High-elastic warm-keeping yoga clothes fabric with double-layer composite structure and preparation method of high-elastic warm-keeping yoga clothes fabric

Abstract

The invention provides a high-elastic warm-keeping yoga clothes fabric with a double-layer composite structure and a preparation method thereof, and belongs to the technical field of preparation management, and the preparation method specifically comprises the following steps: determining concerned defect types in matched production batches according to distribution data of quality defect data in different matched production batches, and determining the concerned defect types in the matched production batches; determining concerned production equipment in the production equipment by utilizing concerned defect types, determining defect associated production batches on the basis of the concerned defect types, and obtaining change similar conditions of real-time monitoring data of the concerned production equipment and monitoring data in the defect associated production batches; and quality defect data of concerned defect types in different defect association generation batches are combined to determine a parameter adjustment processing strategy of the preparation equipment, so that the production quality and the equipment parameter adjustment processing efficiency are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of preparation management, and particularly relates to a highly elastic and warm yoga clothing fabric with a double-layer composite structure and a preparation method thereof. Background Art

[0002] The double-layer composite structure yoga clothing realizes optimization in breathability, warmth retention, and sports performance through the superposition design of different materials or processes. In specific existing technical solutions, double-sided fleece (Rulu fabric) or a composite fleece inner layer is often used to improve the heat preservation effect of the yoga clothing fabric.

[0003] In order to improve the quality of the fabric preparation process, in the patent application for invention CN202411847454.2, "A Fabric Texture Evaluation Analysis System, Method, and Storage Medium", the factor analysis method is used to simplify multi-dimensional objective attribute data into a few common factors, and a regression equation is used to ensure that these common factors can effectively reflect the main characteristics of the fabric texture.

[0004] However, during the production process of the double-layer composite structure yoga clothing fabric, due to the differences in the fabrics with composite structures, the probability of quality problems such as fabric separation and deterioration of the heat preservation effect occurring during use has a deviation. Therefore, how to generate a preparation management strategy targeted at the distribution data of quality problems and improve the fabric quality has become an urgent technical problem to be solved.

[0005] To solve the above technical problems, the present application provides a highly elastic and warm yoga clothing fabric with a double-layer composite structure and a preparation method thereof. Summary of the Invention

[0006] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0007] Specifically, in the first aspect, the present application provides a preparation method, which specifically includes:

[0008] S1 Obtain the monitoring parameters of different preparation devices of the yoga clothing fabric production line with a double-layer composite structure within a preset time period, and determine the matching production batches based on the matching situation between the monitoring parameters and different historical production batches;

[0009] S2 When it is determined that the monitoring parameters of the preparation device are normal based on the analysis results of the quality defect data in different matching production batches, proceed to the next step;

[0010] S3 Determine the types of concerned defects in the matching production batches based on the distribution data of the quality defect data in different matching production batches, and use the types of concerned defects to determine the concerned production devices in the production equipment;

[0011] S4 determines the production batches associated with defects based on the type of defect of concern, obtains the similarity of the real-time monitoring data of the production equipment of concern and the monitoring data in the production batches associated with defects, and combines the quality defect data of the type of defect of concern in different production batches associated with defects to determine the parameter adjustment processing strategy of the preparation equipment.

[0012] The beneficial effects of the present invention are as follows:

[0013] By using the distribution data of the quality defect data in different matching production batches to determine the type of defect of concern in the matching production batches, it avoids the technical problem of slow identification and processing efficiency of quality defects caused by evaluating and analyzing all types of quality defects. At the same time, by determining the type of defect of concern in the matching production batches, it fully considers the types of quality defects with a relatively high probability of occurring quality defects under the current production parameters, ensuring the reliability and quality of production preparation.

[0014] Based on the similarity of the real-time monitoring data of the production equipment of concern and the monitoring data in the production batches associated with defects, and the quality defect data of the type of defect of concern in the production batches associated with defect generation, the parameter adjustment processing strategy of the preparation equipment is determined. It realizes the accurate evaluation of the quality defect probability of the type of defect of concern from the similarity of the monitoring data of the associated production equipment and the production batches associated with defects, and further lays a foundation for determining the parameter adjustment target and adjustment method of the preparation equipment according to the difference in the quality defect probability, not only ensuring the efficiency of the adjustment process, but also ensuring the production quality.

