Preparation management method and system for high-elastic warm-keeping yoga clothes fabric

Abstract

The invention provides a high-elastic warm-keeping yoga clothes fabric preparation management method and system, and belongs to the technical field of production management, and the method specifically comprises the following steps: determining defect parameters of a fabric according to analysis results of feedback data of different regions of interest, and determining the defect parameters of the fabric according to quality defect data of the defect parameters in different regions under different control targets; determining order acquisition data of different regions when determining that defect parameters have an optimal control target in combination with historical sales data of different regions and determining that associated production equipment can adopt a preset control strategy to perform production processing based on the optimal control target of the defect parameters and historical production data of the associated production equipment in a factory; and whether the control strategy of the associated production equipment needs to be adjusted is determined by using the order acquisition data, so that the optimal control of the production quality defect is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of production management, and particularly relates to a preparation management method and system for high-elastic thermal yoga clothing fabrics. Background Art

[0002] In order to realize the production management of high-elastic thermal yoga clothing fabrics, in the invention patent application CN202311808775.7 "A Textile Fabric Production Management System", the production status of production plan orders is updated and managed in real time, and the production inventory management module is docked to complete the warehousing of finished products, thus solving the technical problems of cumbersome operation, poor practicability, inability to meet specific requirements, low efficiency and high cost in the traditional production method. However, there are the following technical defects:

[0003] In the preparation production process of yoga clothing fabrics, the prior art solutions ignore generating differentiated production management methods based on the sales data of yoga clothing fabrics. Specifically, due to the differences in weather data in different regions, even if fabrics with the same parameters are used in different regions, there will be differences in quality defects and parameter requirements. Therefore, if the above factors are ignored, the reliability of the production results of the fabrics cannot be guaranteed.

[0004] In view of the above technical problems, specifically, the present application provides a preparation management method and system for high-elastic thermal yoga clothing fabrics. Summary of the Invention

[0005] To achieve the object of the present invention, the present invention adopts the following technical solutions:

[0006] Specifically, in the first aspect, the present application provides a preparation management method for high-elastic thermal yoga clothing fabrics, which specifically includes:

[0007] S1 Based on the analysis results of the historical sales data of high-elastic thermal yoga clothing fabrics, determine the historical sales data in different regions, and based on the analysis results of the historical sales data, determine the concerned regions in the regions;

[0008] S2 Obtain the feedback data of the after-sales data of the orders in the concerned regions, and based on the analysis results of the feedback data in different concerned regions, determine the defect parameters of the fabrics;

[0009] S3 Based on the defect parameters under different control objectives, determine the quality defect data in different regions, and combine the historical sales data in different regions. When there is a preferred control objective for the defect parameters, enter the next step;

[0010] When S4 determines that the associated production equipment can perform production processing using a preset control strategy based on the preferred control target of the defect parameters and the historical production data of the associated production equipment in the factory, it determines the order acquisition data for different regions and uses the order acquisition data to determine whether it is necessary to adjust the control strategy of the associated production equipment.

[0011] The beneficial effects of the present invention are as follows:

[0012] Based on the preferred control target of the defect parameters and the historical production data of the associated production equipment in the factory, it is determined whether the associated production equipment can perform production processing using a preset control strategy, thereby realizing the identification of the variation of other parameters when the defect parameters are controlled within the preferred control target, avoiding the technical problem of relatively drastic variation of other parameters caused by controlling the defect parameters, thus ensuring the reliability of the production processing of the fabric, and at the same time avoiding the influence on the quality defect situation in other regions.

[0013] Using the order acquisition data of different regions to determine whether it is necessary to adjust the control strategy of the associated production equipment, thereby avoiding the change of the order acquisition data in different regions, resulting in the change of the demand for the preferred control target of the defect parameters of the associated production equipment, and realizing the dynamic switching of the production control strategy from the change of the order data, further ensuring the reliability of the production management of the fabric.

[0014] A further technical solution is that the regions are divided according to the number of similar weather dates within a preset time period.

[0015] A further technical solution is that the method for dividing the regions is as follows:

[0016] Cities with a proportion of the number of similar weather dates within a preset time period greater than a preset proportion are divided into the same region.

[0017] A further technical solution is that the value of the preset time period is 1 year.

[0018] A further technical solution is that the historical sales data includes the historical sales volume of the region in different unit time periods.

