Polyester wool top production optimization method and system based on multivariate data analysis

Abstract

The invention relates to the technical field of polyester wool top production, in particular to a polyester wool top production optimization method based on multivariate data analysis, which optimizes the quality control of polyester wool tops through feedback control and improves the strength and elasticity indexes of the polyester wool tops. The method comprises the steps of obtaining fluidity indexes of raw materials according to raw material information in a polyester wool top spinning stage; the compatibility index of the auxiliary is obtained according to the auxiliary material information in the polyester wool top spinning stage; collecting a temperature data set of the whole process of the polyester wool top spinning stage; obtaining a raw material influence strength predicted value according to the temperature data set and the fluidity index corresponding to the raw material; according to the temperature data set and the compatibility index corresponding to the auxiliary material, obtaining an auxiliary influence strength prediction value; fusing the raw material influence strength prediction value and the assistant influence strength prediction value to obtain a spinning influence strength prediction index; and correcting the draw ratio of the polyester wool top in the drawing stage according to the spinning influence strength prediction index, and executing the corrected draw ratio to produce the polyester wool top.

Description

Technical Field

[0001] The invention relates to the technical field of polyester tops, and in particular to a polyester top production optimization method and system based on multivariate data analysis. Background Art

[0002] Polyester tops are a basic material widely used in the textile industry. They are usually blended with wool tops to produce fabrics such as woolen cloth, and are mostly used in clothing products with high quality requirements such as suits and military uniforms. The production process of polyester tops involves multiple key links, including fusion spinning, fiber drafting, cutting and shaping, and carding. The process parameters of these links and their coordination play a decisive role in the quality of the final product, especially the strength and elasticity of polyester tops.

[0003] After a long period of production practice, a common problem in the industry is that there is a lack of linkage control of each link in the entire polyester top production process, resulting in insufficient coordination between the various production links. For example, the entire production process is executed according to the pre-established process flow and process parameters; this situation makes it difficult to optimize the quality of the finished product in real time in actual production, increasing production costs and the risk of defective products. In addition, the traditional process flow and parameter settings rely on experience and lack scientific analysis and adjustment mechanisms, which further limits the flexibility and adaptability of production. Summary of the invention

[0004] To this end, the technical problem to be solved by the present invention is to overcome the difficulty in real-time optimization of the quality of polyester top finished products in the prior art, and to provide a polyester top production optimization method and system based on multivariate data analysis, which optimizes the quality control of polyester tops through feedback control and improves the strength and elasticity indicators of polyester tops.

[0005] In the first aspect, in order to solve the above technical problems, the present invention provides a polyester top production optimization method based on multivariate data analysis, the polyester top production includes a spinning stage and a drafting stage, and the polyester top production optimization method includes:

[0006] Obtaining raw material information of the polyester sliver spinning stage, and performing fluidity analysis on the raw material information to obtain a fluidity index of the raw material; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature;

[0007] Acquiring auxiliary material information of the polyester sliver spinning stage, and performing compatibility analysis on the auxiliary material information to obtain a compatibility index of the auxiliary; wherein the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change;

[0008] Collecting temperature data of the entire process of the polyester sliver spinning stage to obtain a temperature data set;

[0009] Execute polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the strength prediction value affected by the raw material;

[0010] Execute polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material influence strength prediction value;

[0011] The raw material influence strength prediction value and the auxiliary agent influence strength prediction value are integrated to obtain a spinning influence strength prediction index;

[0012] The drafting ratio of the polyester sliver in the drafting stage is corrected according to the spinning influence strength prediction index, and the corrected drafting ratio is used to produce the polyester sliver.

[0013] In one embodiment of the present invention, the method further comprises correcting the drafting speed of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and producing the polyester sliver according to the corrected drafting speed.

[0014] In one embodiment of the present invention, the drafting ratio of the polyester sliver in the drafting stage is corrected according to the spinning influence strength prediction index, including:

[0015] The spinning-affecting strength prediction index includes an improving strength index and a reducing strength index;

[0016] When the spinning strength prediction index is an improved strength index, reducing the draft ratio of the polyester sliver;

[0017] When the spinning-affecting strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is increased.

