter to measure the thickness of the fiber, which directly affects the texture of the fiber and the performance of the final product. By monitoring the fiber diameter, any abnormal changes can be discovered in time during the production process to ensure that the fiber diameter is within the preset range. If the diameter fluctuates too much, it may affect the consistency of the fiber and the overall quality of the product. By monitoring these key indicators, the physical properties of the fiber during the production process can be effectively controlled to ensure that the production line runs efficiently and produces fiber products that meet quality standards.

[0030] The analysis and certification module is used to use the plurality of indicator value sequence sets as preliminary certification analysis data for simulation testing, and to perform analysis and certification. If the certification is passed, an electrostatic accumulation test is performed in combination with the fiber production line model.

[0031] In one embodiment, in the analysis and authentication module, the system terminal uses multiple indicator value sequence sets obtained by long-time acquisition as preliminary authentication analysis data for simulation testing. These data reflect the changes in various key indicators in the entire production process and can be used to evaluate the stability of production line operation and product quality. Subsequently, these preliminary authentication data are subjected to fluctuation analysis, and the authentication factor of each indicator value sequence set is calculated, and then the mean value of all indicator value sequence sets is calculated to obtain multiple authentication factors. After that, the two authentication factors are analyzed and authenticated according to the preset standard. If the authentication result shows that the two authentication factors are within the expected range and the data change trend meets the production requirements, the system terminal will pass the authentication. This means that the operation status of each device in the production line model is normal, and the production parameters have been optimized to meet the product quality requirements. In the case of passing the authentication, the system terminal combines the fiber production line model to perform an electrostatic accumulation test. Electrostatic accumulation is a common problem in the fiber production process, which may affect production efficiency and product quality. Therefore, the system terminal monitors and analyzes the static electricity that may be generated in the production process through the simulation model to evaluate whether there is a problem of static electricity accumulation. If a lot of static electricity is found to accumulate, further production line optimization is required, such as adjusting the equipment layout or adding conductive grounding equipment to ensure that there are no potential static electricity problems affecting production throughout the production process.

[0032] Further, the present application provides a method of using the plurality of indicator value sequence sets as preliminary certification analysis data for simulation testing to perform analysis and certification, including: The plurality of indicator value sequence sets are traversed to perform fluctuation analysis to determine a plurality of first authentication factors; the plurality of indicator value sequence sets are averaged to determine a plurality of second authentication factors; and analysis and authentication are performed based on the plurality of first authentication factors and the plurality of second authentication factors to obtain an authentication result.

[0033] Preferably, the system terminal traverses each set of indicator value sequences obtained through simulation tests (such as tensile strength, winding speed, fiber diameter, etc.) and performs fluctuation analysis on them. The purpose of this analysis is to evaluate the fluctuation of each indicator throughout the production process to determine whether it is within a reasonable range. For each set of indicator value sequences, the system terminal uses the standard deviation for fluctuation analysis. The standard deviation is a commonly used statistical indicator for measuring the degree of data fluctuation, which reflects the degree of deviation between the data point and the mean. The steps for calculating the standard deviation are as follows: calculate the mean of the indicator value sequence, calculate the difference between each data point and the mean, square these differences and sum them to obtain the variance, and then take the square root of the variance to obtain the standard deviation. The larger the standard deviation, the greater the fluctuation of the indicator in the production process, the data point is far from the mean, and the production process may not be stable enough; the smaller the standard deviation, the smaller the fluctuation of the indicator, the higher the data stability, and the more consistent the production process. In addition, the fluctuation can also be measured by methods such as the difference between the maximum and minimum values. Subsequently, the system terminal performs an inverse proportional calculation on the calculated standard deviation to obtain the first authentication factor of each set of indicator value sequences. After that, the mean is calculated for each set of indicator value sequences. This process is to evaluate whether the overall performance of each indicator meets the predetermined standards. By calculating the average values ​​of indicators such as tensile strength, winding speed, fiber diameter, etc., it can be determined whether the performance of these indicators throughout the production process meets the requirements. The results of these mean calculations will generate a second certification factor to reflect the overall performance of each indicator. Then, the system terminal combines the first certification factor and the second certification factor for comprehensive analysis and certification. By combining these two factors, the stability and consistency of each indicator in the production process can be more comprehensively evaluated. If the first certification factor of all indicators is within the preset fluctuation standard and the mean also meets the preset standard, the system terminal will judge that the certification is passed, indicating that the production line is running stably and the product quality meets the requirements. If the first certification factor or mean of some indicators deviates from the preset standard, the certification will not pass, and further adjustment of production parameters or equipment configuration is required to ensure production stability and quality consistency.