[0015] A further technical solution is that the preset time period is determined according to the daily average output of the yoga clothing fabric production line, where the greater the daily average output, the longer the preset time period.

[0016] A further technical solution is that the matching situation is determined by the similarity of the monitoring parameters of different preparation equipment within the preset time period and the monitoring parameters within different preset time periods in different historical production batches.

[0017] A further technical solution is that the method for determining the matching production batches is as follows:

[0018] Based on the matching situation between the monitoring parameters and the historical production batches, determine the similarity of the monitoring parameters of different preparation equipment within the preset time period and the monitoring parameters within different preset time periods in the historical production batches;

[0019] Based on the similarity situation, determine the preset time periods in the historical production batches where the deviations of the monitoring parameters of different preparation equipment are all within the preset deviation range, and use them as the parameter matching time periods;

[0020] Determine whether the historical production batch is a matching production batch according to the proportion of the parameter matching period in the historical production batch.

[0021] A further technical solution is that when the proportion of the parameter matching period in the historical production batch is greater than the preset parameter matching period proportion, it is determined that the historical production batch is a matching production batch.

[0022] A further technical solution is that the method for determining the parameter adjustment processing strategy of the preparation equipment is as follows:

[0023] Based on the similarity of the changes between the real-time monitoring parameters of the associated production equipment and the monitoring data in the defect-associated production batches, determine the deviation amount between the real-time monitoring parameters of the associated production equipment within the preset period and the monitoring parameters at different times within different preset periods in the defect-associated production batches, and use the moments with the deviation amounts of the monitoring parameters all within the preset deviation amount range as parameter similarity moments;

[0024] Take the preset period with the proportion of the number of parameter similarity moments greater than the preset proportion of the number of similarity moments as the parameter similarity period, and use the proportion of the parameter similarity period in the defect-associated production batch as the parameter similarity weight coefficient;

[0025] Based on the quality defect data of the concerned defect types in different defect-associated production batches, determine the proportion of the number in different defect-associated production batches, and use the average value of the product of the proportion of the number in different defect-associated production batches and the parameter similarity weight coefficient to determine the speculated proportion of the number of different associated production equipment, and determine the determination of the parameter adjustment processing strategy of the preparation equipment based on the speculated proportion of the number of different associated production equipment.

[0026] A further technical solution is that the determination of the parameter adjustment processing strategy of the preparation equipment based on the speculated proportion of the number of different associated production equipment specifically includes:

[0027] When the speculated proportion of the number of different associated production equipment is less than the preset proportion threshold, there is no need to perform the parameter adjustment processing of the preparation equipment;

[0028] When there is an associated production equipment with a speculated proportion of the number not less than the preset proportion threshold, and when the number of associated production equipment with a speculated proportion of the number not less than the preset proportion threshold is greater than the preset number of associated equipment, take the production monitoring parameters of all the preparation equipment as the adjustment target and perform the adjustment processing of the production state of the yoga clothing fabric production line;

[0029] When the number of associated production devices with a speculation quantity ratio not less than the preset ratio threshold is not greater than the preset number of associated devices, the associated production devices with a speculation quantity ratio not less than the preset ratio threshold are taken as the adjustment targets for adjusting the production status of the yoga clothing fabric production line.

[0030] Furthermore, the method for determining the parameter adjustment processing strategy of the preparation device is as follows:

[0031] In a second aspect, the present invention provides a highly elastic and warm-keeping yoga clothing fabric with a double-layer composite structure, which adopts the above-mentioned preparation method and specifically includes: a heat-insulating layer, a connecting layer, and a blended layer;

[0032] Among them, the heat-insulating layer has a heat-insulating effect, the connecting layer is responsible for connecting the heat-insulating layer and the blended layer, and the blended layer is based on a blended material and has a high-elastic effect.