[0018] In one embodiment of the present invention, it further includes:

[0019] Constructing a draft ratio-drafting speed table; wherein the draft ratio-drafting speed table is used to describe the mapping relationship between the draft ratio and the drafting speed, and defining the drafting speed corresponding to the draft ratio in the draft ratio-drafting speed table as the target drafting speed;

[0020] When the spinning-affecting strength prediction index is an improved strength index, the drafting ratio of the polyester sliver is gradually reduced with a first step length until the drafting speed reaches the target drafting speed.

[0021] In one embodiment of the present invention, when the spinning-affecting strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is gradually increased in a second step until the drafting speed reaches the target drafting speed.

[0022] In one embodiment of the present invention, the strength prediction of polyester top is performed according to the temperature data set and the fluidity index corresponding to the raw material to obtain the strength prediction value affected by the raw material, including:

[0023] Constructing a spinning Bayesian network model;

[0024] Analyze the temperature data set and the fluidity index based on the spinning Bayesian network model to obtain the viscosity change vector of the raw material at each process node in the spinning stage;

[0025] According to the viscosity of the raw material at the first node and the viscosity change vector, the viscosity of the raw material at each process node in the spinning stage is obtained;

[0026] The strength of the polyester spinning is predicted according to the raw material viscosity of each node, and the predicted value of the strength affected by the raw materials is obtained.

[0027] In one embodiment of the present invention, the strength prediction of polyester top is performed according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material-affected strength prediction value, including:

[0028] Analyzing the temperature data set and the compatibility index based on the spinning Bayesian network model to obtain the transparency change vector of the additive at each process node in the spinning stage;

[0029] According to the transparency of the first node and the transparency change vector, the transparency of the additive at each process node in the spinning stage is obtained;

[0030] According to the transparency of the auxiliary agent at each process node in the spinning stage, the strength of the polyester spinning is predicted, and the predicted value of the strength affected by the auxiliary agent is obtained.

[0031] In one embodiment of the present invention, the spinning Bayesian network model is trained according to the layered freezing signal;

[0032] The spinning Bayesian network model includes a first network layer and a second network layer, the first network layer is connected to the second network layer, the first network layer is used to analyze the temperature data set and the fluidity index to obtain the viscosity change vector of the raw material at each process node in the spinning stage; the second network layer is used to analyze the temperature data set and the compatibility index to obtain the transparency change vector of the additive at each process node in the spinning stage;

[0033] The spinning Bayesian network model selectively outputs one of the first network layer and the second network layer according to the layered freezing signal.

[0034] In one embodiment of the present invention, the spinning influence strength prediction index is obtained based on the weighted average fusion of the raw material influence strength prediction value and the auxiliary agent influence strength prediction value.

[0035] In the second aspect, in order to solve the above technical problems, the present invention also provides a polyester top production optimization system based on multivariate data analysis. The polyester top production includes a spinning stage and a drafting stage. The polyester top production optimization system includes:

[0036] A data acquisition module, which is used to obtain raw material information and auxiliary material information of the polyester wool top spinning stage; and to collect temperature data of the entire process of the polyester wool top spinning stage to obtain a temperature data set;

[0037] An analysis module, which is used to perform fluidity analysis on the raw material information to obtain the fluidity index of the raw material; and to perform compatibility analysis on the auxiliary material information to obtain the compatibility index of the auxiliary; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature change; and the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change;

[0038] A prediction module, which is used to perform polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the raw material influence strength prediction value; and perform polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary influence strength prediction value;

[0039] A fusion module, which is used to fuse the raw material influence strength prediction value and the auxiliary agent influence strength prediction value to obtain a spinning influence strength prediction index;

[0040] The optimization module is used to correct the drafting ratio of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and execute the corrected drafting ratio to produce the polyester sliver.

[0041] The above technical solution of the present invention has the following beneficial effects compared with the prior art:

[0042] The polyester top production optimization method and system based on multivariate data analysis of the present invention realize feedback control of the polyester top production process through data drive, combine the analysis of raw materials, additives and temperature data, accurately predict the influence of the spinning stage on the strength of the polyester top to dynamically adjust the draft ratio, realize production optimization based on real-time feedback data, significantly improve the strength and elasticity of the polyester top, so that the final product can better meet the market demand; in addition, the process is continuously optimized through real-time feedback, the generation of defective products is reduced, thereby improving production efficiency and reducing material waste and production costs.

