Military clothing production resource intelligent allocation method and system based on big data

Abstract

The invention discloses a military clothing production resource intelligent allocation method and system based on big data, and relates to the field of production resource allocation, and the method comprises the steps: responding to the received allocation instruction tasks of a plurality of production resources, and determining the allocation type of each production resource; the production resource data, the production operation data and the geographic position of each military clothing production factory are acquired; based on the production resource data, the production operation data, the deployment type and the geographic position, determining the deployment priority of each military clothing production factory; according to the geographic position of each military clothing production factory, the allocation and transportation distance of production resources is determined; and determining a production resource allocation plan based on the allocation transportation distance and the allocation priority. The production efficiency of military garments can be effectively improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of production resource allocation, and in particular to a method and system for intelligent allocation of military clothing production resources based on big data. Background Art

[0002] With the development of science and technology and the increasing demand for security, the market demand for military textiles is also expanding. In the process of military clothing production, in order to ensure production quality, due to the special nature of its materials, the allocation of production resources for military clothing is very important.

[0003] Currently, traditional scheduling methods rely primarily on planning management and manual scheduling, with production plans often based on historical data and experience. Managers analyze past production records, sales data, and raw material availability, combining their own experience and judgment to determine production needs and formulate production plans accordingly. However, because manual scheduling involves communication and coordination across multiple links and departments, it is often inefficient. Especially during peak production periods or emergencies, scheduling based on manual experience may not meet production needs, further reducing production efficiency, leading to production delays, and impacting production plans. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for intelligently allocating military clothing production resources based on big data, which are used to improve the production efficiency of military clothing.

[0005] To achieve the above objectives, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, a method for intelligently allocating military clothing production resources based on big data is provided, the method comprising:

[0007] In response to receiving a deployment instruction task for a plurality of production resources, determining a deployment type for each production resource;

[0008] Obtain production resource data, production operation data, and geographic location of each military clothing production factory;

[0009] Determine the allocation priority for each military clothing production plant based on production resource data, production operation data, allocation type and geographical location;

[0010] Determine the deployment and transportation distance of production resources based on the geographical location of each military clothing production factory;

[0011] Determine the production resource allocation plan based on the allocation transportation distance and allocation priority.

[0012] In one possible implementation of the first aspect, the production resource data includes the type and quantity of production resources, and the production operation data includes production capacity data, production quality data, and production efficiency data. Determining the allocation priority of each military clothing production plant based on the production resource data, production operation data, allocation type, and geographic location includes:

[0013] Obtain the required quantity of production resources for each deployment type;

[0014] Determine the factory production capacity index of each military clothing production factory through the production and operation data of each military clothing production factory;

[0015] Determine the dynamic resource matching index based on the production resource requirements of each deployment type, the production resource types of each military clothing production factory, the production resource quantity and the factory production capacity indicators;

[0016] The allocation priority of each military clothing production factory is determined based on the factory production capacity indicators, dynamic resource matching index and geographical location of each military clothing production factory.

[0017] In a possible implementation of the first aspect, the factory production capacity index of each military clothing production factory is constructed using the production operation data of each military clothing production factory, including:

[0018] Determine capacity dimension indicators through production capacity data;

[0019] Determine quality dimension indicators through production quality data;

[0020] Determine efficiency dimension indicators through production efficiency data;

[0021] Obtaining demand data for the dispatch instruction task of each production resource and determining the dispatch instruction task type of each production resource, wherein the dispatch instruction task type includes a capacity demand type, a quality demand type, and an efficiency demand type;

[0022] According to the type of dispatching instruction task, the preset dynamic weight optimization algorithm is used to assign weight values ​​to the capacity dimension indicators, quality dimension indicators, and efficiency dimension indicators respectively;

[0023] Based on the weight values ​​of the production capacity dimension indicators, quality dimension indicators and efficiency dimension indicators, the production capacity dimension indicators, quality dimension indicators and efficiency dimension indicators are weighted and summed to determine the comprehensive score of each military clothing production factory. The comprehensive score is used to represent the factory production capacity indicators of each military clothing production factory.

