A textile fabric production self-adaptive management method based on production scheduling

Abstract

The present application relates to the technical field of self-adaptive management of textile fabric production, and relates to a self-adaptive management method for textile fabric production based on production scheduling.The method comprises the following steps: S1, obtaining historical sales orders, distributing the historical sales orders according to customer attribution, and extracting the fabric quantity of the distributed historical sales orders; S2, performing market trend analysis on fabric categories, obtaining customer scale data, setting a consumption speed threshold for the fabric by combining the scale data with the market trend, and setting additional days for the customer by combining the transaction frequency in the historical sales orders; the present application selects a stable consumption speed that is suitable for the stable customer within the consumption speed range by combining the consumption speed trend of the historical orders with the fabric market trend, accurately calculates the remaining time for ordering, ensures timely production arrangement before the fabric of the customer is exhausted, and avoids the cooperation risk caused by a shortage.

Description

Technical Field

[0001] This invention relates to the field of adaptive management technology for textile fabric production, and more specifically, to an adaptive management method for textile fabric production based on production scheduling. Background Technology

[0002] In the textile fabric production sector, production scheduling is a core element in ensuring efficient operation of enterprises and meeting market demands. Existing technologies use methods such as obtaining customer order information and statistical analysis of historical fabric sales to formulate production plans and inventory management strategies.

[0003] However, existing textile production scheduling technologies mostly operate in scenarios driven by fixed orders or single sales statistics, ignoring the impact of differences in customer size, transaction frequency, and fabric market trends on demand. For example, applying the same production response strategy to long-term customers with large purchase volumes and small-batch, scattered customers can easily lead to untimely responses to the needs of core customers or overproduction for scattered customers, resulting in inventory backlog. Therefore, an adaptive management method for textile production based on production scheduling is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive management method for textile fabric production based on production scheduling, so as to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, an adaptive management method for textile fabric production based on production scheduling is provided, comprising the following steps:

[0006] S1. Obtain historical sales orders, allocate them according to customer affiliation, and extract fabric quantities from the allocated historical sales orders;

[0007] S2. Conduct market trend analysis based on fabric categories, and at the same time obtain customer size data. Combine the size data with market trends to set a consumption rate threshold for fabrics, and combine it with the transaction frequency in historical sales orders to set an extra number of days for customers.

[0008] S3. Extract the order time of customers based on historical sales orders, compare the fabric quantity and consumption time threshold corresponding to the latest order time with the extra days, and classify customers into stable customers and unstable customers based on the comparison results.

[0009] S4. For stable customers, analyze the consumption rate range by combining the order time of historical sales orders with the fabric quantity, and analyze the consumption rate trend by combining the consumption rate range. Then, combine the consumption rate trend with the market trend to analyze the stable consumption rate within the consumption rate range, and combine the stable consumption rate with the real time to analyze the remaining time for ordering.

[0010] S5. For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and use the minimum consumption rate to analyze the remaining time for ordering. At the same time, set the upper limit of production quantity and expiration time according to the transaction frequency.

[0011] S6. Production scheduling is based on the remaining time of orders, with stable customers having higher production priority than unstable customers.

[0012] As a further improvement to this technical solution, step S1 is as follows:

[0013] S1.1 Obtain historical sales orders and simultaneously extract customers from the historical sales orders;

[0014] S1.2. Assign historical sales orders to customers, obtain the historical sales orders corresponding to each customer, extract the fabric quantity from the assigned historical sales orders, and obtain the fabric quantity of each order in the historical sales orders.

[0015] As a further improvement to this technical solution, step S2 is as follows:

[0016] S2.1 Obtain the fabric varieties corresponding to historical sales orders, and then combine the fabric varieties with the network to conduct market trend analysis and obtain the market trends corresponding to the fabric varieties;

[0017] S2.2 Obtain customer scale data, and combine the scale data with market trends to set a consumption rate threshold for fabrics;

[0018] The larger the data volume, the faster the consumption rate threshold.

[0019] The smaller the data size, the slower the consumption rate threshold.

[0020] S2.3 Analyze the transaction frequency of customers' historical sales orders to obtain the corresponding transaction frequency of customers, and then set the number of extra days based on the transaction frequency and the total fabric quantity of historical sales orders.

[0021] The higher the transaction frequency or the greater the total amount of fabric, the more extra days are awarded.