[0043] In summary, the polyester top production optimization method and system based on multivariate data analysis described in this invention optimize the quality control of polyester tops through comprehensive data analysis and feedback control, which not only improves the production efficiency, but also significantly improves the strength and elasticity indicators of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein

[0045] Figure 1 It is a flow chart of a polyester top production optimization method based on multivariate data analysis in a preferred embodiment of the present invention;

[0046] Figure 2 It is a structural block diagram of a polyester top production optimization system based on multivariate data analysis in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0047] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention.

[0048] Embodiment 1

[0049] The whole process of polyester top production includes fusion spinning (hereinafter referred to as "spinning stage"), fiber drafting (hereinafter referred to as "drafting stage"), cutting and shaping, and carding. Among them, fusion spinning and fiber drafting are the most critical links affecting the formation of polyester tops. The specific process of the spinning stage and the drafting stage of polyester top production is described below:

[0050] 1. Spinning stage:

[0051] Usually polyester (such as polyethylene terephthalate, PET) particles are used as raw materials, which are dried to remove moisture and ensure the fluidity of the material during the melting process; then heated to melt, the polyester particles are heated to a certain temperature (usually 240-290°C) in a high-temperature melting device to transform it from a solid state to a flowing molten state; then it enters the spinning process, and the molten polyester is extruded from the nozzle to form a silk thread; the silk thread is quickly solidified in the cooling air or coolant to form a preliminary fiber structure. Each process node in the spinning stage is related to temperature, for example, drying, melting, spinning and cooling are all related to the temperature acting on the raw material; the inventor of this application found through uninterrupted production experiments that temperature affects the viscosity of the raw material by affecting its fluidity, and the difference in viscosity of the raw material at each node in the spinning stage ultimately affects the strength of the polyester wool top.

[0052] In addition, in order to improve the relevant properties of polyester tops, additives need to be added; for example, antistatic agents, plasticizers, colorants and other additives are added as needed to improve the performance of polyester tops. As the spinning stage progresses, the solid additives melt into a fluid state and react with the raw materials to participate in the performance improvement of the polyester tops. The inventors of this application have found through uninterrupted production tests that temperature affects the transparency of the additives by affecting their compatibility, and the transparency of the additives at each node in the spinning stage affects their role in participating in the performance improvement, which ultimately affects the strength of the polyester tops.

[0053] 2. Drafting stage

[0054] The fibers wound up in the spinning stage are loosened to ensure that they will not be knotted during the drafting process; the initially formed fibers are then stretched through the drafting equipment, and the strength and elasticity of the fibers are affected by setting the drafting ratio and drafting speed. The drafted fibers are then shaped by the equipment to maintain their cross-section and strength.

[0055] Reference Figure 1 As shown, the embodiment of the present invention discloses a polyester top production optimization method based on multivariate data analysis, wherein the polyester top production optimization method involves the spinning stage and the drafting stage of polyester top production, and the polyester top production optimization method includes:

[0056] The raw material information of the polyester sliver spinning stage is obtained, and the fluidity analysis is performed on the raw material information to obtain the fluidity index of the raw material; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature change.

[0057] In specific application scenarios, the fluidity index of various raw materials is obtained through preliminary tests; when there are multiple raw materials, the fluidity index of each raw material is obtained through tests, and a set of raw material fluidity indexes is formed to provide basic data for subsequent production optimization. The fluidity index is also used to optimize the temperature in the spinning stage to ensure that the raw materials have appropriate fluidity during spinning.

[0058] Acquiring auxiliary material information of the polyester sliver spinning stage, and performing compatibility analysis on the auxiliary material information to obtain a compatibility index of the auxiliary; wherein the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change;

[0059] In specific application scenarios, pre-tests are conducted to obtain the compatibility indexes of various additives; when there are multiple additives, the compatibility index of each additive is obtained through tests, and a set of additive fluidity indexes is formed to provide basic data for subsequent production optimization. Compatibility indexes are also used to understand the performance of additives at different temperatures, thereby optimizing the additive addition process and ensuring good interaction between additives and raw materials.