[0024] In one possible implementation of the first aspect, determining a dynamic resource matching index based on the required quantity of production resources for each deployment type, the types of production resources, the quantity of production resources, and the production capacity indicators of each military clothing production factory includes:

[0025] Determine the allocation storage quantity corresponding to each allocation type in each military clothing production factory through the production resource type and production resource quantity of each military clothing production factory;

[0026] The ratio between the production resource demand corresponding to each deployment type and the deployment storage capacity of each military clothing production factory is used as the basic matching degree;

[0027] According to the deployment instruction task type, determine the type matching coefficient corresponding to each deployment instruction task type;

[0028] The product of the basic matching degree and the type matching coefficient corresponding to each deployment instruction task type is used as the dynamic resource matching degree index.

[0029] In a possible implementation of the first aspect, determining, according to the deployment instruction task type, a type matching coefficient corresponding to each deployment instruction task type includes:

[0030] By using the production capacity indicators and deployment instruction task types of military clothing production factories and a preset multi-dimensional evaluation quantitative model, the capacity coefficient of each military clothing production factory is determined;

[0031] Divide the production resource demand corresponding to the deployment type by the deployment storage capacity of each military clothing production factory to obtain the capacity demand ratio;

[0032] The capacity-demand ratio is multiplied by the capacity coefficient corresponding to the military clothing production factory to obtain the capacity-demand matching coefficient;

[0033] Obtain the number of inspected products corresponding to the quality requirement type and the total number of products in each military clothing production factory;

[0034] The value of dividing the number of tested products by the total number of products is taken as the quality inspection coverage rate;

[0035] Obtain the industry benchmark standard deviation corresponding to the efficiency demand type and the historical yield standard deviation of each military clothing production factory;

[0036] Calculate yield stability based on historical yield standard deviation and industry benchmark standard deviation;

[0037] Multiply the yield rate stability and quality inspection coverage to obtain the quality matching coefficient;

[0038] Obtain the deployment demand rhythm corresponding to the efficiency demand type and the actual production rhythm of each military clothing production factory;

[0039] The efficiency matching coefficient is obtained by multiplying the ratio between the deployment demand rhythm and the actual production rhythm with the capacity coefficient corresponding to the military clothing production factory.

[0040] In one possible implementation of the first aspect, determining the allocation priority of each military clothing production factory based on the factory production capacity indicator, dynamic resource matching index, and geographical location of each military clothing production factory includes:

[0041] Obtain the current production capacity data of each military clothing production factory, including the factory's average production rate and current production capacity;

[0042] Obtain the maximum production capacity corresponding to the factory production capacity indicator of each military clothing production factory from a preset database;

[0043] Subtract the current production capacity from the maximum production capacity to obtain the available production capacity, and divide the available production capacity by the average production rate of the factory to obtain the shortest response time of each military clothing production factory;

[0044] Sort the dynamic resource matching index of each military clothing production factory from large to small to form the first ranking sequence;

[0045] Determine the transportation distance based on the geographical location of each military clothing production factory in the first arrangement sequence;

[0046] Calculate the transportation time based on the transportation distance and the preset average transportation speed;

[0047] Determine the upper limit of the processing time for each military clothing production factory to handle the production resource allocation instruction task type based on the shortest response time and transportation time;

[0048] Sort the processing time limits from small to large, where the allocation with the smallest processing time limit has the highest priority.

[0049] In a possible implementation of the first aspect, the method further includes:

[0050] When the shortest response time of each military clothing production factory is the same, the production resource allocation rule is executed;

[0051] Among them, production resource allocation rules include:

[0052] S1. Obtain the processing time of the deployment instruction task of n production resources;

[0053] S2. Randomly assign m deployment instruction tasks whose processing time is less than a preset time threshold to m military clothing production factories in sequence, where m < n and m and n are both positive integers;

[0054] S3. Record the completion time of each deployment instruction task of m military clothing production factories, and mark the military clothing production factory that completes the deployment instruction task first;

[0055] S4. Assign the m+1th dispatch order task to the military clothing production factory that completed the dispatch order task first;

[0056] Steps S3 and S4 are executed in a loop until the allocation of the n production resource allocation instructions is completed.

[0057] In a possible implementation of the first aspect, determining a production resource allocation plan based on an allocation transportation distance and an allocation priority includes:

[0058] Obtain the historical transportation volume, average transportation cost and historical dispatch transportation distance of each military clothing production factory;

[0059] Construct a cost objective function based on historical transportation volume, average transportation cost and historical dispatch transportation distance;

[0060] Use the maximum deployment priority as a constraint;

[0061] Combine the cost objective function and constraints to build a production resource allocation model;

[0062] Input the dispatching transportation distance and dispatching priority into the production resource dispatching model to obtain the production resource dispatching plan.