[0034] The target optimization module is used to optimize the distribution positions of multiple production equipment sets of the fiber filament production line model based on the results of the static accumulation test if the static accumulation test fails, and obtain multiple optimized production equipment position identification sets.

[0035] In one embodiment, in the target optimization module, if the system terminal shows that there is too much static electricity accumulation in the production process and the accumulation degree exceeds the process allowable range, the test is judged to be unqualified. At this time, the system terminal extracts the electric field strength distribution map from the static electricity accumulation test results. Subsequently, based on the electric field strength distribution map, the specific position that needs to be optimized is determined by identifying the edge of the electric field strength neighborhood. After clarifying the position that needs to be adjusted, the system terminal optimizes and adjusts the distribution position of the production equipment. Reduce or eliminate static electricity accumulation by re-adjusting the distance between the equipment, the equipment layout, or the relative position of certain equipment. For example, the distance between the spinning machine and the winding machine can be appropriately increased, or the arrangement of the wire guide equipment can be adjusted to reduce the risk of static electricity accumulation. After the optimization is completed, the system terminal will generate a new set of location identifiers for each adjusted equipment distribution. These location identifiers will record the specific location and arrangement of each device in the production line. The new equipment layout will help reduce static electricity accumulation without affecting production efficiency and product quality.

[0036] Furthermore, the present application also provides a method for performing target optimization on the distribution positions of multiple production equipment sets of the fiber yarn production line model based on the electrostatic accumulation test results to obtain multiple optimized production equipment position identification sets, including: Extract the electric field strength distribution diagram in the electrostatic accumulation test result, wherein the electric field strength distribution diagram is used to describe the electrostatic distribution during the operation of the fiber yarn production line model; based on the electric field strength distribution diagram, obtain M distribution points with electric field strength in the first M positions, wherein the M distribution points include M distribution center electric field strengths, and M is an integer greater than or equal to 1; perform electric field strength neighborhood edge identification on the M distribution points, and determine M electric field strength neighborhoods, wherein the M electric field strength neighborhoods include M neighborhood areas; perform distribution position optimization target identification based on the M distribution center electric field strengths, M distribution points, and the M electric field strength neighborhoods, and determine Q optimization target points, wherein Q is a positive integer less than or equal to M; perform distribution position optimization adjustment based on the Q optimization target points, and adaptively adjust the distribution positions of multiple production equipment sets according to the adjusted Q optimized production equipment position identifiers to obtain the multiple optimized production equipment position identifiers.