[0060] Collecting temperature data of the entire process of the polyester sliver spinning stage to obtain a temperature data set;

[0061] In specific application scenarios, the temperature data of the entire spinning process of polyester sliver is collected in real time based on the temperature sensing module to provide time series data for subsequent strength prediction.

[0062] Execute polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the strength prediction value affected by the raw material;

[0063] In specific application scenarios, the real-time collected temperature data set is combined with the fluidity index of the current raw materials to predict the impact of the viscosity difference of the raw materials on the polyester spinning strength during the spinning stage, providing a decision basis for the subsequent dynamic adjustment of the draft ratio. In addition, the selection and use of raw materials can be optimized based on the predicted impact on the polyester spinning strength.

[0064] Execute polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material influence strength prediction value;

[0065] In specific application scenarios, based on the real-time collected temperature data set and combined with the compatibility index corresponding to the current additives, the influence of the transparency difference of the additives on the spinning strength of polyester during the spinning stage is predicted, providing a decision-making basis for the subsequent dynamic adjustment of the draft ratio.

[0066] The raw material influence strength prediction value and the auxiliary agent influence strength prediction value are integrated to obtain a spinning influence strength prediction index;

[0067] In specific application scenarios, a comprehensive spinning strength prediction index is formed by integrating the strength prediction values ​​of raw materials and additives, so as to make more scientific adjustment decisions.

[0068] The draft ratio of the polyester sliver in the drafting stage is corrected according to the spinning strength prediction index, and the revised draft ratio is used to produce the polyester sliver. The revised draft ratio realizes production optimization based on real-time feedback data, and can flexibly adjust production parameters to adapt to actual conditions, thereby improving the strength and elasticity of the polyester sliver.

[0069] The polyester top production optimization method and system based on multivariate data analysis of the present invention realize feedback control of the polyester top production process through data drive, combine the analysis of raw materials, additives and temperature data, accurately predict the influence of the spinning stage on the strength of the polyester top to dynamically adjust the draft ratio, realize production optimization based on real-time feedback data, significantly improve the strength and elasticity of the polyester top, so that the final product can better meet the market demand; in addition, the process is continuously optimized through real-time feedback, the generation of defective products is reduced, thereby improving production efficiency and reducing material waste and production costs.

[0070] In other embodiments of the present invention, the method further comprises correcting the drafting speed of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and producing the polyester sliver according to the corrected drafting speed.

[0071] In specific application scenarios, the drafting ratio and drafting speed are corrected simultaneously according to the prediction indicators of spinning strength, and the production process is further optimized to improve the strength and elasticity of polyester tops.

[0072] Further, the drafting ratio of the polyester sliver in the drafting stage is corrected according to the spinning influence strength prediction index, including:

[0073] The spinning-affecting strength prediction index includes an improving strength index and a reducing strength index;

[0074] In a specific application scenario, the process flow of polyester tops and the process parameters of each process flow are preset, and the strength of the polyester tops produced based on the set process parameters is tested to obtain a strength reference value. When the other stages except the spinning stage and the drafting stage remain unchanged, the polyester tops are produced based on the above method, and the strength prediction value is obtained according to the spinning-affected strength prediction index, and the strength prediction value is compared with the strength reference value. If the strength prediction value is less than the strength reference value, the spinning-affected strength prediction index is a reduced strength index; if the strength prediction value is greater than the strength reference value, the spinning-affected strength prediction index is an increased strength index. If the strength prediction value is equal to the strength reference value, the current draft ratio and drafting speed are maintained to execute polyester top production.

[0075] It should be noted that the drafting ratio refers to the ratio of the fiber length before and after drafting, which is characterized as follows: Among them, Dr is the draft ratio; L 牵伸前 is the length when not stretched; L 牵伸后 It is the length after drawing. Drawing speed refers to the speed of the fiber passing through the drawing equipment.

[0076] The draft ratio can characterize the compactness of the internal structure of the fiber, thus affecting the strength and modulus of the fiber.