[0033] Furthermore, the blended material is a material blended from polyester fiber and nylon.

[0034] Other features and advantages will be described in the subsequent description. The objectives and other advantages of the present invention are achieved and obtained by the structures specifically pointed out in the description and the drawings.

[0035] To make the above objectives, features, and advantages of the present invention more obvious and understandable, the following specifically gives preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By referring to the accompanying drawings and describing its exemplary embodiments in detail, the above and other features and advantages of the present invention will become more obvious.

[0037] Figure 1 is a flowchart of a preparation method;

[0038] Figure 2 is a flowchart of a method for determining a matching production batch;

[0039] Figure 3 is a flowchart for determining that there is no abnormality in the production monitoring parameters of the preparation device;

[0040] Figure 4 is a flowchart of a method for determining the concerned defect types in a matching production batch. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments of this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this specification.

[0042] In this application, by analyzing the production parameters of the preparation equipment, a adjustment strategy for the production parameters of the preparation equipment is generated accordingly, ensuring the quality of the preparation of the yoga clothing fabric.

[0043] Embodiment 1

[0044] As Figure 1 shown, this application provides a preparation method, specifically including:

[0045] S1 Obtain the monitoring parameters of different preparation equipment on the yoga clothing fabric production line with a double-layer composite structure within a preset time period, and determine the matching production batches based on the matching situation between the monitoring parameters and different historical production batches.

[0046] Further, the preset time period is determined according to the daily average output of the yoga clothing fabric production line, where the greater the daily average output, the longer the preset time period.

[0047] Specifically, the matching situation is determined based on the similarity between the monitoring parameters of different preparation equipment within the preset time period and the monitoring parameters within different preset time periods in different historical production batches.

[0048] Specifically, as Figure 2 shown, the method for determining the matching production batches is:

[0049] Based on the matching situation between the monitoring parameters and the historical production batches, determine the similarity between the monitoring parameters of different preparation equipment within the preset time period and the monitoring parameters within different preset time periods in the historical production batches;

[0050] Based on the similarity situation, determine the preset time periods in the historical production batches where the deviations of the monitoring parameters of different preparation equipment are all within the preset deviation range, and use them as parameter matching time periods;

[0051] According to the proportion of the parameter matching time periods in the historical production batches, determine whether the historical production batches are matching production batches.

[0052] Further, when the proportion of the parameter matching period in the historical production batch is greater than the preset parameter matching period proportion, the historical production batch is determined as a matching production batch.

[0053] Optionally, the method for determining the matching production batch is as follows:

[0054] Based on the matching situation between the monitoring parameters and the historical production batch, determine the similarity between the monitoring parameters of different preparation devices within the preset period and the monitoring parameters within different preset periods in the historical production batch;

[0055] Based on the similarity situation, determine the average value of the deviation amounts of the monitoring parameters of the historical production batch at different preset periods and different preparation devices, and use it as the average deviation amount;

[0056] According to the average deviation amount, determine whether the historical production batch is a matching production batch.

[0057] Further, when the average deviation amounts of different preset periods are all within the preset deviation range, the historical production batch is determined as a matching production batch.

[0058] Optionally, the method for determining the matching production batch is as follows:

[0059] S11 Based on the matching situation between the monitoring parameters and the historical production batch, determine the similarity between the monitoring parameters of different preparation devices within the preset period and the monitoring parameters within different preset periods in the historical production batch. Based on the similarity situation, determine the deviation amounts of the monitoring parameters of the historical production batch at different preset periods and different preparation devices, and use the deviation amounts of the monitoring parameters of different preparation devices to determine the preparation parameter deviation coefficients within different preset periods;

[0060] S12 Based on the deviation amounts of the monitoring parameters of different preparation devices within different preset periods, determine the equipment parameter deviation amounts of different preparation devices;

[0061] S13 According to the preparation parameter deviation coefficients within different preset periods and the equipment parameter deviation amounts of different preparation devices, determine the deviation amount evaluation value of the historical production batch, and use the deviation amount evaluation value to determine whether the historical production batch is a matching production batch.