[0037] Preferably, the system terminal extracts an electric field strength distribution diagram from the static electricity accumulation test results. This distribution diagram shows the static electricity distribution of different positions of the fiber production line model during operation, reflecting the degree of static electricity accumulation in the area around the equipment. Through this diagram, it can be clearly seen which areas have strong static electricity, which may lead to static electricity accumulation problems in the production process. Subsequently, from the electric field strength distribution diagram, M distribution points with electric field strength values ​​in the top M positions are selected, where M is an integer greater than or equal to 1. These points are the locations with the strongest static electricity, and each distribution point has a corresponding central electric field strength value, representing the strength of static electricity at the point. Afterwards, the selected M distribution points are analyzed, and the electric field strength neighborhood edges around each distribution point are identified based on a preset difference threshold to form M electric field strength neighborhoods, each of which also includes its corresponding area. This process helps the system terminal understand the size of the static electricity accumulation range. Then, based on the M distribution center electric field strengths, distribution points, and neighborhood information identified in the electric field strength distribution diagram, the system terminal sorts the M distribution points and their corresponding electric field strength neighborhoods. The sorting criteria can be the magnitude of the electric field strength, the neighborhood area, or a combination of the two to determine the priority of each distribution point. Through this sorting, it is possible to determine which distribution points need to be treated first to reduce the impact of static electricity accumulation on production. After determining the priorities of the M distribution points, the system terminal selects Q optimization target points from them, where Q is a positive integer less than or equal to M. This means that the system terminal will select the most critical Q points from the M distribution points according to the severity of static electricity accumulation. These points are the areas where static electricity accumulates most seriously or has the most impact during the production process. Optimizing these points can significantly reduce the risks brought by static electricity during the production process. Finally, the position of the production equipment is adaptively adjusted according to the Q optimization target points. By optimizing the relative position and distance of the equipment, the risk of static electricity accumulation in these high electric field strength areas can be reduced. The adjusted equipment distribution location identification set will reflect the new layout of the equipment, thereby reducing the problem of static electricity accumulation.

[0038] Furthermore, the present application also provides for determining M electric field strength neighborhoods, including: Taking the M distribution points as the identification starting point, iterate in the electric field strength distribution map according to the preset identification step size, and add the points whose electric field strength difference with the M distribution center electric field strength of the M distribution points is within the preset difference threshold into the corresponding electric field strength neighborhood until the preset identification stop condition is met, so as to obtain the M electric field strength neighborhoods, wherein the preset identification stop condition is that the electric field strength difference between the point and the electric field strength difference between the M distribution center electric field strengths is less than the preset difference threshold.

[0039] Optionally, the system terminal uses the M distribution points in each electric field strength distribution map as the starting points for identification. Each distribution point has a corresponding central electric field strength value, which represents the electrostatic strength of the point. Subsequently, an identification step is set, and the electric field strength distribution map is gradually expanded according to this step. In each iteration, the system terminal will scan the electric field strength area around the M distribution points to find points with electric field strengths close to those of these distribution points. In each iteration, the difference between the electric field strength of the current point and the electric field strength at the center of the distribution point is calculated. The system terminal compares these differences with a preset difference threshold. If the difference between the electric field strength of the point and the strength of the distribution center is less than a preset threshold, the point is added to the neighborhood range of the distribution point, that is, these points are close in electric field strength and belong to the same electrostatic accumulation area. After that, continue to iteratively search for points that meet the conditions and add them to the corresponding electric field strength neighborhood. Through gradual expansion, the electric field strength neighborhood will continue to expand, including those points with electric field strength close to the distribution point until the preset recognition stop condition is reached, that is, when it is found during iteration that the difference in electric field strength between the new point and the distribution center is close to or exceeds the preset threshold, the system terminal will stop adding the point to the neighborhood and end the neighborhood expansion process of the current distribution point. When the difference in electric field strength is too large, it means that the electrostatic intensity in the area has dropped significantly, and the system terminal will no longer include these points in the neighborhood range. When the neighborhood expansion of all distribution points is completed, M electric field strength neighborhoods are obtained. Each neighborhood contains areas with electric field strength close to the corresponding distribution point, and the degree of electrostatic accumulation in these areas is high, which requires special attention and optimization.

[0040] The test result output module is used to output multiple process flow nodes, multiple production equipment sets and multiple optimized production equipment position identification sets as simulation test results.

[0041] In one embodiment, in the test result output module, the system terminal outputs multiple process nodes involved in the simulation test process as simulation test results. Each node represents a key step or operation link of the production line. These nodes reflect the process flow of the entire production process and provide a basis for subsequent equipment configuration and production adjustments. The production equipment set associated with each process node is output as a simulation test result. These equipment sets include all equipment used in the production line, as well as their configuration and operating parameters in the simulation test. By outputting these equipment sets, it is possible to understand in detail the equipment involved in each production link and its performance in the simulation test. The optimized production equipment position identification set is also output as a simulation test result. This identification set includes the specific location and layout of each device in the production line, especially the optimized equipment arrangement, to ensure that problems such as static electricity accumulation can be minimized during the production process, and to improve production efficiency and product quality.