[0022] The lower the transaction frequency or the smaller the total amount of fabric, the fewer the extra days.

[0023] As a further improvement to this technical solution, step S3 is as follows:

[0024] S3.1 Extract the order time of customers based on historical sales orders, and obtain the order time of each order in the historical sales orders;

[0025] S3.2 Extract the order with the latest order time from the historical sales orders, and calculate the real-time difference time by combining the latest order time with the real-time time. Then compare the fabric quantity and consumption time threshold corresponding to the order with the real-time difference time and the number of extra days.

[0026] If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, and the value of the real-time difference time exceeds the number of extra days, then the customer is judged as an unstable customer.

[0027] If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, but the value of the difference time does not exceed the extra days, it is judged as a stable customer.

[0028] If the fabric quantity combined with the consumption time threshold is greater than the real-time difference time, the customer is directly identified as a stable customer.

[0029] As a further improvement to this technical solution, step S4 is as follows:

[0030] S4.1 For stable customers, analyze the consumption rate range by combining the order time of the historical sales orders corresponding to stable customers with the fabric quantity. First, obtain the consumption rate corresponding to each order, and then summarize all orders to obtain the consumption rate range.

[0031] S4.2 Sort the consumption speed according to the order time, and perform consumption speed trend analysis on the sorted consumption speed to obtain the consumption speed trend of the customer.

[0032] S4.3. Combine the consumption rate trend with the market trend to conduct a stable consumption rate analysis within the consumption rate range, and then select a consumption rate within the consumption rate range as the stable consumption rate corresponding to the stable customer.

[0033] When the consumption rate trend is getting faster and faster, or the market trend shows that the heat is getting higher and higher, the faster the stable consumption rate is selected within the consumption rate range;

[0034] When the consumption rate trend is getting slower and slower, or the market trend shows that the heat is getting lower and lower, the lower the stable consumption rate should be selected within the consumption rate range.

[0035] S4.4. Combine the stable consumption rate selected in S4.3 with the real-time difference time to analyze the remaining time for ordering. Calculate the consumption days based on the stable consumption rate and the fabric quantity corresponding to the latest order time. Then subtract the consumption days from the real-time difference time and use the subtracted value as the remaining time for ordering.

[0036] As a further improvement to this technical solution, step S5 is as follows:

[0037] S5.1 For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and use the minimum consumption rate to analyze the remaining order time to obtain the remaining order time corresponding to the unstable customer.

[0038] S5.2 Set the production quantity limit and expiration time according to the transaction frequency. At the same time, if the difference between the customer's time in S3.2 and the real time is greater than the expiration time, the customer is determined to be invalid and deleted from the list of unstable customers.

[0039] The higher the transaction frequency, the higher the upper limit of production quantity and the longer the expiration time;

[0040] The lower the transaction frequency, the lower the production quantity limit and the shorter the expiration time.

[0041] As a further improvement to this technical solution, the quantity of fabric produced corresponding to the remaining order time in S4 is the quantity of fabric corresponding to the latest order time of the customer. Similarly, the base quantity of the upper limit of production quantity in S5 is also the quantity of fabric corresponding to the latest order time of the customer. The upper limit of production quantity is less than the quantity of fabric corresponding to the latest order time. The upper limit of production quantity is set by calculating the percentage of the fabric quantity.

[0042] As a further improvement to this technical solution, in the production process, S6 prioritizes the production of fabric varieties and quantities with less remaining order time, and ensures that the corresponding fabric quantity can be produced quantitatively when the remaining order time is completed.

[0043] When a customer fails, the fabric produced for that customer is directly added to the fabric to be produced.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0045] 1. In this adaptive management method for textile fabric production based on production scheduling, for stable customers, by combining the consumption rate trend of historical orders with fabric market trends, a suitable stable consumption rate is selected within the consumption rate range, and the remaining time for ordering is accurately calculated to ensure that production is arranged in time before the customer runs out of fabric, avoiding cooperation risks caused by stockouts. For unstable customers, the minimum consumption rate is used to conservatively predict the ordering time, and an upper limit for production quantity is set based on transaction frequency to prevent overproduction of fabric due to demand fluctuations, reduce the capital tied up in inventory and warehousing costs, and achieve accurate prediction of customer demand, thereby avoiding the problems caused by one-size-fits-all production from the root.