[0062] Further, when the deviation amount evaluation value is greater than the preset deviation amount threshold, it is determined that the historical production batch does not belong to the matching production batch.

[0063] Optionally, the above step S11 includes the following content:

[0064] S111 Determine the similarity between the monitoring parameters of different preparation devices within the preset time period and the monitoring parameters within different preset time periods in the historical production batch based on the matching situation between the monitoring parameters and the historical production batch. Based on the similarity, determine the average value of the deviation amounts of the monitoring parameters of the historical production batch at different preset time periods and different preparation devices. When there is no preset time period in which the average value of the deviation amount meets the requirements, it is determined that the historical production batch does not belong to the matching production batch. When there is a preset time period in which the average value of the deviation amount meets the requirements, proceed to step S112;

[0065] S112 When the proportion of the number of preset time periods in the historical production batch in which the average value of the deviation amount meets the requirements is greater than the preset parameter matching time period proportion, it is determined that the historical production batch is a matching production batch. When the proportion of the number of preset time periods in the historical production batch in which the average value of the deviation amount meets the requirements is not greater than the preset parameter matching time period proportion, proceed to step S113;

[0066] S113 Use the deviation amounts of the monitoring parameters of different preparation devices to determine the preparation parameter deviation coefficients within different preset time periods. When the average value of the preparation parameter deviation coefficients within different preset time periods meets the requirements, proceed to step S114. When the average value of the preparation parameter deviation coefficients within different preset time periods does not meet the requirements, it is determined that the historical production batch does not belong to the matching production batch;

[0067] S114 When there is a preset time period in which the preparation parameter deviation coefficient is less than the preset parameter deviation coefficient, proceed to step S115. When there is no preset time period in which the preparation parameter deviation coefficient is less than the preset parameter deviation coefficient, proceed to step S12;

[0068] S115 When the number of preset time periods in the historical production batch in which the preparation parameter deviation coefficient is less than the preset parameter deviation coefficient is greater than the preset parameter matching time period proportion, it is determined that the historical production batch is a matching production batch. When the number of preset time periods in the historical production batch in which the preparation parameter deviation coefficient is less than the preset parameter deviation coefficient is not greater than the preset parameter matching time period proportion, proceed to step S12.

[0069] Optionally, the following content is included in the above step S12:

[0070] S121 Determine the equipment parameter deviation amounts of different preparation devices based on the deviation amounts of the monitoring parameters of different preparation devices within different preset time periods. When the preparation parameter deviation amounts of different preparation devices are all within the preset deviation amount range, proceed to step S122. When there is a preparation device in which the preparation parameter deviation amount is not within the preset deviation amount range, it is determined that the historical production batch does not belong to the matching production batch;

[0071] S122 When the average value of the deviation amounts of the preparation parameters of different preparation devices is less than the set value of the deviation amount, it is determined that the historical production batch belongs to a matching production batch. When the average value of the deviation amounts of the preparation parameters of different preparation devices is not less than the set value of the deviation amount, proceed to step S123;

[0072] S123 Obtain the number of preparation devices with deviation amounts of preparation parameters less than the set value of the deviation amount. When the number of preparation devices with deviation amounts of preparation parameters less than the set value of the deviation amount meets the requirements, it is determined that the historical production batch belongs to a matching production batch. When the number of preparation devices with deviation amounts of preparation parameters less than the set value of the deviation amount does not meet the requirements, proceed to step S13.

[0073] S2 When it is determined that the monitoring parameters of the preparation device are normal based on the analysis results of the quality defect data in different matching production batches, proceed to the next step;

[0074] It should be noted that as Figure 3 shown, determining that the production monitoring parameters of the preparation device are normal specifically includes:

[0075] Based on the analysis results of the quality defect data in different matching production batches, determine the quantity proportion of different quality defect types in different matching production batches;

[0076] Based on the average value of the quantity proportions of different quality defect types in different matching production batches, determine the predicted occurrence probabilities of different quality defect types;

[0077] Based on the predicted occurrence probabilities of different quality defect types, determine whether the production monitoring parameters of the preparation device are normal.