[0042] The ultra-fine fiber production process simulation test platform according to the embodiment of the present invention is used to solve the technical problem of low production efficiency and static electricity accumulation caused by the inability to quickly optimize the production line layout and equipment control parameters due to changes in production tasks or fluctuations in process conditions, and achieves the technical effect of optimizing the production process and equipment layout through the simulation system, reducing static electricity accumulation, and improving production efficiency and product quality. The ultra-fine fiber production process simulation test platform includes: a task acquisition module 1, an arrangement scheme acquisition module 2, a model construction module 3, a long time series acquisition module 4, an analysis and certification module 5, a target optimization module 6, and a test result output module 7.

[0043] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0044] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. Ultrafine fiber production process simulation test platform, characterized by: The platform includes: A task collection module is used to collect production tasks of target ultrafine fiber yarns and obtain production task information; A layout scheme acquisition module, used to perform process flow analysis and production equipment configuration based on the production task information, and acquire a production line layout scheme, wherein the production line layout scheme includes multiple process flow nodes, multiple production equipment sets, and multiple production equipment control parameter sets, each process flow node corresponds to a production equipment set, and the multiple production equipment sets include multiple production equipment location identifier sets; A model building module, used to use a simulation system to build a three-dimensional model according to the multiple process nodes, multiple production equipment sets, multiple production equipment control parameter sets and multiple production equipment position identification sets to obtain a fiber production line model; A long time series acquisition module is used to call the fiber production line model to perform a production simulation test, and perform a long time series acquisition of indicator values ​​according to a preset monitoring indicator set during the test process to obtain multiple indicator value sequence sets, wherein each indicator value sequence set corresponds to a monitoring indicator; An analysis and certification module, used to use the plurality of indicator value sequence sets as preliminary certification analysis data for simulation test, to perform analysis and certification, and if the certification is passed, to perform an electrostatic accumulation test in combination with the fiber production line model; A target optimization module, for performing target optimization on the distribution positions of a plurality of production equipment sets of the fiber filament production line model based on the result of the electrostatic accumulation test if the electrostatic accumulation test fails, to obtain a plurality of optimized production equipment position identification sets; The test result output module is used to output multiple process flow nodes, multiple production equipment sets and multiple optimized production equipment position identification sets as simulation test results.

2. The ultra-fine fiber production process simulation test platform according to claim 1, characterized in that: Based on the production task information, process flow analysis and production equipment configuration are performed to obtain a production line arrangement plan, wherein the production line arrangement plan includes multiple process flow nodes, multiple production equipment sets, and multiple production equipment control parameter sets, including: Perform data mining based on the production task information to determine multiple mining production line arrangement plans; Traversing the plurality of mining production line arrangement schemes to perform production log retrieval, and obtaining a plurality of mining production line production log sets; Performing scheme feasibility evaluation based on the multiple mining production line production log sets to obtain multiple scheme feasibility factors; Derivative optimization is performed based on the feasibility factors of the multiple schemes and the multiple mining production line arrangement schemes to determine the production line arrangement scheme.

3. The ultra-fine fiber production process simulation test platform according to claim 2, characterized in that: Derivative optimization is performed based on the multiple scheme feasibility factors and multiple mining production line arrangement schemes to determine the production line arrangement scheme, including: Constructing a solution evaluation identifier based on the plurality of mining production line production log sets and the plurality of solution feasibility factors; Performing hierarchical clustering on the multiple mining production line arrangement schemes according to the sizes of the scheme feasibility factors to obtain multiple cluster mining production line arrangement scheme sets, wherein the multiple cluster mining production line arrangement scheme sets include multiple cluster scheme feasibility factors; Taking the cluster mining production line arrangement solution set corresponding to the maximum value among the plurality of cluster solution feasibility factors as the derived object set; The solution evaluation identifier is combined to perform solution derivation optimization on the derived object set to determine the production line arrangement solution.