[0046] 2. In this adaptive management method for textile fabric production based on production scheduling, the production priority of stable customers is clearly defined as higher than that of unstable customers. Priority is given to ensuring the delivery of orders from customers with close cooperative relationships and stable demand. At the same time, all production tasks are sorted from shortest to longest remaining time after ordering, and the production cycle is calculated in combination with the average daily capacity of the production line to ensure that production is completed within the remaining time after ordering and to avoid overdue delivery. At the same time, resource waste is avoided, the overall utilization rate of the production line is improved, and production resources are tilted towards high-value and urgent needs, thereby optimizing overall production efficiency.

[0047] 3. In this adaptive management method for textile fabric production based on production scheduling, an expiration time is set for unstable customers. If the real-time difference time exceeds the expiration time, the customer is deemed to be invalid and dedicated production is stopped. This avoids inventory waste caused by producing for customers with no subsequent demand. At the same time, the fabrics produced for invalid customers are directly transferred to the waiting fabric pool, which can be redistributed to customers with demand or used as emergency inventory. This reduces the risk of bad debts and losses from idle fabrics, improves the ability to flexibly allocate resources, proactively avoids potential business risks, and ensures the stable operation of the enterprise. Attached Figure Description

[0048] Figure 1 This is an overall flowchart of the present invention;

[0049] Figure 2 This is a flowchart illustrating the process of obtaining historical sales orders according to the present invention.

[0050] Figure 3 This is a flowchart illustrating the process of obtaining the fabric varieties corresponding to historical sales orders in this invention.

[0051] Figure 4 This is a flowchart illustrating the process of extracting customer order times based on historical sales orders according to the present invention.

[0052] Figure 5 This is a flowchart illustrating how the consumption rate is sorted according to the order placement time according to the present invention.

[0053] Figure 6 This is a flowchart illustrating the process of obtaining the remaining order time for non-stable customers according to the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Please see Figure 1 - Figure 6As shown, the purpose of this embodiment is to provide an adaptive management method for textile fabric production based on production scheduling, including the following steps:

[0056] S1. Obtain historical sales orders, allocate them according to customer affiliation, and extract fabric quantities from the allocated historical sales orders;

[0057] The steps for S1 are as follows:

[0058] S1.1 Obtain historical sales orders and simultaneously extract customers from the historical sales orders;

[0059] Retrieve all historical sales orders from the enterprise's sales order management system over a period of time. Extract customer information for each order, such as customer name and customer number, to identify which customer placed each order.

[0060] S1.2. Assign historical sales orders to customers, obtain the historical sales orders corresponding to each customer, extract the fabric quantity from the assigned historical sales orders, and obtain the fabric quantity for each order in the historical sales orders. Establish the correspondence between customers, orders, and fabric quantities.

[0061] S2. Conduct market trend analysis based on fabric categories, and simultaneously obtain customer size data. Combine the size data with market trends to set a consumption rate threshold for fabrics, and combine the transaction frequency in historical sales orders to set an additional number of days for customers. Combine market trends and customer characteristics to set two key parameters for subsequent customer classification.

[0062] The steps for S2 are as follows:

[0063] S2.1 Obtain the fabric varieties corresponding to historical sales orders, and then combine the fabric varieties with the network to conduct market trend analysis and obtain the market trends corresponding to the fabric varieties;

[0064] From the historical sales orders we have obtained, we extract the fabric variety information for each order to identify which types of fabrics are involved in the order. We then collect and analyze the extracted fabric varieties in conjunction with data from online platforms (such as sales data from e-commerce platforms, discussion trends in industry forums, and related topics on social media). By analyzing this online data, we can determine the popularity and demand trends of each fabric variety in the current market, thereby obtaining the corresponding market trend for each fabric variety, such as whether it is in an upward, stable, or downward trend.

[0065] S2.2 Obtain customer scale data, and combine the scale data with market trends to set a consumption rate threshold for fabrics;

[0066] The larger the data volume, the faster the consumption rate threshold.

[0067] The smaller the data size, the slower the consumption rate threshold.