[0078] Furthermore, when there is a quality defect type with a predicted occurrence probability greater than the preset occurrence probability threshold, it is determined that the production monitoring parameters of the preparation device are abnormal.

[0079] It can be understood that when the production monitoring parameters of the preparation device are abnormal, the production monitoring parameters of all preparation devices are taken as the adjustment target for adjusting the production state of the yoga clothing fabric production line.

[0080] Specifically, the adjustment process of the production state of the yoga clothing fabric production line specifically includes:

[0081] Based on the production monitoring parameters of different preparation devices, according to the deviation situation between the production monitoring parameters of different preparation devices and the preset optimal monitoring parameters, perform the adjustment process of the production state of the yoga clothing fabric production line.

[0082] Optionally, it is determined that there are no abnormalities in the production monitoring parameters of the preparation equipment, specifically including:

[0083] Based on the analysis results of quality defect data in different matching production batches, determine the quantity proportion of different quality defect types in different matching production batches;

[0084] Based on the quantity proportion of different quality defect types in different matching production batches, determine the matching production batches in which the quantity proportion of the existing quality defect types is greater than the preset quantity proportion, and use them as defective production batches;

[0085] Based on the quantity proportion of the defective production batches in the matching production batches, determine whether there are abnormalities in the production monitoring parameters of the preparation equipment.

[0086] It should be noted that when the quantity proportion of the defective production batches in the matching production batches is greater than the preset defective production batch proportion, it is determined that there are abnormalities in the production parameters of the preparation equipment.

[0087] In another possible embodiment, it is determined that there are no abnormalities in the production monitoring parameters of the preparation equipment, specifically including:

[0088] S21 Based on the quantity proportion of different quality defect types in different matching production batches, determine the quantity proportion of different quality defect types in different matching production batches. Based on the quantity proportion of different quality defect types, determine the quality defect deviation coefficient of different matching production batches;

[0089] S22 Based on the analysis results of quality defect data in different matching production batches, determine the quantity proportion of different quality defect types in different matching production batches. Based on the quantity proportion of different quality defect types in different matching production batches, determine the defect abnormality coefficient of different quality defect types;

[0090] S23 Based on the quality defect deviation coefficient of different matching production batches and the defect abnormality coefficient of different quality defect types, determine the parameter abnormality probability of the production monitoring parameters of the preparation equipment, and use the parameter abnormality probability to determine whether there are abnormalities in the production monitoring parameters of the preparation equipment.

[0091] Further, when the parameter abnormality probability of the production monitoring parameters of the preparation equipment is greater than the preset abnormality probability threshold, it is determined that there are abnormalities in the production monitoring parameters of the preparation equipment.

[0092] Optionally, the following content is included in the above step S21:

[0093] When it is determined, according to the proportion of the quantity of different quality defect types in different matching production batches, that there is no matching production batch in which the proportion of the quantity of the quality defect type is greater than the preset proportion, step S213 is entered; when there is a matching production batch in which the proportion of the quantity of the quality defect type is greater than the preset proportion, step S212 is entered.

[0094] S212 takes the matching production batch in which the proportion of the quantity of the quality defect type is greater than the preset proportion as the defective production batch. When the proportion of the defective production batch in the matching production batches does not meet the requirements, it is determined that there is an abnormality in the production monitoring parameters of the preparation equipment. When the proportion of the defective production batch in the matching production batches meets the requirements, step S213 is entered.

[0095] S213 determines the quality defect deviation coefficients of different matching production batches based on the proportions of the quantities of different quality defect types. When the quality defect deviation coefficients of different matching production batches are all less than the preset deviation coefficient threshold, it is determined that there is no abnormality in the production monitoring parameters of the preparation equipment. When there is a matching production batch with a quality defect deviation coefficient not less than the preset deviation coefficient threshold, step S214 is entered.