[0077] When the spinning-affected strength prediction index is an improved strength index, the draft ratio of the polyester sliver is reduced; when the spinning-affected strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is increased.

[0078] In a specific application scenario, when the spinning-affected strength prediction index is an improved strength index, it indicates that the fiber structure obtained by spinning already has a relatively high initial strength, and the high strength is balanced by reducing the draft ratio to avoid unnecessary risk of breakage; when the spinning-affected strength prediction index is a reduced strength index, it indicates that the initial strength of the fiber structure obtained by spinning is low, and the fiber strength is increased by increasing the draft ratio to ensure that the final product meets the required performance standards; thereby ensuring that the produced polyester tops will neither break due to excessive drafting nor suffer from insufficient strength due to insufficient drafting.

[0079] Furthermore, it also includes constructing a drafting ratio-drafting speed table; wherein the drafting ratio-drafting speed table is used to describe the mapping relationship between the drafting ratio and the drafting speed, and the drafting speed corresponding to the drafting ratio in the drafting ratio-drafting speed table is defined as the target drafting speed;

[0080] In specific application scenarios, the draft ratio and the draft speed have a certain intrinsic correlation. The control logic of the draft ratio is to achieve the expected fiber properties, such as strength, modulus, fineness, etc. The control logic of the draft speed is to ensure that the fiber can be evenly stressed during the drafting process to avoid fiber breakage due to too fast speed or inefficiency due to too slow speed. Within a certain range, the draft ratio and the draft speed have a positive correlation, that is, increasing the draft ratio and speeding up the draft speed can improve production efficiency; reducing the draft ratio and speeding down can avoid fiber breakage. Based on historical production experience, a draft ratio-drafting speed table is established to provide data support for subsequent decision-making.

[0081] When the spinning-affecting strength prediction index is an improved strength index, the drafting ratio of the polyester sliver is gradually reduced with a first step length until the drafting speed reaches the target drafting speed.

[0082] In a specific application scenario, when the spinning strength prediction index is the strength improvement index, production is performed with the goal of reducing the draft ratio, and the target total reduction of the draft ratio is determined according to the actual strength improvement index, and the draft ratio is gradually reduced to the target total reduction in a certain step length, so as to slowly change the draft ratio and avoid the influence of the sudden change of the draft ratio on other properties. As the draft ratio decreases, the draft speed is correspondingly reduced. Since data transmission and processing have a certain lag, in order to overcome the adverse effect of the lag on production control, when the changed draft ratio reaches the target draft speed corresponding to it in the draft ratio-draft speed table, the draft ratio adjustment is stopped to avoid over-adjustment.

[0083] In one embodiment of the present invention, when the spinning-affecting strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is gradually increased in a second step until the drafting speed reaches the target drafting speed.

[0084] In a specific application scenario, when the spinning-affecting strength prediction index is a strength reduction index, production is performed with the goal of increasing the draft ratio, and the target total increase of the draft ratio is determined according to the actual strength reduction index, and the draft ratio is gradually increased to the target total increase in a certain step length, so as to slowly change the draft ratio and avoid the influence of the sudden change of the draft ratio on other properties. As the draft ratio increases, the draft speed is correspondingly accelerated. Since data transmission and processing have a certain lag, in order to overcome the adverse effect of the lag on production control, when the changed draft ratio reaches the target draft speed corresponding to it in the draft ratio-draft speed table, the draft ratio adjustment is stopped to avoid over-adjustment.

[0085] In a further embodiment of the present invention, the strength prediction of polyester top is performed according to the temperature data set and the fluidity index corresponding to the raw material to obtain the strength prediction value affected by the raw material, including:

[0086] Constructing a spinning Bayesian network model;

[0087] In specific application scenarios, relevant variables are determined, including temperature data, fluidity indicators of raw material components, compatibility indicators of auxiliary materials, strength of polyester tops, etc.; a spinning Bayesian network model is established based on domain knowledge, in which each node represents a variable and the edge represents the conditional dependency between variables. The relationship between fluidity indicators and temperature, raw material components and polyester top strength, as well as the relationship between compatibility indicators and temperature, auxiliary components and polyester top strength are determined; conditional probabilities are estimated from historical data sets to fill in the parameters in the spinning Bayesian network.

