A method and system for intelligent scheduling of product production and processing based on M0M system

Abstract

The invention discloses a product production and processing intelligent scheduling method and system based on a MOM system, and relates to the technical field of product production and processing scheduling. Based on the MOM system, live broadcast sales information of merchants of brand clothing on an e-commerce live broadcast platform is collected to obtain order information data, and production information data is obtained in combination with the production and processing conditions of manufacturers for clothing of different sizes. The order information data and the production information data are analyzed to obtain an order production scheduling index Zjj of each merchant, and merchant orders that need to be prioritized for production scheduling are determined and marked according to the size of the order production scheduling index Zjj, and multiple marked orders are merged into a priority production set order and a resource analysis instruction is issued, and the required original cloth purchase quantity Ncg is obtained by fitting by analyzing the size quantity information and the cloth demand information data, and then a corresponding original cloth purchase plan is formulated.

Description

Technical Field

[0001] The present invention relates to the technical field of product production and processing scheduling, and specifically to a product production and processing intelligent scheduling method and system based on a M0M system. Background Art

[0002] With the development of big data and intelligent technology, many companies have gradually shifted from traditional empirical scheduling to data-driven intelligent scheduling in the production and processing process. Especially in the field of product production and processing scheduling, production and order management has become one of the core concerns of enterprises, requiring the system to be able to respond to market demand in real time, dynamically adjust production resource allocation, and improve production efficiency and delivery capabilities. Especially for the production scheduling of e-commerce live clothing orders, the order delivery time is tight, the quantity fluctuates greatly, and the size requirements are diverse. Therefore, accurate production planning and flexible scheduling management are required to cope with the rapid changes in e-commerce sales.

[0003] At present, the production scheduling of most enterprises is still at the stage of experience and static scheduling, and it is difficult to dynamically adjust fabric procurement, size production and order priority, which affects production efficiency and resource utilization. In addition, traditional procurement and inventory management methods are mostly regular procurement and quantitative safety inventory, which is not only difficult to follow the fluctuations in order demand, but also easy to cause size out-of-stock and inventory backlog and shortage. Therefore, by introducing the intelligent scheduling and real-time analysis process of the M0M system, by accurately collecting the merchant's order demand and the manufacturer's production information, judging the order priority, optimizing the raw material procurement and supply chain flexibility, and realizing refined and data-based intelligent production scheduling management, it effectively solves the shortcomings of response delay and uneven resource allocation in order management, makes up for the information fragmentation problem in the traditional scheduling method, and improves the response efficiency and production flexibility of orders. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method and system for intelligent scheduling of product production and processing based on a M0M system, which solves the problems in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a product production and processing intelligent scheduling system based on the M0M system, including a data acquisition module, a scheduling analysis module, a production scheduling module, a resource analysis module and a procurement planning module;

[0006] The data acquisition module is used to collect sales information of brand clothing live broadcast by various merchants on the e-commerce live broadcast platform according to the MOM system, obtain order information data, and obtain production information data according to the production and processing conditions of manufacturers for clothing of different sizes;

[0007] The scheduling analysis module is used to analyze the order information data and production information data, and obtain the order production scheduling index Zjj of each merchant through deep machine learning calculation;

[0008] The production scheduling module is used to determine and mark the merchant orders that need priority production scheduling based on the value of the order production scheduling index Zjj of each merchant, merge several marked merchant orders into a priority production set order, and issue a resource analysis instruction;

[0009] The resource analysis module is used to, after receiving the resource analysis instruction, collect statistics on each clothing size of the priority production set order, obtain priority production size information data, and obtain fabric demand information data and remaining fabric information data according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse;

[0010] The procurement planning module is used to analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric procurement quantity Ncg, and formulate a corresponding original fabric procurement plan based on the numerical value of the original fabric procurement quantity Ncg.

[0011] Preferably, the data collection module includes a sales data collection unit and a production data collection unit;

[0012] The sales data collection unit is used to connect the MOM system with the data interface of the e-commerce platform, obtain the data authorization of the merchant, build a real-time data synchronization mechanism, import and record the sales information of each merchant live broadcast and the delivery time information set by the platform agreement in batches according to the merchant ID, so as to obtain the order information data of the brand clothing in each merchant live broadcast room on the e-commerce live broadcast platform during the sales cycle, and the order information data includes the sales quantity Nbt of different clothing sizes of each merchant order and the number of days to be shipped Tdf of each merchant order;

[0013] The production data acquisition unit is used to connect the M0M system with the manufacturer's enterprise resource planning platform and manufacturing execution platform, and in combination with the sensors and data acquisition equipment installed on the production line, monitor the production and processing of clothing of different sizes and obtain production information data, which includes the production efficiency Vds and production cost Ydj of each clothing size.