[0019] A further technical solution is that the method for determining the concerned region in the region is as follows:

[0020] Based on the historical sales data of the region, determine the proportion of the historical sales volume of the region in different production batches of the factory;

[0021] Based on the proportion of the historical sales volume of the region in different production batches of the factory, determine the associated production batches of the region;

[0022] Determine the sales attention coefficient of the region according to the proportion of the quantity of the associated production batches in the production batches and the average value of the historical sales proportions in different production batches in the factory, and use the sales attention coefficient to determine whether the region is a concerned region.

[0023] A further technical solution is that the associated production batches in the region are the production batches with sales data in the region.

[0024] A further technical solution is that the sales attention coefficient of the region is determined according to the product of the proportion of the quantity of the associated production batches in the production batches and the average value of the historical sales proportions in different production batches in the factory.

[0025] A further technical solution is to determine whether it is necessary to adjust the control strategy of the associated production equipment, specifically including:

[0026] Based on the order acquisition data of different regions, determine the sales proportion of different regions in the current production batch, and based on the deviation amount between the sales proportion and the average value of the historical sales proportions of different regions in different production batches, determine the regions with sales volume changes in the region;

[0027] Determine whether it is necessary to adjust the control strategy of the associated production equipment based on the number of regions with sales volume changes.

[0028] A further technical solution is that the regions with sales volume changes are the regions where the deviation amount between the sales proportion and the average value of the historical sales proportions in different production batches does not meet the requirements.

[0029] A further technical solution is that when the number of regions with sales volume changes is greater than the preset threshold of the number of regions with changes, it is determined that it is necessary to adjust the control strategy of the associated production equipment.

[0030] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, and when the processor runs the computer program, it executes the above-mentioned method for preparing and managing a highly elastic warm yoga clothing fabric.

[0031] Other features and advantages will be described in the subsequent description, and 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.

[0032] To make the above-mentioned 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. Description of the Drawings

[0033] By describing in detail its exemplary embodiments with reference to the accompanying drawings, the above and other features and advantages of the present invention will become more apparent.

[0034] Figure 1 is a flowchart of a method for preparing and managing a highly elastic and warm yoga clothing fabric;

[0035] Figure 2 is a flowchart of a method for determining the areas of concern in a region;

[0036] Figure 3 is a flowchart of a method for determining the defect parameters of the fabric;

[0037] Figure 4 is a flowchart for determining that there is a preferred control target for the defect parameters;

[0038] Figure 5 is a flowchart for determining whether it is necessary to adjust the control strategy of the associated production equipment. Detailed Description of the Embodiments

[0039] 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.

[0040] In this application, by using the changes in the order acquisition data in different regions, the dynamic adjustment of the control strategy of the associated production equipment is realized, fully considering the differences in the requirements for fabric parameters in different regions, and ensuring the reliability of production management.

[0041] Embodiment 1

[0042] As Figure 1 shown, this application provides a method for preparing and managing a highly elastic and warm yoga clothing fabric, specifically including:

[0043] S1 Based on the analysis results of the historical sales data of the highly elastic and warm yoga clothing fabric, determine the historical sales data in different regions, and based on the analysis results of the historical sales data, determine the areas of concern in the region;

[0044] Specifically, production batches with a historical sales proportion in a region greater than a preset sales proportion threshold are used as sales-matched production batches. When the proportion of the number of sales-matched production batches in the region within a preset time period in the total number of production batches is greater than 0.3, the region is determined as a concerned region.

[0045] S2 Obtain the feedback data of the after-sales data of the orders in the concerned region, and determine the defect parameters of the fabric based on the analysis results of the feedback data of different concerned regions;

[0046] It should be noted that based on the parameters of the fabric corresponding to different quality defect types and the total number of orders of different quality defect types, the sum of the total number of orders of the quality defect types associated with different parameters is determined and used as the associated defect quantity. When the associated defect quantity is greater than 3, the parameter is determined as a defect parameter.