[0096] S214 determines that there is no abnormality in the production monitoring parameters of the preparation equipment when the proportion of the matching production batches with a quality defect deviation coefficient not less than the preset deviation coefficient threshold is greater than the preset proportion of defective production batches. When the proportion of the matching production batches with a quality defect deviation coefficient not less than the preset deviation coefficient threshold is not greater than the preset proportion of defective production batches, step S22 is entered.

[0097] Optionally, the following content is included in step S22 above:

[0098] S221 determines the proportions of the quantities of different quality defect types in different matching production batches based on the analysis results of the quality defect data in different matching production batches. When the proportions of the quantities of different quality defect types in different matching production batches all meet the requirements, it is determined that there is no abnormality in the production monitoring parameters of the preparation equipment. When there is a matching production batch in which the proportion of the quantity of the quality defect type does not meet the requirements, step S222 is entered.

[0099] S222 obtains the quantities of the matching production batches in which the proportions of the quantities of different quality defect types do not meet the requirements. When there is a quality defect type in which the proportion of the quantity of the matching production batches that do not meet the requirements is greater than the preset value of the proportion of the matching quantity, it is determined that there is no abnormality in the production monitoring parameters of the preparation equipment. When there is no quality defect type in which the proportion of the quantity of the matching production batches that do not meet the requirements is greater than the preset value of the proportion of the matching quantity, step S223 is entered.

[0100] S223 determines the defect anomaly coefficients of different quality defect types based on the proportion of the number of different quality defect types in different matching production batches. When the defect anomaly coefficients of different quality defect types are all within the preset defect anomaly coefficient range, it proceeds to step S23. When there is a quality defect type whose defect anomaly coefficient is not within the preset defect anomaly coefficient range, it proceeds to step S224;

[0101] S224 When the number of quality defect types whose defect anomaly coefficients are not within the preset defect anomaly coefficient range does not meet the requirements, it is determined that there is no anomaly in the production monitoring parameters of the preparation equipment. When the number of quality defect types whose defect anomaly coefficients are not within the preset defect anomaly coefficient range meets the requirements, it proceeds to step S23.

[0102] S3 determines the concerned defect types in the matching production batches based on the distribution data of the quality defect data in different matching production batches, and determines the concerned production equipment in the production equipment by using the concerned defect types;

[0103] Specifically, as Figure 4 shown, the method for determining the concerned defect types in the matching production batches is as follows:

[0104] Determine the proportion of the number of different quality defect types in different matching production batches based on the analysis results of the quality defect data in different matching production batches;

[0105] Determine the defect anomaly coefficients of different quality defect types based on the average value of the proportion of the number of different quality defect types in different matching production batches;

[0106] Determine whether the quality defect type is a concerned defect type according to the defect anomaly coefficient.

[0107] Furthermore, when the defect anomaly coefficient of the quality defect type is greater than the preset anomaly coefficient threshold, it is determined that the quality defect type is a concerned defect type.

[0108] It should be noted that the concerned production equipment in the production equipment is the production equipment whose parameters of the yoga fabric corresponding to the concerned defect type are related.

[0109] S4 determines the defect-related production batches based on the concerned defect types, obtains the similarity of the changes between the real-time monitoring data of the concerned production equipment and the monitoring data in the defect-related production batches, and determines the parameter adjustment processing strategy of the preparation equipment in combination with the quality defect data of the concerned defect types in different defect-related production batches.

[0110] Specifically, the defective - associated production batch is a historical production batch in which the proportion of the quantity of the concerned defect type is within a preset proportion range.

[0111] Specifically, the method for determining the parameter - adjustment processing strategy of the preparation equipment is as follows:

[0112] Based on the similarity of the real - time monitoring parameters of the associated production equipment and the changes in the monitoring data in the defective - associated production batch, determine the deviation amount between the real - time monitoring parameters of the associated production equipment within the preset time period and the monitoring parameters at different times within different preset time periods in the defective - associated production batch, and regard the moments with the deviation amounts of the monitoring parameters all within the preset deviation amount range as parameter - similar moments;

[0113] Regard the preset time period with the proportion of parameter - similar moments greater than the preset proportion of similar moments as the parameter - similar time period, and use the proportion of the parameter - similar time period in the defective - associated production batch as the parameter - similar weight coefficient;

[0114] Based on the quality - defect data of the concerned defect type in different defective - associated production batches, determine the proportion in different defective - associated production batches, and use the average value of the product of the proportion in different defective - associated production batches and the parameter - similar weight coefficient to determine the speculated proportion of different associated production equipment, and determine the determination of the parameter - adjustment processing strategy of the preparation equipment based on the speculated proportion of different associated production equipment.