[0014] Preferably, the scheduling analysis module includes an order analysis unit, a size analysis unit and a comprehensive analysis unit;

[0015] The order analysis unit calculates the sum of the sales quantities Nbt of different clothing sizes of the orders of each merchant after feature extraction based on the order information data, obtains the sales quantities Nxs of all clothing sizes of the orders of each merchant, and calculates the order urgency coefficient Xqz of the corresponding merchant order by linearly normalizing the sales quantities Nxs of all clothing sizes of the orders of each merchant and the number of days to be shipped Tdf of the corresponding merchant order, which is specifically obtained by the following formula:

[0016] ;

[0017] In the formula, Represented as the sales quantity of all clothing sizes of the i-th merchant order, It is represented as the mean sales quantity of all merchants’ orders. It is represented by the number of days to be shipped for the i-th merchant order. It is represented by the average number of days to be shipped for all merchant orders, i = 1, 2, 3, ..., n, n represents the number of merchants in the order, and All are weight values.

[0018] Preferably, the size analysis unit is used to analyze the order information data and the production information data, and after dimensionless processing, to obtain the size distribution influence coefficient Xyx of each merchant order by fitting, which is specifically obtained by the following formula;

[0019] ;

[0020] In the formula, It is represented by the sales quantity of the j-th clothing size of the i-th merchant order, Represented as the sales quantity of all clothing sizes of the i-th merchant order, Expressed as the production cost of the j-th clothing size, It is expressed as the production efficiency of the j-th clothing size, j=1, 2, 3, ..., m, and m represents the type of clothing size.

[0021] Preferably, the comprehensive analysis unit is used to associate the order urgency coefficient Xqz of each merchant order with the size distribution influence coefficient Xyx of the corresponding merchant order, and obtain the order production scheduling index Zjj of each merchant order after dimensionless processing; specifically, it is obtained by the following formula:

[0022] ;

[0023] In the formula, and They are respectively represented as the weight values ​​of the order urgency coefficient Xqz and the size distribution influence coefficient Xyx, and A is represented as the correction constant.

[0024] Preferably, the production scheduling module is used to calculate the order production scheduling index of each merchant order based on the comprehensive analysis unit. The order production scheduling index Zjj of several merchant orders is obtained respectively, and the mean value of the order production scheduling index is calculated based on the statistical mean algorithm. ;

[0025] The order production scheduling index Zjj and the mean value of several merchant orders Compare the size and obtain the order production scheduling index Zjj exceeding the mean The corresponding merchant orders are marked, several marked merchant orders are merged into a priority production collection order, and resource analysis instructions are issued to the outside.

[0026] Preferably, the resource analysis module is used to, after receiving the resource analysis instruction, perform statistics on each clothing size of the priority production set order, obtain priority production size information data of the priority production set order, and the priority production size information data includes the sales quantity Nyx of each priority production size; and obtain fabric demand information data and remaining fabric information data according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, the fabric demand information data includes the original fabric quantity Ndj required for a single piece of each priority production size; the remaining fabric information data includes the total original fabric quantity Nsy remaining in the warehouse.

[0027] Preferably, the procurement planning module includes a demand analysis unit and a procurement unit;

[0028] The demand analysis unit is used to analyze the priority production size information data and the fabric demand information data to obtain the original total fabric demand Nzs. The original total fabric demand Nzs is obtained by the following formula:

[0029] ;

[0030] In the formula, It is expressed as the number of the kth priority production size, It represents the original amount of fabric required for a single piece of the kth priority production size, k = 1, 2, 3, ..., s, s represents the priority production size type;

[0031] The original cloth demand amount Nzs of the priority production set order is associated with the remaining cloth information data to obtain the original cloth purchase amount Ncg, and the original cloth purchase amount Ncg is obtained by the following formula;

[0032] ;

[0033] Where Nsy represents the total amount of original fabric remaining in the warehouse.

[0034] Preferably, the purchasing unit is used to determine whether the remaining amount of original fabric in the manufacturer's warehouse meets the total amount of original fabric required for the priority production of the aggregate order according to the value of the original fabric purchase amount Ncg, and formulate a corresponding original fabric purchase plan, and the specific process is as follows;

[0035] If the original fabric purchase quantity Ncg>0, it means that the remaining quantity of original fabric in the current factory warehouse does not meet the total quantity of original fabric required for the priority production set order, and an original fabric purchase plan is generated. The original fabric purchase plan includes formulating the original fabric purchase batches and quantities according to the original fabric purchase quantity Ncg and the number of days to be shipped Tdf of the priority production set order, giving priority to meeting the recent production needs, and signing a purchase contract to clarify the delivery time of each batch of purchased original fabrics;

[0036] If the original fabric purchase quantity Ncg≤0, it means that the remaining quantity of original fabric in the current manufacturer's warehouse satisfies the total quantity of original fabric required for the priority production of the aggregate order, and no additional original fabric purchase plan is generated.