[0068] We obtain customer size data for each customer from the company's customer management system or relevant business records. This data can include indicators reflecting customer size, such as annual purchase volume, company size (e.g., number of employees, annual turnover), and duration of cooperation. Based on the obtained customer size data and market trends for the fabric type, we set a threshold for fabric consumption rate. Larger customer size data suggests stronger demand or purchasing power, allowing for a faster fabric consumption rate threshold; conversely, smaller customer size data results in a slower threshold. Market trends are also considered. If market trends indicate rapid demand growth for a particular fabric type, the threshold will be adjusted accordingly to better reflect actual market conditions. The formula is as follows:

[0069] ;

[0070] Among them, V t Let be the consumption rate threshold for fabric type t, i.e., the expected consumption quantity of that fabric type per unit time; k is a proportionality constant, determined based on historical enterprise data, used to adjust the dimensions and numerical range of the formula calculation results; S c The size data for customer c is a quantitative indicator reflecting the size of the customer base. T t t represents the market trend coefficient for fabric variety t, used to reflect the market trend of that fabric variety.

[0071] S2.3 Analyze the transaction frequency of customers' historical sales orders to obtain the corresponding transaction frequency of customers, and then set the number of extra days based on the transaction frequency and the total fabric quantity of historical sales orders.

[0072] The higher the transaction frequency or the greater the total amount of fabric, the more extra days are awarded.

[0073] The lower the transaction frequency or the smaller the total amount of fabric, the fewer the extra days.

[0074] The system tracks the number of orders placed by the customer throughout all time periods. A higher number of orders indicates higher transaction frequency, while fewer orders indicate lower frequency. The system also sums up the fabric quantities from all historical sales orders to obtain the total fabric quantity. Based on the calculated transaction frequency and total fabric quantity, the system determines the number of extra days to allocate. Higher transaction frequency or a larger total fabric quantity results in a larger number of extra days, while lower transaction frequency or a smaller total fabric quantity results in a smaller number of extra days. The formula is as follows:

[0075] ;

[0076] Where D represents the extra days, which is the extra time days given based on the transaction situation; F represents the transaction frequency, which represents how frequently a customer places an order within a unit of time; Q represents the total quantity of fabric in historical sales orders, which is the sum of the quantity of fabric in all of the customer's past orders; α represents the weighting coefficient of transaction frequency, which is used to measure the weighting of the impact of transaction frequency on the extra days; and β represents the weighting coefficient of total fabric quantity, which is used to measure the weighting of the impact of total fabric quantity on the extra days.

[0077] S3. Extract the order time of customers based on historical sales orders, compare the fabric quantity and consumption time threshold corresponding to the latest order time with the extra days, and classify customers into stable customers and unstable customers based on the comparison results; classify customer types based on the comparison of real-time difference time with consumption cycle + extra days.

[0078] The steps for S3 are as follows:

[0079] S3.1 Extract the order time of customers based on historical sales orders, and obtain the order time of each order in the historical sales orders;

[0080] S3.2 Extract the order with the latest order time from the historical sales orders, and calculate the real-time difference time by combining the latest order time with the real-time time. Then compare the fabric quantity and consumption time threshold corresponding to the order with the real-time difference time and the number of extra days.

[0081] If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, and the value of the real-time difference time exceeds the number of extra days, then the customer is judged as an unstable customer.

[0082] If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, but the value of the difference time does not exceed the extra days, it is judged as a stable customer.

[0083] If the fabric quantity combined with the consumption time threshold is greater than the real-time difference time, the customer is directly identified as a stable customer.

[0084] S4. For stable customers, analyze the consumption rate range by combining the order time of historical sales orders with the fabric quantity, and then analyze the consumption rate trend by combining the consumption rate range. Then, combine the consumption rate trend with the market trend to analyze the stable consumption rate within the consumption rate range, and combine the stable consumption rate with real-time time to analyze the remaining time for ordering; accurately predict the remaining time for ordering to avoid stockouts.

[0085] The steps for S4 are as follows:

[0086] S4.1 For stable customers, analyze the consumption rate range by combining the order time of the historical sales orders corresponding to stable customers with the fabric quantity. First, obtain the consumption rate corresponding to each order, and then summarize all orders to obtain the consumption rate range.

[0087] This process involves filtering out all orders belonging to stable customers from historical sales orders, extracting the order placement time and corresponding fabric quantity for each order, and then dividing the fabric quantity of each historical order from a stable customer by the time interval from the order placement to the next order placement (or, if it is the last order, the real-time time of the analysis) to obtain the consumption rate for each order. Finally, the consumption rates calculated from all historical orders of stable customers are summarized, and the maximum and minimum values ​​are identified. The range between these two values ​​is the consumption rate range.