[0115] Further, determining the determination of the parameter - adjustment processing strategy of the preparation equipment based on the speculated proportion of different associated production equipment specifically includes:

[0116] When the speculated proportion of different associated production equipment is less than the preset proportion threshold, there is no need to perform the parameter - adjustment processing of the preparation equipment;

[0117] When there is an associated production equipment with a speculated proportion not less than the preset proportion threshold, and when the number of associated production equipment with a speculated proportion not less than the preset proportion threshold is greater than the preset number of associated equipment, then use all the production - monitoring parameters of the preparation equipment as the adjustment target to perform the adjustment processing of the production state of the yoga - clothing fabric production line;

[0118] When the number of associated production equipment with a speculated proportion not less than the preset proportion threshold is not greater than the preset number of associated equipment, then use the associated production equipment with a speculated proportion not less than the preset proportion threshold as the adjustment target to perform the adjustment processing of the production state of the yoga - clothing fabric production line.

[0119] It can be understood that the method for determining the parameter - adjustment processing strategy of the preparation equipment is as follows:

[0120] Based on the similarity between the real-time monitoring parameters of the associated production equipment and the changes in the monitoring data in the defective associated production batches, determine the deviation amount between the real-time monitoring parameters of the associated production equipment within the preset time period and the monitoring parameters at different times within different preset time periods in the defective associated production batches, and use the moments with the deviation amounts of the monitoring parameters all within the preset deviation amount range as parameter similarity moments;

[0121] Use the preset time period with the proportion of the number of parameter similarity moments greater than the preset proportion of the number of similarity moments as the parameter similarity time period, and use the proportion of the parameter similarity time period in the defective associated production batches as the parameter similarity weight coefficient. When there are no defective associated production batches with a parameter similarity weight coefficient greater than the preset weight coefficient for different associated production equipment, there is no need to perform the parameter adjustment process for the preparation equipment;

[0122] When there is an associated production equipment with a defective associated production batch having a parameter similarity weight coefficient greater than the preset weight coefficient:

[0123] When the number of associated production equipment with defective associated production batches having a parameter similarity weight coefficient greater than the preset weight coefficient is greater than the preset associated equipment quantity threshold, then use the production monitoring parameters of all the preparation equipment as the adjustment target to perform the adjustment process for the production status of the yoga clothing fabric production line;

[0124] When the number of associated production equipment with defective associated production batches having a parameter similarity weight coefficient greater than the preset weight coefficient is not greater than the preset associated equipment quantity threshold:

[0125] Use the average value of the parameter similarity weight coefficients of different associated production equipment and different defective associated production batches as the equipment association weight coefficient of different associated production equipment. When there is an associated production equipment with an equipment association weight coefficient greater than the preset associated weight coefficient threshold, then use the production monitoring parameters of all the preparation equipment as the adjustment target to perform the adjustment process for the production status of the yoga clothing fabric production line;

[0126] When there is no associated production equipment with an equipment association weight coefficient greater than the preset associated weight coefficient threshold:

[0127] Based on the quality defect data of the concerned defect types in different defective associated production batches, determine the proportion in different defective associated production batches, and use the average value of the product of the proportion in different defective associated production batches and the parameter similarity weight coefficient to determine the speculated proportion of different associated production equipment, and determine the determination of the parameter adjustment process strategy of the preparation equipment based on the speculated proportion of different associated production equipment.