[0037] A method for intelligent scheduling of product production and processing based on a M0M system, comprising the following steps:

[0038] Step 1: According to the M0M system, the sales information of brand clothing live broadcast by each merchant on the e-commerce live broadcast platform is collected to obtain order information data, and the production information data is obtained according to the production and processing conditions of the manufacturer for clothing of different sizes; and the order information data and production information data are analyzed, and the order production scheduling index Zjj of each merchant is obtained through deep machine learning calculation;

[0039] Step 2: According to the value of the order production scheduling index Zjj of each merchant, the merchant orders that need to be prioritized for production scheduling are determined and marked, and several marked merchant orders are merged into a priority production set order, and a resource analysis instruction is issued;

[0040] Step 3: after receiving the resource analysis instruction, statistics are collected on each clothing size of the priority production set order to obtain priority production size information data, and according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, fabric demand information data and remaining fabric information data are obtained;

[0041] Step 4: Analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric purchase quantity Ncg, and formulate a corresponding original fabric purchase plan based on the value of the original fabric purchase quantity Ncg.

[0042] The present invention provides a method and system for intelligent scheduling of product production and processing based on a MOM system, which has the following beneficial effects:

[0043] (1) Through the construction of the M0M system, the sales information of the e-commerce platform and the production information of the manufacturer are collected in real time, and the dynamic association between order and production data is realized, reflecting the current order demand and production capacity. Deep machine learning is used to analyze these data and generate the order production scheduling index Zjj of each merchant, so as to accurately judge the priority of each order. Based on the numerical value of the scheduling index Zjj, the merchant orders that need to be prioritized for production scheduling are determined and marked. Several marked merchant orders are merged into a priority production collection order to ensure that production resources are allocated to the most urgent orders first, thereby optimizing the utilization efficiency of resources. According to the requirements of each size of the priority production collection order, the original fabric demand is counted, and a demand replenishment plan is formulated in combination with the current inventory situation of the warehouse, avoiding the risk of raw material shortages caused by size or inventory differences, ensuring It not only hinders the continuity of production, but also further associates the fabric demand with the inventory information, calculates the original fabric purchase volume Ncg that needs to be purchased, and ensures the timely supply of priority production orders through flexible procurement plans, avoiding the problems of inventory backlog and shortage, and solves the demand forecasting, priority scheduling and procurement flexibility that are difficult to achieve in traditional production and processing methods, and improves resource management and production response speed. On the whole, by introducing the intelligent scheduling and real-time analysis process of the M0M system, by accurately collecting the merchant's order demand and the manufacturer's production information, judging the order priority, optimizing the raw material procurement and supply chain flexibility, and realizing refined and data-based intelligent production scheduling management, it effectively solves the shortcomings of response delay and uneven resource allocation in order management, makes up for the information fragmentation problem in the traditional scheduling method, and improves the order response efficiency and production flexibility.

[0044] (2) Through the division of labor and collaboration among the order analysis, size analysis and comprehensive analysis units, refined scheduling analysis of orders from different merchants is achieved, thereby improving the accuracy of production priority judgment. The order analysis unit obtains the order urgency coefficient Xqz after normalization, which can measure the delivery urgency of each merchant's order to respond to the market demand for fast delivery. The size analysis unit performs dimensionless processing on the order data and production data to obtain the size distribution influence coefficient Xyx, taking into account the production cost and efficiency of different sizes, and providing data support for the rational allocation of production resources. The comprehensive analysis unit associates the order urgency coefficient Xqz with the size distribution influence coefficient Xyx. The final order production scheduling index Zjj comprehensively reflects the urgency and production difficulty of the order, achieving a more scientific production priority sorting. The average value of the order production scheduling index is calculated through the production scheduling module. Analysis can be above the mean The orders are merged into priority production set orders and marked to ensure that urgent orders are met first and production resources are reasonably allocated. Based on detailed analysis and intelligent decision-making, it effectively avoids the problem of low production efficiency caused by information lag and uneven resource allocation in traditional scheduling. It can ensure that resources are prioritized to high-demand and urgent orders when there is large volatility in orders, thereby improving order response speed and customer satisfaction.