[0047] S3 Under different control objectives with the defect parameters, for the quality defect data in different regions, and in combination with the historical sales data of different regions, when it is determined that there is an optimal control objective for the defect parameters, proceed to the next step;

[0048] It can be understood that under different control objectives, there is a proportion of the number of orders of the quality defect types associated with the defect parameters in different regions. The regions with the proportion of the number of orders of the associated quality defect types greater than the preset order quantity proportion threshold are used as quality defect regions under the control objective. Based on the historical sales data of different quality defect regions, the sum of the historical sales proportions in different production batches of the factory in different quality defect regions is determined and used as the defect region proportion. When the average value of the defect region proportions in different production batches is greater than 0.2, it is determined that the control objective does not belong to the optimal control objective.

[0049] S4 When it is determined that the associated production equipment in the factory can perform production processing using a preset control strategy based on the optimal control objective of the defect parameter and the historical production data of the associated production equipment, determine the order acquisition data of different regions, and use the order acquisition data to determine whether it is necessary to adjust the control strategy of the associated production equipment.

[0050] Based on the optimal control objective of the defect parameter and the historical production data of the associated production equipment in the factory, determine the historical production data when the associated production equipment controls the defect parameter at the optimal control objective, and use it as the matching production data. Based on the deviation situation between the parameters of other dimensions in the matching production data and the current parameters, determine the parameters with deviations and use them as variable parameters. When the number of variable parameters is within the preset parameter quantity range, it is determined that the associated production equipment can perform production processing using a preset control strategy.

[0051] Based on the data obtained from orders in different regions, determine the sales volume proportion of different regions in the current production batch. Based on the deviation between the sales volume proportion and the average value of the historical sales volume proportions of different regions in different production batches, determine the regions with sales volume changes. When the number of regions with sales volume changes is greater than the preset threshold of the number of regions with changes, it is determined that an adjustment to the control strategy of the associated production equipment is required.

[0052] Furthermore, the regions are divided according to the number of similar weather dates within a preset time period.

[0053] Specifically, the method for dividing the regions is as follows:

[0054] Cities with a proportion of the number of similar weather dates within a preset time period greater than the preset proportion are divided into the same region.

[0055] It should be noted that the value of the preset time period is 1 year.

[0056] Furthermore, the historical sales data includes the historical sales volumes of the regions in different unit time periods.

[0057] It can be understood that, as Figure 2 shown, the method for determining the regions of concern in the regions is as follows:

[0058] Based on the historical sales data of the regions, determine the historical sales volume proportions of the regions in different production batches of the factory;

[0059] Based on the historical sales volume proportions of the regions in different production batches of the factory, determine the associated production batches of the regions;

[0060] According to the proportion of the number of the associated production batches in the production batch and the average value of the historical sales volume proportions of the factory in different production batches, determine the sales volume concern coefficient of the regions, and use the sales volume concern coefficient to determine whether the regions are regions of concern.

[0061] Furthermore, the associated production batches of the regions are the production batches with sales data in the regions.

[0062] In addition, it should be noted that the sales volume concern coefficient of the regions is determined according to the product of the proportion of the number of the associated production batches in the production batch and the average value of the historical sales volume proportions of the factory in different production batches.

[0063] It can be understood that the value range of the sales volume concern coefficient of the regions is between 0 and 1. When the sales volume concern coefficient of the regions is greater than the preset concern coefficient threshold, it is determined that the regions are regions of concern.

[0064] In another possible embodiment, the method for determining the area of concern in the area is as follows:

[0065] Based on the historical sales data of the area, determine the proportion of historical sales volume of the area in different production batches of the factory;

[0066] Based on the proportion of historical sales volume of the area in different production batches of the factory, determine the production batches with a historical sales volume proportion greater than the preset sales volume proportion threshold, and use them as sales-matching production batches;

[0067] Determine whether the area is an area of concern according to the proportion of the number of sales-matching production batches in the production batches within a preset time period.

[0068] Furthermore, when the proportion of the number of sales-matching production batches in the production batches within a preset time period meets the requirements, then determine that the area is an area of concern.