[0128] Embodiment 2

[0129] In a second aspect, the present invention provides a highly elastic and warm-keeping yoga clothing fabric with a double-layer composite structure, which is prepared by using the above-mentioned preparation method, and specifically includes: a heat-insulating layer, a connecting layer, and a blended layer;

[0130] Among them, the heat-insulating layer has a heat-insulating effect, the connecting layer is responsible for connecting the heat-insulating layer and the blended layer, and the blended layer is based on a blended material and has a highly elastic effect.

[0131] Furthermore, the technical solution lies in that the blended material is a material blended with polyester fiber and nylon.

[0132] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the embodiments of devices, equipment, and non-volatile computer storage media, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0133] The above describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0134] The above is only one or more embodiments of this specification and is not intended to limit this specification. For those skilled in the art, one or more embodiments of this specification can have various modifications and changes. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A preparation method, characterized in that, Specifically include: Obtain the monitoring parameters of different preparation devices on the production line of yoga clothing fabric with a double-layer composite structure, and determine the matching production batches based on the matching situation between the monitoring parameters and different historical production batches; When it is determined that the monitoring parameters of the preparation device are normal based on the analysis results of the quality defect data in different matching production batches, proceed to the next step; Determine the concerned defect types in the matching production batches based on the distribution data of the quality defect data in different matching production batches, and use the concerned defect types to determine the concerned production equipment in the production equipment; Based on the concerned defect types, determine the defect-related production batches, obtain the similarity of the changes between the real-time monitoring data of the concerned production equipment and the monitoring data in the defect-related production batches, and combine the quality defect data of the concerned defect types in different defect-related production batches to determine the parameter adjustment processing strategy of the preparation equipment.

2. The preparation method according to claim 1, characterized in that, The preset time period is determined according to the daily average output of the yoga clothing fabric production line, where the larger the daily average output, the longer the preset time period.

3. The preparation method according to claim 1, characterized in that, The matching situation includes determining the similarity between the monitoring parameters of different preparation devices within the preset time period and the monitoring parameters within different preset time periods in different historical production batches.

4. The preparation method according to claim 1, characterized in that, The method for determining the matching production batches is: Based on the matching situation between the monitoring parameters and the historical production batches, determine the similarity between the monitoring parameters of different preparation devices within the preset time period and the monitoring parameters within different preset time periods in the historical production batches; Based on the similarity situation, determine the preset time periods in the historical production batches where the deviations of the monitoring parameters of different preparation devices are within the preset deviation range, and use them as parameter matching time periods; According to the proportion of the parameter matching time periods in the historical production batches, determine whether the historical production batches are matching production batches.

5. The preparation method according to claim 4, characterized in that, When the proportion of the parameter matching time periods in the historical production batches is greater than the preset proportion of the parameter matching time periods, determine that the historical production batches are matching production batches.

6. The preparation method according to claim 1, characterized in that, The method for determining the concerned defect types in the matching production batches is: Based on the analysis results of the quality defect data in different matching production batches, determine the quantity proportion of different quality defect types in different matching production batches; Determine the defect abnormality coefficients of different quality defect types based on the average value of the quantity proportions of different quality defect types in different matching production batches; Determine whether the quality defect type is a concerned defect type according to the defect abnormality coefficient.

7. The preparation method according to claim 1, wherein The concerned production equipment in the production equipment is the production equipment whose parameters of the yoga clothing fabric corresponding to the concerned defect types are related.

8. The preparation method according to claim 1, characterized in that, The defect-related production batches are the historical production batches where the quantity proportion of the concerned defect types is within the preset quantity proportion range.

9. A highly elastic and warm yoga clothing fabric with a double-layer composite structure, which is prepared by using the preparation method described in any one of claims 1-8, and is characterized in that, Specifically include : a thermal insulation layer, a connecting layer, and a blended layer; Among them, the thermal insulation layer has a heat preservation effect, the connecting layer is responsible for connecting the thermal insulation layer and the blended layer, and the blended layer is based on a blended material and has a high elastic effect.

10. A highly elastic and warm yoga clothing fabric with a double-layer composite structure as described in claim 9, characterized in that, The blended material is a blend of polyester fiber and nylon.

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