[0088] Analyze the temperature data set and the fluidity index based on the spinning Bayesian network model to obtain the viscosity change vector of the raw material at each process node in the spinning stage;

[0089] In specific application scenarios, the spinning lobe network is used to conduct a comprehensive analysis of the temperature data set and raw material fluidity indicators to obtain the viscosity change vector of the raw materials in each process node; the viscosity change vector describes the viscosity change between the next process node and the previous process node.

[0090] According to the viscosity of the raw material at the first node and the viscosity change vector, the viscosity of the raw material at each process node in the spinning stage is obtained;

[0091] In a specific application scenario, the raw material viscosity of the second node is obtained based on the raw material viscosity of the first node (i.e., the first node) and the viscosity change between the first node and the second node. The raw material viscosity of the third node is obtained based on the raw material viscosity of the second node and the viscosity change between the second node and the third node. And so on, to obtain the raw material viscosity of all process nodes in the spinning stage.

[0092] The strength of the polyester spinning is predicted according to the raw material viscosity of each node, and the predicted value of the strength affected by the raw materials is obtained.

[0093] In specific application scenarios, the spinning Bayesian network model is trained according to historical data to obtain a strength prediction model. The raw material viscosity of all process nodes in the current raw material spinning stage is learned through the strength prediction model to obtain the predicted strength of polyester spinning produced by the current raw material spinning. The predicted strength is compared with the actual strength of historical data to obtain the predicted value of raw material impact strength.

[0094] In one embodiment of the present invention, the strength prediction of polyester top is performed according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material-affected strength prediction value, including:

[0095] Analyzing the temperature data set and the compatibility index based on the spinning Bayesian network model to obtain the transparency change vector of the additive at each process node in the spinning stage;

[0096] In the specific application scenario, the spinning lobe network is used to conduct a comprehensive analysis of the temperature data set and the additive compatibility index to obtain the transparency change vector of the additive in each process node; the transparency change vector describes the transparency change between the next process node and the previous process node.

[0097] According to the transparency of the first node and the transparency change vector, the transparency of the additive at each process node in the spinning stage is obtained;

[0098] In a specific application scenario, the additive transparency of the second node is obtained based on the additive transparency of the first node (i.e., the first node) and the transparency change between the first node and the second node. The additive transparency of the third node is obtained based on the additive transparency of the second node and the transparency change between the second node and the third node. And so on, to obtain the additive transparency of all process nodes in the spinning stage.

[0099] According to the transparency of the auxiliary agent at each process node in the spinning stage, the strength of the polyester spinning is predicted, and the predicted value of the strength affected by the auxiliary agent is obtained.

[0100] In specific application scenarios, the strength prediction model is used to learn the transparency of the original additives at all process nodes in the current auxiliary material spinning stage, obtain the predicted strength of polyester spinning produced by the current auxiliary spinning, compare the predicted strength with the actual strength of historical data, and obtain the predicted value of the additive impact strength.

[0101] Further, the spinning Bayesian network model is trained according to the layered freezing signal;

[0102] The spinning Bayesian network model includes a first network layer and a second network layer, the first network layer is connected to the second network layer, the first network layer is used to analyze the temperature data set and the fluidity index to obtain the viscosity change vector of the raw material at each process node in the spinning stage; the second network layer is used to analyze the temperature data set and the compatibility index to obtain the transparency change vector of the additive at each process node in the spinning stage;

[0103] The spinning Bayesian network model selectively outputs one of the first network layer and the second network layer according to the layered freezing signal.

[0104] In specific application scenarios, by freezing signals in layers and selectively outputting features of different network layers, features of different network layers can be selected according to specific needs during the training process, thereby training in stages and gradually adjusting the intensity prediction model to better adapt to data types of different complexities. This enables better feature representation to be obtained when processing various data, thereby further improving the accuracy of intensity prediction.

[0105] In one embodiment of the present invention, the spinning influence strength prediction index is obtained based on the weighted average fusion of the raw material influence strength prediction value and the auxiliary agent influence strength prediction value.