[0088] S4.2 Sort the consumption speed according to the order placement time, and perform consumption speed trend analysis on the sorted consumption speed to obtain the consumption speed trend corresponding to the customer; Sort the consumption speed of each order according to the order placement time, observe the changes in consumption speed after sorting, and determine whether it shows a trend of gradually accelerating, gradually slowing down or relatively stable, so as to obtain the consumption speed trend corresponding to the customer.

[0089] S4.3. Combine the consumption rate trend with the market trend to conduct a stable consumption rate analysis within the consumption rate range, and then select a consumption rate within the consumption rate range as the stable consumption rate corresponding to the stable customer.

[0090] When the consumption rate trend is getting faster and faster, or the market trend shows that the heat is getting higher and higher, the faster the stable consumption rate is selected from the consumption rate range, the higher the value is selected from the consumption rate range as the stable consumption rate.

[0091] When the consumption rate trend is getting slower and slower, or the market trend shows that the heat is getting lower and lower, the lower the stable consumption rate selected from the consumption rate range, the more likely a value is to be selected as the stable consumption rate. The formula is as follows:

[0092] ;

[0093] Among them, v stable To stabilize the consumption rate, a fixed consumption rate value, V, is used for subsequent calculations. min V represents the minimum value within the consumption rate range, specifically the minimum consumption rate among historical orders from stable customers. max K is the maximum value of the consumption rate range, that is, the maximum value among the historical consumption rates of stable customers' orders. K is the trend coefficient, an adjustment coefficient set according to the consumption rate trend and market trend.

[0094] When the consumption rate increases or the market enthusiasm increases, K is set to 0.7-1 (e.g., 0.9), biased towards the upper limit of the range;

[0095] When the consumption rate slows down or the market enthusiasm decreases, K is set to 0-0.3 (e.g., 0.2), biased towards the lower limit of the range.

[0096] S4.4. Combine the stable consumption rate selected in S4.3 with the real-time difference time to analyze the remaining order time (the difference between the real-time time and the latest order time). Calculate the consumption days based on the stable consumption rate and the fabric quantity corresponding to the latest order time. Then, subtract the consumption days from the real-time difference time, and use the difference as the remaining order time. This time indicates when to arrange the next order for the customer. The formula is as follows:

[0097] ;

[0098] Among them, T consume To determine the number of days to consume, the latest fabric order will be used up at a stable consumption rate, Q. latest This refers to the quantity of fabric corresponding to the order placed at the latest time, i.e., the total amount of fabric purchased by the customer in their most recent order.

[0099] ;

[0100] Among them, T order This indicates the remaining time before ordering, providing a buffer period for initiating the next order for the customer. A negative result means the suggested ordering time has passed. d This is the real-time difference, the time difference between the current real-time time and the customer's latest order time.

[0101] S5. For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and analyze the remaining time for ordering based on the minimum consumption rate. At the same time, set the upper limit of production quantity and expiration time according to the transaction frequency; conservatively predict the ordering time, set the production upper limit and expiration mechanism, and reduce inventory risk.

[0102] The steps for S5 are as follows:

[0103] S5.1 For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and use the minimum consumption rate to analyze the remaining order time to obtain the remaining order time corresponding to the unstable customer.

[0104] The method for calculating the remaining time for orders is the same as that for stable customers. The difference is that the minimum consumption rate is selected directly within the consumption rate range. First, the quantity of fabric in the latest order is divided by the minimum consumption rate to obtain the time (consumption days) required for this batch of fabric to be consumed at the slowest speed. Then, the real-time difference time is subtracted from the consumption days to obtain the remaining time for orders of non-stable customers.

[0105] S5.2 Set the production quantity limit and expiration time according to the transaction frequency. At the same time, if the difference between the customer's time in S3.2 and the real time is greater than the expiration time, the customer is determined to be invalid and deleted from the list of unstable customers.

[0106] The higher the transaction frequency, the higher the upper limit of production quantity, indicating that its potential demand is relatively stable and the failure time is longer;

[0107] For customers with high transaction frequency and relatively close cooperative relationships with the company, the expiration time is set to a longer period of 180 days.

[0108] The lower the transaction frequency, the lower the upper limit of production quantity, avoiding overproduction that leads to inventory backlog and shorter expiration time.

[0109] For customers with low transaction frequency and poor cooperation stability, the expiration time is set to a shorter period of 90 days.