[0045] (3) Through the linkage between the demand analysis unit and the procurement unit, the procurement management of raw fabrics is optimized. The demand analysis unit analyzes the priority production size information data and fabric demand data, calculates the actual total raw fabric demand Nzs, and combines the total raw fabric remaining in the current warehouse Nsy to obtain the raw fabric procurement quantity Ncg, ensuring that during production peaks and emergency order priority scheduling, the manufacturer can accurately judge the adequacy of fabric inventory and avoid production interruptions and delays caused by insufficient raw materials. The procurement unit determines whether additional purchases are needed based on the raw fabric procurement quantity Ncg, and arranges procurement batches and quantities based on the raw fabric procurement quantity Ncg and the number of days to be shipped Tdf of the priority production collection order, ensuring that fabrics arrive on demand and in batches in a timely manner. The above analysis process not only improves the accuracy and timeliness of fabric procurement, but also avoids warehouse backlogs and waste, reduces raw material costs and improves production response speed, meets real-time order needs, and reduces the complexity of inventory management, realizing a cost-saving and efficient intelligent procurement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a block diagram of a product production and processing intelligent scheduling system based on the M0M system of the present invention;

[0047] Figure 2 The present invention is a flowchart of an intelligent scheduling method for product production and processing based on a M0M system. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1

[0050] See also Figure 1 The present invention provides a product production and processing intelligent scheduling system based on the M0M system, including a data acquisition module, a scheduling analysis module, a production scheduling module, a resource analysis module and a procurement planning module;

[0051] The data acquisition module is used to collect sales information of brand clothing live broadcast by various merchants on the e-commerce live broadcast platform according to the MOM system, obtain order information data, and obtain production information data according to the production and processing conditions of manufacturers for clothing of different sizes;

[0052] The scheduling analysis module is used to analyze the order information data and production information data, and obtain the order production scheduling index Zjj of each merchant through deep machine learning calculation;

[0053] The production scheduling module is used to determine and mark the merchant orders that need priority production scheduling based on the value of the order production scheduling index Zjj of each merchant, merge several marked merchant orders into a priority production set order, and issue a resource analysis instruction;

[0054] The resource analysis module is used to, after receiving the resource analysis instruction, collect statistics on each clothing size of the priority production set order, obtain priority production size information data, and obtain fabric demand information data and remaining fabric information data according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse;

[0055] The procurement planning module is used to analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric procurement quantity Ncg, and formulate a corresponding original fabric procurement plan based on the numerical value of the original fabric procurement quantity Ncg.

[0056] In the present embodiment, a product production and processing intelligent scheduling system based on the M0M system has five core modules: a data acquisition module, a scheduling analysis module, a production scheduling module, a resource analysis module, and a procurement planning module. It realizes intelligent optimization scheduling management of the entire process from order information collection to raw material procurement. Through the data acquisition module, the sales information data of each merchant of brand clothing can be obtained in real time from the e-commerce live broadcast platform, and detailed production information data can be obtained in combination with the manufacturer's production and processing of clothing of different sizes. The scheduling analysis module performs intelligent analysis of order information and production information through deep machine learning, thereby calculating the order production scheduling index Zjj of each merchant, realizing quantitative evaluation of order priority, and providing a scientific basis for the accurate sorting of production plans. Next, the production scheduling module determines and marks the merchant orders that need priority production scheduling based on the numerical value of the order production scheduling index Zjj of each merchant, merges several marked merchant orders into a priority production set order, and issues a resource analysis instruction to ensure efficient allocation of production resources. The resource analysis module After receiving the resource analysis instruction, the priority production size data is further counted, and combined with the remaining original fabric in the manufacturer's warehouse, fabric demand information data and remaining fabric information data are generated to ensure the reasonable allocation of production raw materials. Finally, the procurement planning module calculates the original fabric purchase quantity Ncg according to the priority size demand and fabric inventory, and formulates an accurate fabric procurement plan based on this to avoid excessive inventory backlogs and raw material shortages. Compared with the existing technology, accurate analysis and intelligent sorting of order priorities are achieved through data collection and deep machine learning, reducing the subjective errors and response delays of manual judgment. The resource analysis and procurement planning modules further strengthen the reasonable allocation of production resources, avoid resource waste and material shortages, and improve production efficiency and cost-effectiveness. In short, the intelligent scheduling method based on the M0M system forms a closed-loop optimization process from order processing to raw material procurement. By accurately collecting merchant order requirements and manufacturer production information, judging order priorities, optimizing raw material procurement and supply chain flexibility, and realizing refined and data-based intelligent production scheduling management.

[0057] Example 2

[0058] Please refer to Figure 1 ,Specifically: the data acquisition module includes a sales data acquisition unit and a production data acquisition unit;

[0059] The sales data collection unit is used to connect the MOM system with the data interface of the e-commerce platform, obtain the data authorization of the merchant, build a real-time data synchronization mechanism, import and record the sales information of each merchant live broadcast and the delivery time information set by the platform agreement in batches according to the merchant ID, so as to obtain the order information data of the brand clothing in each merchant live broadcast room on the e-commerce live broadcast platform during the sales cycle, and the order information data includes the sales quantity Nbt of different clothing sizes of each merchant order and the number of days to be shipped Tdf of each merchant order;

[0060] The production data acquisition unit is used to connect the M0M system with the manufacturer's enterprise resource planning platform and manufacturing execution platform, and in combination with the sensors and data acquisition equipment installed on the production line, monitor the production and processing of clothing of different sizes and obtain production information data, which includes the production efficiency Vds and production cost Ydj of each clothing size.