[0106] In specific application scenarios, a specific weight is assigned to the raw material influence intensity prediction value and the auxiliary agent influence intensity prediction value according to their contribution to the final result, and each is multiplied by the corresponding weight and then added together; the weighted average fusion to obtain the spinning influence intensity prediction index can improve the prediction accuracy and enhance the robustness.

[0107] Embodiment 2

[0108] like Figure 2 As shown, the embodiment of the present invention discloses a polyester top production optimization system based on multivariate data analysis. The polyester top production includes a spinning stage and a drafting stage. The polyester top production optimization system includes:

[0109] A data acquisition module, which is used to obtain raw material information and auxiliary material information of the polyester wool top spinning stage; and to collect temperature data of the entire process of the polyester wool top spinning stage to obtain a temperature data set;

[0110] An analysis module, which is used to perform fluidity analysis on the raw material information to obtain the fluidity index of the raw material; and to perform compatibility analysis on the auxiliary material information to obtain the compatibility index of the auxiliary; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature change; and the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change;

[0111] A prediction module, which is used to perform polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the raw material influence strength prediction value; and perform polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary influence strength prediction value;

[0112] A fusion module, which is used to fuse the raw material influence strength prediction value and the auxiliary agent influence strength prediction value to obtain a spinning influence strength prediction index;

[0113] The optimization module is used to correct the drafting ratio of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and execute the corrected drafting ratio to produce the polyester sliver.

[0114] The polyester top production optimization system based on multivariate data analysis disclosed in the embodiment of the present invention can effectively realize the production optimization of polyester tops, and the technical effects that can be achieved are as described in the above embodiment, which will not be repeated here.

[0115] In summary, the polyester top production optimization method and system based on multivariate data analysis described in the present invention realize feedback control of the polyester top production process through data-driven, and combine the analysis of raw materials, additives and temperature data to accurately predict the influence of the spinning stage on the strength of the polyester top to dynamically adjust the draft ratio, thereby realizing production optimization based on real-time feedback data, significantly improving the strength and elasticity of the polyester top, and enabling the final product to better meet market demand; in addition, the process is continuously optimized through real-time feedback, the generation of defective products is reduced, thereby improving production efficiency, reducing material waste and production costs.

[0116] The polyester top production optimization method and system based on multivariate data analysis described herein optimize the quality control of polyester tops through comprehensive data analysis and feedback control, which not only improves production efficiency, but also significantly improves the strength and elasticity indicators of the product.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A polyester top production optimization method based on multivariate data analysis, the polyester top production includes a spinning stage and a drafting stage, characterized in that: The polyester top production optimization method comprises: Obtaining raw material information of the polyester sliver spinning stage, and performing fluidity analysis on the raw material information to obtain a fluidity index of the raw material; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature; Acquiring auxiliary material information of the polyester sliver spinning stage, and performing compatibility analysis on the auxiliary material information to obtain a compatibility index of the auxiliary; wherein the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change; Collecting temperature data of the entire process of the polyester sliver spinning stage to obtain a temperature data set; Execute polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the strength prediction value affected by the raw material; Execute polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material influence strength prediction value; The raw material influence strength prediction value and the auxiliary agent influence strength prediction value are integrated to obtain a spinning influence strength prediction index; The drafting ratio of the polyester sliver in the drafting stage is corrected according to the spinning influence strength prediction index, and the corrected drafting ratio is used to produce the polyester sliver.

2. The polyester top production optimization method based on multivariate data analysis according to claim 1, characterized in that: The method further includes correcting the drafting speed of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and producing the polyester sliver according to the corrected drafting speed.

3. The polyester top production optimization method based on multivariate data analysis according to claim 1 or 2, characterized in that: Correcting the drafting ratio of the polyester sliver in the drafting stage according to the spinning influence strength prediction index includes: The spinning-affecting strength prediction index includes an improving strength index and a reducing strength index; When the spinning strength prediction index is an improved strength index, reducing the draft ratio of the polyester sliver; When the spinning-affecting strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is increased.