4. The ultra-fine fiber production process simulation test platform according to claim 3, characterized in that: Combining the scheme evaluation identifier with the derived object set to perform scheme derivation optimization to determine the production line arrangement scheme, including: Extracting a derived scheme feasibility factor set corresponding to the derived object set, and taking a derived object corresponding to a maximum value in the derived scheme feasibility factor set as a directional derived object, and taking the remaining derived objects as a follow-up derived object set; Taking the direction derived object as the target of solution derivation optimization, optimizing and adjusting the follow-up derived object set according to a preset adjustment range to obtain multiple adjusted follow-up derived object sets; Using the solution evaluation identifier to perform solution feasibility evaluation on the plurality of adjustment-following derivative object sets, and obtaining a plurality of adjustment-following derivative solution feasibility factors; Determine whether there is a derivative scheme feasibility factor greater than the direction derivative object among the multiple adjustment-following derivative scheme feasibility factors. If so, update the adjustment-following derivative object corresponding to the maximum value of the multiple adjustment-following derivative scheme feasibility factors to the direction derivative object, and continue to perform scheme derivation optimization until the preset number of times is met. The direction derivative object corresponding to the maximum value of the derivative scheme feasibility factor during the optimization process is used as the production line arrangement plan.

5. The ultra-fine fiber production process simulation test platform according to claim 1, characterized in that: The plurality of indicator value sequence sets are used as preliminary certification analysis data for simulation testing, and analysis certification is performed, including: Traversing the plurality of indicator value sequence sets to perform fluctuation analysis and determine a plurality of first authentication factors; Calculating the mean of the plurality of indicator value sequence sets to determine a plurality of second authentication factors; An analysis authentication is performed based on the plurality of first authentication factors and the plurality of second authentication factors to obtain an authentication result.

6. The ultra-fine fiber production process simulation test platform according to claim 1, characterized in that: Based on the static electricity accumulation test results, the distribution positions of multiple production equipment sets of the fiber yarn production line model are optimized to obtain multiple optimized production equipment position identification sets, including: Extracting an electric field strength distribution diagram from the static electricity accumulation test result, wherein the electric field strength distribution diagram is used to describe the static electricity distribution during the operation of the fiber production line model; Based on the electric field strength distribution diagram, M distribution points with electric field strengths located in the first M positions are obtained, wherein the M distribution points include M distribution center electric field strengths, and M is an integer greater than or equal to 1; Performing electric field strength neighborhood edge recognition on the M distribution points to determine M electric field strength neighborhoods, wherein the M electric field strength neighborhoods include M neighborhood areas; Based on the M distribution center electric field intensities, the M distribution points and the M electric field intensity neighborhoods, an optimization target of the distribution position is identified to determine Q optimization target points, where Q is a positive integer less than or equal to M; The distribution position is optimized and adjusted based on the Q optimization target points, and the distribution positions of the plurality of production equipment sets are adaptively adjusted according to the adjusted Q optimization production equipment position identifiers to obtain the plurality of optimization production equipment position identifiers.

7. The ultra-fine fiber production process simulation test platform according to claim 6, characterized in that: include: Taking the M distribution points as the identification starting point, iterate in the electric field strength distribution map according to the preset identification step size, and add the points whose electric field strength difference with the M distribution center electric field strength of the M distribution points is within the preset difference threshold into the corresponding electric field strength neighborhood until the preset identification stop condition is met, so as to obtain the M electric field strength neighborhoods, wherein the preset identification stop condition is that the electric field strength difference between the point and the electric field strength difference between the M distribution center electric field strengths is less than the preset difference threshold.

8. The ultra-fine fiber production process simulation test platform according to claim 1, characterized in that: The preset monitoring index set includes tensile strength, winding speed and fiber diameter.

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