[0061] In this embodiment, data connection from the e-commerce platform to the production end is achieved through the synergy of the sales data collection unit and the production data collection unit in the data collection module. The sales data collection unit obtains the order information data of live sales of different merchants in a timely manner through real-time synchronization with the e-commerce platform interface, thereby ensuring the accuracy and real-time nature of the sales data. At the same time, the production data collection unit combines the enterprise resource planning platform and the manufacturing execution platform, as well as the data collection of the production line sensors, to grasp the production efficiency Vds and production cost Ydj of each clothing size, which can not only achieve accurate tracking of order sales, but also dynamically monitor the actual processing capacity and cost of the production end. This comprehensive and real-time data collection mechanism provides data support for subsequent intelligent scheduling and resource planning, which helps to improve production response speed and optimize resource allocation in a rapidly changing market environment, and effectively reduce inventory pressure and cost investment.

[0062] Example 3

[0063] Please refer to Figure 1 ,Specifically: the scheduling analysis module includes an order analysis unit, a size analysis unit and a comprehensive analysis unit;

[0064] The order analysis unit calculates the sum of the sales quantities Nbt of different clothing sizes of the orders of each merchant after feature extraction based on the order information data, obtains the sales quantities Nxs of all clothing sizes of the orders of each merchant, and calculates the order urgency coefficient Xqz of the corresponding merchant order by linearly normalizing the sales quantities Nxs of all clothing sizes of the orders of each merchant and the number of days to be shipped Tdf of the corresponding merchant order, which is specifically obtained by the following formula:

[0065] ;

[0066] In the formula, Represented as the sales quantity of all clothing sizes of the i-th merchant order, It is represented as the mean sales quantity of all merchants’ orders. It is represented by the number of days to be shipped for the i-th merchant order. It is represented by the average number of days to be shipped for all merchant orders, i = 1, 2, 3, ..., n, n represents the number of merchants in the order, and All are weight values.

[0067] Specifically, the size analysis unit is used to analyze the order information data and the production information data, and after dimensionless processing, to obtain the size distribution influence coefficient Xyx of each merchant order by fitting, which is specifically obtained by the following formula;

[0068] ;

[0069] In the formula, It is represented by the sales quantity of the j-th clothing size of the i-th merchant order, Represented as the sales quantity of all clothing sizes of the i-th merchant order, Expressed as the production cost of the j-th clothing size, It is expressed as the production efficiency of the j-th clothing size, j=1, 2, 3, ..., m, and m represents the type of clothing size.

[0070] Specifically, the comprehensive analysis unit is used to associate the order urgency coefficient Xqz of each merchant order with the size distribution influence coefficient Xyx of the corresponding merchant order, and obtain the order production scheduling index Zjj of each merchant order after dimensionless processing; specifically, it is obtained by the following formula:

[0071] ;

[0072] In the formula, and They are respectively represented as the weight values ​​of the order urgency coefficient Xqz and the size distribution influence coefficient Xyx, and A is represented as the correction constant.

[0073] Specifically, the production scheduling module is used to calculate the order production scheduling index of each merchant order based on the comprehensive analysis unit. The order production scheduling index Zjj of several merchant orders is obtained respectively, and the mean value of the order production scheduling index is calculated based on the statistical mean algorithm. ;

[0074] The order production scheduling index Zjj and the mean value of several merchant orders Compare the size and obtain the order production scheduling index Zjj exceeding the mean The corresponding merchant orders are marked, several marked merchant orders are merged into a priority production collection order, and resource analysis instructions are issued to the outside.

[0075] In this embodiment, by extracting features from order data and performing linear normalization processing, the order urgency coefficient Xqz can be quickly calculated to reflect the urgency of each merchant's order, ensuring that resources are preferentially allocated to orders with urgent delivery requirements. At the same time, the size analysis unit calculates the size distribution influence coefficient Xyx based on the production efficiency Vds and production cost Ydj of each clothing size in the order, thereby realizing a quantitative evaluation of the production priority of different sizes. The comprehensive analysis unit calculates the order production scheduling index Zjj by associating the order urgency coefficient Xqz with the size distribution influence coefficient Xyx, thereby constructing a system that combines the urgency of order demand with the size distribution influence coefficient Xyx. The comprehensive indicator that integrates production cost, efficiency and balance improves the accuracy and scientificity of order scheduling. In the production scheduling module, the order production scheduling index Zjj is aggregated according to the statistical mean algorithm, which can accurately identify orders that need to be prioritized and generate priority production set orders. It promotes resource optimization allocation by issuing resource analysis instructions. This process, based on detailed analysis and intelligent decision-making, effectively avoids the problem of low production efficiency caused by information lag and uneven resource allocation in traditional scheduling, and can ensure that resources are prioritized to high-demand and urgent orders when there is large volatility in orders, thereby improving order response speed and customer satisfaction.