[0069] Optionally, the method for determining the area of concern in the area is as follows:

[0070] Based on the historical sales data of the area, determine the total historical sales volume of the area. When the total historical sales volume of the area is greater than the preset sales volume threshold, then determine that the area is an area of concern;

[0071] When the total historical sales volume of the area is not greater than the preset sales volume threshold:

[0072] Obtain the total historical sales volume of the area within a preset time period. When the total historical sales volume of the area within the preset time period does not meet the requirements, then determine that the area does not belong to the area of concern;

[0073] When the total historical sales volume of the area within a preset time period meets the requirements:

[0074] Based on the historical sales data of the area, determine the associated production batches of the area. When neither the associated production batches nor the proportion of the associated production batches in the production batches is within the preset range, then determine that the area does not belong to the area of concern;

[0075] When either the associated production batches or the proportion of the associated production batches in the production batches is within the preset range:

[0076] Determine the proportion of historical sales volume of the area in different production batches of the factory. When the proportion of historical sales volume in different production batches is not greater than the preset sales volume proportion threshold, then determine that the area does not belong to the area of concern;

[0077] When there are production batches in the region where the historical sales proportion is not greater than the preset sales proportion threshold:

[0078] Based on the historical sales proportion of different production batches of the factory in the region, determine the production batches with a historical sales proportion greater than the preset sales proportion threshold, and use them as sales-matched production batches. When the quantity proportion of the sales-matched production batches within the preset time period among the production batches meets the requirements, then determine that the region is a concerned region;

[0079] When the quantity proportion of the sales-matched production batches within the preset time period among the production batches does not meet the requirements:

[0080] Based on the historical sales proportion of different production batches of the factory in the region, determine the associated production batches of the region. According to the quantity proportion of the associated production batches among the production batches and the average value of the historical sales proportion of different production batches of the factory, determine the sales concern coefficient of the region, and use the sales concern coefficient to determine whether the region is a concerned region.

[0081] Furthermore, the feedback data of the after-sales data of the order includes the order quantities of different quality defect types.

[0082] Specifically, as Figure 3 shown, the method for determining the defect parameters of the fabric is:

[0083] Based on the analysis results of the feedback data of different concerned regions, determine the order quantities of different concerned regions for different quality defect types;

[0084] According to the order quantities of different concerned regions for different quality defect types, determine the total order quantity of different quality defect types;

[0085] Based on the fabric parameters corresponding to different quality defect types and the total order quantity of different quality defect types, determine the sum of the total order quantities of the quality defect types associated with different parameters, and use it as the associated defect quantity. Based on the associated defect quantity, determine whether the parameter is a defect parameter.

[0086] Furthermore, the quality defect types include poor heat preservation effect, short heat preservation time, no elasticity, poor elasticity, easy to break, poor abrasion resistance, poor moisture absorption, poor air permeability, and stains that cannot be cleaned.

[0087] In addition, it should be noted that the parameters include cotton fabric, polyester fiber content, nylon content, spandex content, the usage method of moisture-wicking finishing agent, thickness, yarn count, gram weight, and density.

[0088] Specifically, the corresponding relationship between the quality defect type and the fabric parameters is determined according to the preset corresponding relationship between the quality defect type and the fabric parameters.

[0089] In another possible embodiment, the method for determining the defect parameters of the fabric is as follows:

[0090] Based on the analysis results of the feedback data from different concerned regions, determine the order quantities of different concerned regions for different quality defect types;

[0091] According to the order quantities of different quality defect types and the fabric parameters corresponding to different quality defect types, determine the sum of the order quantities of the quality defect types associated with the parameters in different concerned regions, and determine the associated defect parameters of different concerned regions based on the sum of the order quantities.

[0092] Based on the associated defect parameters of different concerned regions, determine the defect parameters of the fabric.

[0093] Furthermore, the associated defect parameter is a parameter for which the sum of the order quantities of the associated quality defect types does not meet the requirements.

[0094] It should be noted that when the proportion of the number of concerned regions to which the parameter belongs among the associated defect parameters is greater than the preset proportion of the number of concerned regions, then determine that the parameter is the defect parameter of the fabric.

[0095] Optionally, the method for determining the defect parameters of the fabric is as follows:

[0096] Based on the analysis results of the feedback data from different concerned regions, determine the order quantities of different concerned regions for different quality defect types;

[0097] According to the order quantities of different concerned regions for different quality defect types, determine the total order quantity of different quality defect types. If there is no associated quality defect type for the parameter in the concerned region with different fabric parameters corresponding to different quality defect types and the total order quantity of different quality defect types, then determine that the parameter does not belong to the defect parameter;

[0098] When there is an associated quality defect type for the parameter in the concerned region:

[0099] Determine the sum of the total order quantities of the quality defect types associated with different parameters, and use it as the associated defect quantity. When the associated defect quantity of the parameter is greater than the preset defect order quantity threshold, then determine that the parameter is the defect parameter;