[0110] For example, if a customer has low transaction frequency, and the maximum production quantity is set at 0.3, and the latest order time corresponds to 2 tons of fabric, then the production quantity for this customer is 2 tons × 0.4 = 0.5 tons.

[0111] Retrieve the real-time difference time (the difference between the real-time time and the latest order time) calculated in S3.2 for the unstable customer, and compare it with the set expiration time: if the real-time difference time is greater than the expiration time, the customer is determined to be invalid and deleted from the list of unstable customers; if the real-time difference time is less than or equal to the expiration time, the customer is retained in the list of unstable customers.

[0112] The remaining time for ordering in S4 corresponds to the quantity of fabric to be produced at the time the customer placed the order. Similarly, the base quantity for the upper limit of production quantity in S5 is also the quantity of fabric to be produced at the time the customer placed the order. The upper limit of production quantity is less than the quantity of fabric to be produced at the time the customer placed the order. The upper limit of production quantity is set by calculating a percentage of the quantity of fabric.

[0113] S6. Production scheduling is based on the remaining time of orders, and production priority is given to stable customers over unstable customers. All production tasks must be arranged to prioritize the needs of stable customers.

[0114] During the production process, S6 prioritizes the production of fabric varieties and quantities with less remaining order time, and ensures that the corresponding fabric quantity can be produced in a quantitative manner when the remaining order time is completed.

[0115] When a customer fails to produce their product, the corresponding fabric is directly added to the fabric to be produced. The steps are as follows:

[0116] For stable customer groups, collect the remaining time of each customer's order and the corresponding fabric type and quantity to be produced. Sort the orders by remaining time from shortest to longest, and prioritize production tasks with shorter remaining time. After the tasks of stable customers are scheduled, non-stable customers are sorted according to the same rules of remaining time of order and added to the production plan. At the same time, based on the sorted production tasks and the average daily capacity of the company's production line, calculate the production cycle required for each task to ensure that the production cycle of each task is less than or equal to its remaining time of order, so as to avoid failure to deliver on time.

[0117] Regularly check customer status. If a customer is deemed invalid (real-time difference time exceeds invalid time), stop the dedicated production task for that customer. Fabrics that have been produced but not delivered are directly transferred to the company's pending production fabric pool for later allocation to customers with the same fabric needs or as emergency inventory.

[0118] By segmenting customers and using dynamic parameters, we avoid inventory backlogs or stockouts caused by one-size-fits-all production. We also adjust production rhythms in conjunction with fabric market trends to improve responsiveness to market changes. For unstable customers, we set production caps and failure mechanisms to reduce bad debts and inventory risks.

[0119] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for adaptive management of textile fabric production based on production scheduling, characterized in that: Includes the following steps: S1. Obtain historical sales orders, allocate them according to customer affiliation, and extract fabric quantities from the allocated historical sales orders; S2. Conduct market trend analysis based on fabric categories, and at the same time obtain customer size data. Combine the size data with market trends to set a consumption rate threshold for fabrics, and combine it with the transaction frequency in historical sales orders to set an extra number of days for customers. S3. Extract the order time of customers based on historical sales orders, compare the fabric quantity and consumption time threshold corresponding to the latest order time with the extra days, and classify customers into stable customers and unstable customers based on the comparison results. S4. For stable customers, analyze the consumption rate range by combining the order time of historical sales orders with the fabric quantity, and analyze the consumption rate trend by combining the consumption rate range. Then, combine the consumption rate trend with the market trend to analyze the stable consumption rate within the consumption rate range, and combine the stable consumption rate with the real time to analyze the remaining time for ordering. The steps in S4 are as follows: S4.1 For stable customers, analyze the consumption rate range by combining the order time of the historical sales orders corresponding to stable customers with the fabric quantity. First, obtain the consumption rate corresponding to each order, and then summarize all orders to obtain the consumption rate range. S4.2 Sort the consumption speed according to the order time, and perform consumption speed trend analysis on the sorted consumption speed to obtain the consumption speed trend of the customer. S4.3 Combine the consumption rate trend with the market trend to conduct a stable consumption rate analysis within the consumption rate range, and then select a consumption rate within the consumption rate range as the stable consumption rate corresponding to the stable customer. When the consumption rate trend is getting faster and faster, or the market trend shows that the heat is getting higher and higher, the faster the stable consumption rate is selected within the consumption rate range; When the consumption rate trend is getting slower and slower, or the market trend shows that the heat is getting lower and lower, the lower the stable consumption rate should be selected within the consumption rate range. S4.