[0076] Example 4

[0077] Please refer to Figure 1 Specifically: the resource analysis module is used to, after receiving the resource analysis instruction, perform statistics on each clothing size of the priority production set order, obtain priority production size information data of the priority production set order, and the priority production size information data includes the sales quantity Nyx of each priority production size; and according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, obtain fabric demand information data and remaining fabric information data, the fabric demand information data includes the original fabric quantity Ndj required for each single piece of each priority production size; the remaining fabric information data includes the total remaining original fabric quantity Nsy in the warehouse.

[0078] In this embodiment, by receiving the resource analysis instruction of the priority production set order, the demand situation of each priority production size is systematically counted, and the priority production size information data is obtained and generated, including the sales quantity Nyx of each priority production size. Based on this information, the module combines the manufacturer's fabric demand for the production of clothing of different sizes and the real-time status of the remaining original fabric in the current warehouse to further obtain fabric demand information data and remaining fabric information data. The fabric demand information data clarifies the original fabric quantity Ndj required for a single piece, and the remaining fabric information provides the total remaining original fabric quantity Nsy in the warehouse. Through real-time and accurate fabric demand analysis and warehouse surplus material monitoring, it is ensured that the supply of raw materials required for the priority production order can be responded to quickly, avoiding the occurrence of material shortages and excessive purchases, reducing production delays and inventory pressure, and at the same time, through dynamic fabric demand and surplus analysis, accurate basic data support is provided for subsequent procurement planning, improving the utilization efficiency of raw materials and the coordination of the supply chain.

[0079] Example 5

[0080] Please refer to Figure 1 ,Specifically: the procurement planning module includes a demand analysis unit and a procurement unit;

[0081] The demand analysis unit is used to analyze the priority production size information data and the fabric demand information data to obtain the original total fabric demand Nzs. The original total fabric demand Nzs is obtained by the following formula:

[0082] ;

[0083] In the formula, It is expressed as the number of the kth priority production size, It represents the original amount of fabric required for a single piece of the kth priority production size, k = 1, 2, 3, ..., s, s represents the priority production size type;

[0084] The original cloth demand amount Nzs of the priority production set order is associated with the remaining cloth information data to obtain the original cloth purchase amount Ncg, and the original cloth purchase amount Ncg is obtained by the following formula;

[0085] ;

[0086] Where Nsy represents the total amount of original fabric remaining in the warehouse.

[0087] Specifically, the purchasing unit is used to determine whether the remaining amount of original fabric in the manufacturer's warehouse meets the total amount of original fabric required for the priority production of the aggregate order according to the value of the original fabric purchase amount Ncg, and formulate a corresponding original fabric purchase plan. The specific process is as follows;

[0088] If the original fabric purchase quantity Ncg>0, it means that the remaining quantity of original fabric in the current factory warehouse does not meet the total quantity of original fabric required for the priority production set order, and an original fabric purchase plan is generated. The original fabric purchase plan includes formulating the original fabric purchase batches and quantities according to the original fabric purchase quantity Ncg and the number of days to be shipped Tdf of the priority production set order, giving priority to meeting the recent production needs, and signing a purchase contract to clarify the delivery time of each batch of purchased original fabrics;

[0089] If the original fabric purchase quantity Ncg≤0, it means that the remaining quantity of original fabric in the current manufacturer's warehouse satisfies the total quantity of original fabric required for the priority production of the aggregate order, and no additional original fabric purchase plan is generated.

[0090] In this embodiment, accurate management of the original fabric demand is achieved through the demand analysis unit and the procurement unit. The demand analysis unit uses the priority production size information data and the fabric demand data to calculate the total original fabric demand Nzs, and associates it with the total original fabric remaining in the warehouse Nsy to obtain the original fabric procurement quantity Ncg. The procurement unit automatically determines the inventory level and formulates a reasonable procurement plan: when the original fabric procurement quantity Ncg>0, a batched fabric procurement plan is generated to ensure timely replenishment of inventory and give priority to meeting short-term production needs, and the delivery cycle is clearly stated in the contract to improve the supply chain collaboration efficiency; when the original fabric procurement quantity Ncg≤0, no additional original fabric procurement plan is generated to avoid unnecessary procurement. This process effectively solves the inventory backlog and shortage problems caused by demand fluctuations in traditional procurement plans, optimizes fabric procurement and inventory management strategies through real-time calculation, and thus realizes the precision and efficiency of raw material supply.