[0100] When the associated defect quantity of the parameter is not greater than the preset defect order quantity threshold,

[0101] Based on the order quantities of different quality defect types and the parameters of the fabrics corresponding to different quality defect types, determine the sum of the order quantities of the quality defect types associated with the parameters in different regions of concern. When it is determined that the parameters do not belong to the associated defect parameters in different regions of concern based on the sum of the order quantities, then determine that the parameters do not belong to the defect parameters;

[0102] When it is determined that there are regions of concern where the parameters belong to the associated defect parameters based on the sum of the order quantities:

[0103] Take the regions of concern where the parameters belong to the associated defect parameters as the parameter-associated regions. When the number of parameter-associated regions is greater than the preset number threshold of associated regions, then determine that the parameters belong to the defect parameters;

[0104] When the number of parameter-associated regions is not greater than the preset number threshold of associated regions:

[0105] Determine the defect correlation coefficients with different regions of concern based on the order quantities of the quality defect types associated with the parameters in different regions of concern. When the average value of the defect correlation coefficients with different regions of concern does not meet the requirements, then determine that the parameters belong to the defect parameters;

[0106] When the average value of the defect correlation coefficients with different regions of concern meets the requirements:

[0107] Based on the defect correlation coefficients with different regions of concern, determine the parameter anomaly coefficient of the parameters, and use the parameter anomaly coefficient to determine whether the parameters are defect parameters.

[0108] Furthermore, when the parameter anomaly coefficient of the parameters is greater than the preset anomaly coefficient threshold, then determine that the parameters are defect parameters.

[0109] Specifically, under different control objectives, the quality defect data in different regions are determined according to the order quantities of the quality defect types associated with the defect parameters in different regions under different control objectives.

[0110] Specifically, as Figure 4 shown, determine that there is an optimal control objective for the defect parameters, which specifically includes:

[0111] Based on the quality defect data of the defect parameters in different regions under different control objectives, determine the proportion of the order quantities of the quality defect types associated with the defect parameters in different regions under different control objectives;

[0112] Take the regions where the proportion of the order quantities of the associated quality defect types is greater than the preset proportion threshold of order quantities as the quality defect regions under the control objective;

[0113] Determine whether the control target belongs to an optimal control target according to the historical sales data of the quality defect area.

[0114] Further, determining whether the control target belongs to an optimal control target according to the historical sales data of the quality defect area specifically includes:

[0115] Based on the historical sales data of different quality defect areas, determine the sum of the historical sales proportions of different quality defect areas in different production batches of the factory, and use it as the proportion of the defect area.

[0116] When the average value of the proportion of the defect area in different production batches is greater than the preset proportion threshold of the defect area, it is determined that the control target does not belong to the optimal control target.

[0117] Optionally, the optimal control target is the control target with the smallest average value of the proportion of the defect area among the control targets where the average value of the proportion of the defect area in different production batches is not greater than the preset proportion threshold of the defect area.

[0118] Further, when there is no optimal control target for the defect parameter, set a separate production batch for the concerned area where the defect parameter belongs to the associated defect parameter and perform separate production processing.

[0119] In addition, it should be noted that the associated production equipment belongs to the production equipment that will cause changes in the indicators of the defect parameter.

[0120] It should be noted that determining the associated production equipment can adopt a preset control strategy for production processing, specifically including:

[0121] Based on the optimal control target of the defect parameter and the historical production data of the associated production equipment in the factory, determine the historical production data of the associated production equipment when controlling the defect parameter within the optimal control target, and use it as the matching production data.

[0122] Based on the deviation situation between the parameters of other dimensions in the matching production data and the current parameters, determine the parameters with deviations and use them as the variable parameters.

[0123] According to the number of the variable parameters, determine whether the associated production equipment can adopt a preset control strategy for production processing.

[0124] Further, when the number of the variable parameters is within the preset parameter number range, it is determined that the associated production equipment can adopt a preset control strategy for production processing.

[0125] It is understandable that the preset control strategy is to control the associated production equipment so that the defect parameters are controlled within the preferred control target.

[0126] Specifically, when the associated production equipment cannot be processed using the preset control strategy, a separate production batch is set for the area of concern where the defect parameters belong to the associated defect parameters for separate production processing.