4. The polyester top production optimization method based on multivariate data analysis according to claim 3, characterized in that: Also includes, Constructing a draft ratio-drafting speed table; wherein the draft ratio-drafting speed table is used to describe the mapping relationship between the draft ratio and the drafting speed, and defining the drafting speed corresponding to the draft ratio in the draft ratio-drafting speed table as the target drafting speed; When the spinning-affecting strength prediction index is an improved strength index, the drafting ratio of the polyester sliver is gradually reduced with a first step length until the drafting speed reaches the target drafting speed.

5. The polyester top production optimization method based on multivariate data analysis according to claim 4, characterized in that: When the spinning-affecting strength prediction index is a reduced strength index, the draft ratio of the polyester sliver is gradually increased in a second step until the drafting speed reaches the target drafting speed.

6. The polyester top production optimization method based on multivariate data analysis according to claim 1, characterized in that: Execute polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the raw material-affected strength prediction value, including: Constructing a spinning Bayesian network model; Analyze the temperature data set and the fluidity index based on the spinning Bayesian network model to obtain the viscosity change vector of the raw material at each process node in the spinning stage; According to the viscosity of the raw material at the first node and the viscosity change vector, the viscosity of the raw material at each process node in the spinning stage is obtained; The strength of the polyester spinning is predicted according to the raw material viscosity of each node, and the predicted value of the strength affected by the raw materials is obtained.

7. The polyester top production optimization method based on multivariate data analysis according to claim 6, characterized in that: Execute the polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary material-affected strength prediction value, including: Analyzing the temperature data set and the compatibility index based on the spinning Bayesian network model to obtain the transparency change vector of the additive at each process node in the spinning stage; According to the transparency of the first node and the transparency change vector, the transparency of the additive at each process node in the spinning stage is obtained; According to the transparency of the auxiliary agent at each process node in the spinning stage, the strength of the polyester spinning is predicted, and the predicted value of the strength affected by the auxiliary agent is obtained.

8. The polyester top production optimization method based on multivariate data analysis according to claim 7, characterized in that: Training the spinning Bayesian network model according to the hierarchical freezing signal; The spinning Bayesian network model includes a first network layer and a second network layer, the first network layer is connected to the second network layer, the first network layer is used to analyze the temperature data set and the fluidity index to obtain the viscosity change vector of the raw material at each process node in the spinning stage; the second network layer is used to analyze the temperature data set and the compatibility index to obtain the transparency change vector of the additive at each process node in the spinning stage; The spinning Bayesian network model selectively outputs one of the first network layer and the second network layer according to the layered freezing signal.

9. The polyester top production optimization method based on multivariate data analysis according to claim 1, characterized in that: The spinning influence intensity prediction index is obtained based on the weighted average fusion of the raw material influence intensity prediction value and the auxiliary agent influence intensity prediction value.

10. A polyester top production optimization system based on multivariate data analysis, the polyester top production includes a spinning stage and a drafting stage, characterized in that: The polyester top production optimization system includes: A data acquisition module, which is used to obtain raw material information and auxiliary material information of the polyester wool top spinning stage; and to collect temperature data of the entire process of the polyester wool top spinning stage to obtain a temperature data set; An analysis module, which is used to perform fluidity analysis on the raw material information to obtain the fluidity index of the raw material; and to perform compatibility analysis on the auxiliary material information to obtain the compatibility index of the auxiliary; wherein the fluidity index is used to describe the relationship between the viscosity of the raw material and the temperature change; and the compatibility index is used to describe the relationship between the transparency of the auxiliary and the temperature change; A prediction module, which is used to perform polyester top strength prediction according to the temperature data set and the fluidity index corresponding to the raw material to obtain the raw material influence strength prediction value; and perform polyester top strength prediction according to the temperature data set and the compatibility index corresponding to the auxiliary material to obtain the auxiliary influence strength prediction value; A fusion module, which is used to fuse the raw material influence strength prediction value and the auxiliary agent influence strength prediction value to obtain a spinning influence strength prediction index; The optimization module is used to correct the drafting ratio of the polyester sliver in the drafting stage according to the spinning influence strength prediction index, and execute the corrected drafting ratio to produce the polyester sliver.

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