[0091] Example 6

[0092] Please refer to Figure 1 and Figure 2 , specifically: a product production and processing intelligent scheduling method based on M0M system, comprising the following steps;

[0093] Step 1: According to the M0M system, the sales information of brand clothing live broadcast by each merchant on the e-commerce live broadcast platform is collected to obtain order information data, and the production information data is obtained according to the production and processing conditions of the manufacturer for clothing of different sizes; and the order information data and production information data are analyzed, and the order production scheduling index Zjj of each merchant is obtained through deep machine learning calculation;

[0094] Step 2: According to the value of the order production scheduling index Zjj of each merchant, the merchant orders that need to be prioritized for production scheduling are determined and marked, and several marked merchant orders are merged into a priority production set order, and a resource analysis instruction is issued;

[0095] Step 3: after receiving the resource analysis instruction, statistics are collected on each clothing size of the priority production set order to obtain priority production size information data, and according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, fabric demand information data and remaining fabric information data are obtained;

[0096] Step 4: Analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric purchase quantity Ncg, and formulate a corresponding original fabric purchase plan based on the value of the original fabric purchase quantity Ncg.

[0097] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A product production and processing intelligent scheduling system based on the M0M system, characterized by: It includes data acquisition module, scheduling analysis module, production scheduling module, resource analysis module and procurement planning module; The data acquisition module is used to collect sales information of brand clothing live broadcast by various merchants on the e-commerce live broadcast platform according to the MOM system, obtain order information data, and obtain production information data according to the production and processing conditions of manufacturers for clothing of different sizes; The data collection module includes a sales data collection unit and a production data collection unit; The sales data collection unit is used to connect the MOM system with the data interface of the e-commerce platform, obtain the data authorization of the merchant, build a real-time data synchronization mechanism, import and record the sales information of each merchant live broadcast and the delivery time information set by the platform agreement in batches according to the merchant ID, so as to obtain the order information data of the brand clothing in each merchant live broadcast room on the e-commerce live broadcast platform during the sales cycle, and the order information data includes the sales quantity Nbt of different clothing sizes of each merchant order and the number of days to be shipped Tdf of each merchant order; The production data acquisition unit is used to connect the MOM system with the manufacturer's enterprise resource planning platform and manufacturing execution platform, and in combination with the sensors and data acquisition equipment installed on the production line, monitor the production and processing of clothing of different sizes and obtain production information data, wherein the production information data includes the production efficiency Vds and production cost Ydj of each clothing size; The scheduling analysis module is used to analyze the order information data and production information data, and obtain the order production scheduling index Zjj of each merchant through deep machine learning calculation; The scheduling analysis module includes an order analysis unit, a size analysis unit and a comprehensive analysis unit; The order analysis unit calculates the sum of the sales quantities Nbt of different clothing sizes of the orders of each merchant after feature extraction based on the order information data, obtains the sales quantities Nxs of all clothing sizes of the orders of each merchant, and calculates the order urgency coefficient Xqz of the corresponding merchant order by linearly normalizing the sales quantities Nxs of all clothing sizes of the orders of each merchant and the number of days to be shipped Tdf of the corresponding merchant order, which is specifically obtained by the following formula: ； In the formula, Represented as the sales quantity of all clothing sizes of the i-th merchant order, It is represented as the mean sales quantity of all merchants’ orders. It is represented by the number of days to be shipped for the i-th merchant order. It is represented by the average number of days to be shipped for all merchant orders, i = 1, 2, 3, ..., n, n represents the number of merchants in the order, and All are weight values; The size analysis unit is used to analyze the order information data and the production information data, and after dimensionless processing, to obtain the size distribution influence coefficient Xyx of each merchant order by fitting, which is specifically obtained by the following formula; ； In the formula, It is represented by the sales quantity of the j-th clothing size of the i-th merchant order, Represented as the sales quantity of all clothing sizes of the i-th merchant order, Expressed as the production cost of the j-th clothing size, It is represented as the production efficiency of the jth clothing size, j = 1, 2, 3, ..., m, where m represents the type of clothing size; The production scheduling module is used to determine and mark the merchant orders that need priority production scheduling based on the value of the order production scheduling index Zjj of each merchant, merge several marked merchant orders into a priority production set order, and issue a resource analysis instruction; The resource analysis module is used to, after receiving the resource analysis instruction, collect statistics on each clothing size of the priority production set order, obtain priority production size information data, and obtain fabric demand information data and remaining fabric information data according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse; The procurement planning module is used to analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric procurement quantity Ncg, and formulate a corresponding original fabric procurement plan based on the numerical value of the original fabric procurement quantity Ncg.