[0127] Optionally, determining that the associated production equipment can be processed using the preset control strategy specifically includes:

[0128] S41 Using the preferred control target of the defect parameters and the historical production data of the associated production equipment in the factory, determine the historical production data of the associated production equipment when controlling the defect parameters within the preferred control target, and use it as the matching production data;

[0129] S42 Based on the deviation between the parameters of other dimensions in the matching production data and the current parameters, determine the parameters with deviations, and use them as variable parameters. According to the number of the variable parameters and the deviation between different parameters and the current parameters, determine the parameter influence variation coefficient;

[0130] S43 Based on the association between different parameters and different quality defect types in different areas of concern, determine the variable defect influence factors of different parameters, and combine the deviation between different parameters and the current parameters to determine the defect influence coefficient;

[0131] S44 According to the parameter influence variation coefficient and the defect influence coefficient, determine the parameter influence anomaly coefficient of the associated production equipment, and use the parameter influence anomaly coefficient to determine whether the associated production equipment can be processed using the preset control strategy.

[0132] Furthermore, when the parameter influence anomaly coefficient is greater than the preset influence anomaly coefficient threshold, it is determined that the associated production equipment cannot be processed using the preset control strategy.

[0133] Specifically, as Figure 5 shown, determining whether it is necessary to adjust the control strategy of the associated production equipment specifically includes:

[0134] Based on the order acquisition data in different regions, determine the sales proportion of different regions in the current production batch. Based on the deviation between the sales proportion and the average value of the historical sales proportions of different regions in different production batches, determine the regions with sales volume changes in the regions;

[0135] Based on the number of regions with sales volume changes, determine whether it is necessary to adjust the control strategy of the associated production equipment.

[0136] Further, the sales volume change area is the area where the deviation between the sales volume ratio and the average value of the historical sales volume ratios in different production batches does not meet the requirements.

[0137] It should be noted that when the number of the sales volume change areas is greater than the preset change area number threshold, it is determined that the control strategy of the associated production equipment needs to be adjusted.

[0138] Embodiment 2

[0139] In a second aspect, the present invention provides a computer system, including: a memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor. When the processor runs the computer program, it executes the above-mentioned method for preparing and managing a highly elastic warm yoga clothing fabric.

[0140] Optionally, the above step S41 includes the following content:

[0141] S411 determines the historical production data of the associated production equipment when controlling the defect parameter at the preferred control target based on the preferred control target of the defect parameter and the historical production data of the associated production equipment in the factory, and uses it as the matching production data;

[0142] S412 when the data volume of the matching production data is less than the preset data volume threshold, it is determined that the associated production equipment can adopt the preset control strategy for production processing. When the data volume of the matching production data is not less than the preset data volume threshold, it proceeds to step S42.

[0143] Optionally, the above step S42 includes the following content:

[0144] S421 determines the parameters without deviation based on the deviation between the parameters of other dimensions in the matching production data and the current parameters, and determines that the associated production equipment can adopt the preset control strategy for production processing. When there are parameters with deviation, it proceeds to step S422;

[0145] S422 takes the parameters whose deviation from the current parameters does not meet the requirements as the parameters with deviation, and uses them as the variable parameters. When the number of dimensions of the variable parameters does not meet the requirements, it is determined that the associated production equipment cannot adopt the preset control strategy for production processing. When the number of dimensions of the variable parameters meets the requirements, it proceeds to step S423;

[0146] S423 Determine a parameter influence variation coefficient according to the number of the variation parameters and the deviation between different parameters and the current parameters. When the parameter influence variation coefficient does not meet the requirements, it is determined that the associated production equipment cannot adopt a preset control strategy for production processing. When the parameter influence variation coefficient meets the requirements, go to step S43.