4. Combine the stable consumption rate selected in S4.3 with the real-time difference time to analyze the remaining time for ordering. Calculate the consumption days based on the stable consumption rate and the fabric quantity corresponding to the latest order time. Then subtract the consumption days from the real-time difference time and use the subtracted value as the remaining time for ordering. S5. For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and use the minimum consumption rate to analyze the remaining time for ordering. At the same time, set the upper limit of production quantity and expiration time according to the transaction frequency. The steps in S5 are as follows: S5.1 For unstable customers, extract the minimum consumption rate within the consumption rate range corresponding to the unstable customer, and use the minimum consumption rate to analyze the remaining order time to obtain the remaining order time corresponding to the unstable customer. S5.2 Set the production quantity limit and expiration time according to the transaction frequency. At the same time, if the difference between the customer's time in S3.2 and the real time is greater than the expiration time, the customer is determined to be invalid and deleted from the list of unstable customers. The higher the transaction frequency, the higher the upper limit of production quantity and the longer the expiration time; The lower the transaction frequency, the lower the production quantity limit and the shorter the expiration time; S6. Production scheduling is based on the remaining time of orders, with stable customers having higher production priority than unstable customers.

2. The adaptive management method for textile fabric production based on production scheduling according to claim 1, characterized in that: The steps in S1 are as follows: S1.1 Obtain historical sales orders and simultaneously extract customers from the historical sales orders; S1.

2. Assign historical sales orders to customers, obtain the historical sales orders corresponding to each customer, extract the fabric quantity from the assigned historical sales orders, and obtain the fabric quantity of each order in the historical sales orders.

3. The adaptive management method for textile fabric production based on production scheduling according to claim 1, characterized in that: The steps in S2 are as follows: S2.1 Obtain the fabric varieties corresponding to historical sales orders, and then combine the fabric varieties with the network to conduct market trend analysis and obtain the market trends corresponding to the fabric varieties; S2.2 Obtain customer scale data, and combine the scale data with market trends to set a consumption rate threshold for fabrics; The larger the scale of the data, the faster the consumption rate threshold. The smaller the data size, the slower the consumption rate threshold. S2.3 Analyze the transaction frequency of customers' historical sales orders to obtain the corresponding transaction frequency of customers, and then set the number of extra days based on the transaction frequency and the total fabric quantity of historical sales orders. The higher the transaction frequency or the greater the total amount of fabric, the more extra days are awarded. The lower the transaction frequency or the smaller the total amount of fabric, the fewer the extra days.

4. The adaptive management method for textile fabric production based on production scheduling according to claim 1, characterized in that: The steps in S3 are as follows: S3.1 Extract the order time of customers based on historical sales orders to obtain the order time of each order in the historical sales orders; S3.2 Extract the order with the latest order time from the historical sales orders, and calculate the real-time difference time by combining the latest order time with the real-time time. Then compare the fabric quantity and consumption time threshold corresponding to the order with the real-time difference time and the number of extra days. If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, and the value of the real-time difference time exceeds the number of extra days, then the customer is judged as an unstable customer. If the fabric quantity combined with the consumption time threshold is less than the real-time difference time, but the value of the difference time does not exceed the extra days, it is judged as a stable customer. If the fabric quantity combined with the consumption time threshold is greater than the real-time difference time, the customer is directly identified as a stable customer.

5. The adaptive management method for textile fabric production based on production scheduling according to claim 1, characterized in that: The quantity of fabric to be produced corresponding to the remaining order time in S4 is the quantity of fabric corresponding to the latest order time of the customer. Similarly, the base quantity of the upper limit of production quantity in S5 is also the quantity of fabric corresponding to the latest order time of the customer. The upper limit of production quantity is less than the quantity of fabric corresponding to the latest order time. The upper limit of production quantity is set by calculating the percentage of the fabric quantity.

6. The adaptive management method for textile fabric production based on production scheduling according to claim 1, characterized in that: In the production process, S6 prioritizes the production of fabric varieties and quantities with less remaining order time, and ensures that the corresponding fabric quantity can be produced quantitatively when the remaining order time is completed. When a customer fails, the fabric produced for that customer is directly added to the fabric to be produced.

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