2. According to claim 1, a product production and processing intelligent scheduling system based on the MOM system is characterized in that: The comprehensive analysis unit is used to associate the order urgency coefficient Xqz of each merchant order with the size distribution influence coefficient Xyx of the corresponding merchant order, and obtain the order production scheduling index Zjj of each merchant order after dimensionless processing; specifically, it is obtained by the following formula: ； In the formula, and They are respectively represented as the weight values ​​of the order urgency coefficient Xqz and the size distribution influence coefficient Xyx, and A is represented as the correction constant.

3. According to claim 2, a product production and processing intelligent scheduling system based on the MOM system is characterized in that: The production scheduling module is used to calculate the order production scheduling index of each merchant order based on the comprehensive analysis unit. The order production scheduling index Zjj of several merchant orders is obtained respectively, and the mean value of the order production scheduling index is calculated based on the statistical mean algorithm. ; The order production scheduling index Zjj and the mean value of several merchant orders Compare the size and obtain the order production scheduling index Zjj exceeding the mean The corresponding merchant orders are marked, several marked merchant orders are merged into a priority production collection order, and resource analysis instructions are issued to the outside.

4. According to claim 3, a product production and processing intelligent scheduling system based on the MOM system is characterized in that: The resource analysis module is used to, after receiving the resource analysis instruction, count the various clothing sizes of the priority production set order, obtain the priority production size information data of the priority production set order, and the priority production size information data includes the sales quantity Nyx of each priority production size; and according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, obtain fabric demand information data and remaining fabric information data, the fabric demand information data includes the original fabric quantity Ndj required for a single piece of each priority production size; the remaining fabric information data includes the total original fabric quantity Nsy remaining in the warehouse.

5. According to claim 4, a product production and processing intelligent scheduling system based on the MOM system is characterized in that: The procurement planning module includes a demand analysis unit and a procurement unit; The demand analysis unit is used to analyze the priority production size information data and the fabric demand information data to obtain the original total fabric demand Nzs. The original total fabric demand Nzs is obtained by the following formula: ； In the formula, It is expressed as the number of the kth priority production size, It represents the original amount of fabric required for a single piece of the kth priority production size, k = 1, 2, 3, ..., s, s represents the priority production size type; The original cloth demand amount Nzs of the priority production set order is associated with the remaining cloth information data to obtain the original cloth purchase amount Ncg, and the original cloth purchase amount Ncg is obtained by the following formula; ； Where Nsy represents the total amount of original fabric remaining in the warehouse.

6. According to claim 5, a product production and processing intelligent scheduling system based on the MOM system is characterized in that: The purchasing unit is used to determine whether the remaining amount of original fabric in the manufacturer's warehouse meets the total amount of original fabric required for the priority production of the aggregate order according to the value of the original fabric purchase amount Ncg, and formulate a corresponding original fabric purchase plan. The specific process is as follows; If the original fabric purchase quantity Ncg>0, it means that the remaining quantity of original fabric in the current factory warehouse does not meet the total quantity of original fabric required for the priority production set order, and an original fabric purchase plan is generated. The original fabric purchase plan includes formulating the original fabric purchase batches and quantities according to the original fabric purchase quantity Ncg and the number of days to be shipped Tdf of the priority production set order, giving priority to meeting the recent production needs, and signing a purchase contract to clarify the delivery time of each batch of purchased original fabrics; If the original fabric purchase quantity Ncg≤0, it means that the remaining quantity of original fabric in the current manufacturer's warehouse satisfies the total quantity of original fabric required for the priority production of the aggregate order, and no additional original fabric purchase plan is generated.

7. A method for intelligent scheduling of product production and processing based on a MOM system, used to implement the intelligent scheduling system for product production and processing based on a MOM system as described in any one of claims 1 to 6, characterized in that: The steps include: Step 1: According to the M0M system, the sales information of brand clothing live broadcast by each merchant on the e-commerce live broadcast platform is collected to obtain order information data, and the production information data is obtained according to the production and processing conditions of the manufacturer for clothing of different sizes; and the order information data and production information data are analyzed, and the order production scheduling index Zjj of each merchant is obtained through deep machine learning calculation; Step 2: According to the value of the order production scheduling index Zjj of each merchant, the merchant orders that need to be prioritized for production scheduling are determined and marked, and several marked merchant orders are merged into a priority production set order, and a resource analysis instruction is issued; Step 3: after receiving the resource analysis instruction, statistics are collected on each clothing size of the priority production set order to obtain priority production size information data, and according to the original fabric quantity required by the manufacturer for the priority production of clothing of different sizes and the remaining original fabric in the manufacturer's warehouse, fabric demand information data and remaining fabric information data are obtained; Step 4: Analyze the priority production size information data and fabric demand information data, and associate them with the remaining fabric information data to obtain the original fabric purchase quantity Ncg, and formulate a corresponding original fabric purchase plan based on the value of the original fabric purchase quantity Ncg.

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