[0147] Optionally, the above step S43 includes the following content:

[0148] S431 Determine a variation defect influence factor of different parameters according to the association between different parameters and different quality defect types in different concerned regions. When there is no variation parameter with a variation defect influence factor greater than a preset influence factor threshold, go to step S432. When there is a variation parameter with a variation defect influence factor greater than the preset influence factor threshold, it is determined that the associated production equipment cannot adopt a preset control strategy for production processing;

[0149] S432 Determine that there is a variation parameter with a variation defect influence factor within a preset influence factor range according to the variation defect influence factors of different variation parameters. When there is no variation parameter with a variation defect influence factor within the preset influence factor range, go to step S334;

[0150] S433 When the number of variation parameters with a variation defect influence factor within the preset influence factor range does not meet the requirements, it is determined that the associated production equipment cannot adopt a preset control strategy for production processing. When the number of variation parameters with a variation defect influence factor within the preset influence factor range meets the requirements, go to step S334;

[0151] S334 Determine a defect influence coefficient based on the variation defect influence factors of different parameters and the deviation between different parameters and the current parameters. When the defect influence coefficient does not meet the requirements, it is determined that the associated production equipment cannot adopt a preset control strategy for production processing. When the defect influence coefficient meets the requirements, go to step S34.

[0152] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non - volatile computer storage medium embodiments, 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.

[0153] The specific embodiments of this specification are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the figures do not necessarily require the particular order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0154] The above are only one or more embodiments of this specification and are not intended to limit this specification. For those skilled in the art, various changes and modifications can be made to one or more embodiments of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included within the scope of the claims of this specification.

Claims

1. A preparation management method for a highly elastic and warm yoga clothing fabric, characterized in that, Specifically include: Based on the analysis results of the historical sales data of the high-elastic thermal yoga clothing fabric, determine the historical sales data in different regions, and determine the concerned regions in the regions based on the analysis results of the historical sales data; Obtain the feedback data of the after-sales data of the orders in the concerned regions, and determine the defect parameters of the fabric based on the analysis results of the feedback data in different concerned regions; Under different control objectives with the defect parameters, determine the quality defect data in different regions, and combine with the historical sales data in different regions. When there is a preferred control objective for the defect parameters, proceed to the next step; Based on the preferred control objective of the defect parameters and the historical production data of the associated production equipment in the factory, when it is determined that the associated production equipment can be processed using a preset control strategy, determine the order acquisition data in different regions, and use the order acquisition data to determine whether it is necessary to adjust the control strategy of the associated production equipment.

2. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 1, characterized in that, The regions are divided according to the number of similar weather dates within a preset time period.

3. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 1, characterized in that, The method for dividing the regions is as follows: Cities with a proportion of the number of similar weather dates within a preset time period greater than a preset proportion are divided into the same region.

4. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 1, characterized in that, The historical sales data includes the historical sales volume in different unit time periods in the region.

5. The preparation management method of the high-elastic thermal yoga clothing fabric according to claim 1, characterized in that The method for determining the concerned regions in the regions is as follows: Based on the historical sales data of the region, determine the proportion of the historical sales volume in different production batches of the factory in the region; Based on the proportion of the historical sales volume in different production batches of the factory in the region, determine the associated production batches of the region; According to the proportion of the number of the associated production batches in the production batches and the average value of the proportion of the historical sales volume in different production batches of the factory, determine the sales volume concern coefficient of the region, and use the sales volume concern coefficient to determine whether the region is a concerned region.

6. The preparation management method of the high-elastic thermal yoga clothing fabric according to claim 5, characterized in that, The associated production batches of the region are the production batches with sales data in the region.

7. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 5, characterized in that The value range of the sales volume concern coefficient of the region is between 0 and 1. When the sales volume concern coefficient of the region is greater than the preset concern coefficient threshold, it is determined that the region is a concerned region.

8. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 1, characterized in that, Determining whether it is necessary to adjust the control strategy of the associated production equipment specifically includes: Based on the order acquisition data in different regions, determine the proportion of the sales volume in the current production batch in different regions. Based on the deviation amount between the sales volume proportion and the average value of the proportion of the historical sales volume in different production batches in different regions, determine the regions with sales volume changes in the regions; Based on the number of regions with sales volume changes, determine whether it is necessary to adjust the control strategy of the associated production equipment.

9. The preparation management method of the highly elastic thermal yoga clothing fabric according to claim 8, characterized in that, The regions with sales volume changes are the regions where the deviation amount between the sales volume proportion and the average value of the proportion of the historical sales volume in different production batches does not meet the requirements.

10. A computer system, comprising: A memory and a processor connected by communication, and a computer program stored on the memory and capable of running on the processor, characterized in that when the processor runs the computer program, it executes a method for preparing and managing a highly elastic thermal yoga clothing fabric according to any one of claims 1-9.

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