[0063] In a second aspect, the present application provides a machine-readable storage medium having stored thereon instructions for enabling a machine to execute the above-mentioned method for intelligent allocation of military clothing production resources based on big data.

[0064] In a third aspect, the present application provides an electronic device, comprising:

[0065] a memory configured to store instructions; and

[0066] The processor is configured to call the instructions from the memory and implement the above-mentioned method for intelligent allocation of military clothing production resources based on big data when executing the instructions.

[0067] By using this technical solution to determine the allocation type for each production resource, the actual needs of different military clothing production plants can be determined, avoiding resource waste or shortages. Subsequently, based on production resource data, operational data, and geographic location, the allocation priority of each plant can be determined, effectively improving resource allocation efficiency and ensuring the production efficiency of military clothing production plants. By analyzing geographic location and selecting the shortest transportation route, logistics costs can be effectively reduced. By combining allocation priority and transportation distance, the optimal transportation plan can be planned to avoid empty vehicles or duplicate transportation, thereby optimizing transportation costs and reducing transportation time, thereby improving the efficiency of production resource allocation.

[0068] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flowchart of a method for intelligently allocating military clothing production resources based on big data provided in an embodiment of the present application;

[0070] Figure 2 A schematic diagram of a process for determining the allocation priority of each military clothing production factory provided in an embodiment of the present application. DETAILED DESCRIPTION

[0071] To make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation methods described herein are only used to illustrate and explain the embodiments of the present application and are not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0072] It should be noted that if the embodiments of the present application involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indications will also change accordingly.

[0073] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, the descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.

[0074] Figure 1 The following schematically shows a flow chart of a method for intelligently allocating military clothing production resources based on big data according to an embodiment of the present application. Figure 1 As shown, an embodiment of the present application provides a method for intelligently allocating military clothing production resources based on big data, which may include the following steps.

[0075] S110, in response to receiving a deployment instruction task for multiple production resources, determining a deployment type for each production resource;

[0076] S120, obtaining production resource data, production operation data, and geographic location of each military clothing production factory;

[0077] S130, determining the allocation priority of each military clothing production factory based on production resource data, production operation data, allocation type and geographical location;

[0078] S140. Determine the deployment and transportation distance of production resources based on the geographical location of each military clothing production factory;

[0079] S150. Determine a production resource allocation plan based on the allocation transportation distance and allocation priority.

[0080] In response to receiving multiple production resource allocation instruction tasks, the allocation type of each production resource is determined. Specifically, after receiving multiple production resource allocation instruction tasks, the allocation type of production resources needs to be determined. The allocation type of production resources can be determined according to actual conditions. In this embodiment, the allocation type of production resources can be material resource allocation and order resource allocation. For example, during the production process, there may be shortages of different types of material resources. Therefore, it is necessary to allocate material resources between different factories to ensure the timely supply of materials to maintain production continuity. In addition, when the production capacity of some factories exceeds the load, it is necessary to allocate the order resources that exceed the load to ensure that the orders can be delivered on time and meet customer needs.

[0081] Subsequently, production resource data, production operation data, and geographic location are obtained for each military clothing production factory. In this implementation, production resource data includes the types and quantities of production resources, while production operation data includes production capacity data, production quality data, and production efficiency data. These data can be obtained through the factory management system. Production resource types refer to the classification of various types of resources used in the production process; production resource quantity refers to the specific quantity or scale of various production resources owned by an enterprise within a specific period. Production operation data is a key basis for enterprises to understand production conditions, optimize production processes, and enhance competitiveness. Production capacity data, production quality data, and production efficiency data can reflect production operations from different perspectives. Production capacity data refers to the number of products an enterprise can produce within a specific period under established production organization and technical conditions; production quality data refers to a series of quantitative indicators used to measure, evaluate, and control products during the production process; and production efficiency data refers to a series of quantitative indicators and data used to measure and evaluate the efficiency level of the production process. Obtaining the geographic location of military clothing production factories can be achieved through electronic map services, thereby obtaining the geographic location of each military clothing production factory.

[0082] After obtaining production resource data, production operation data, and geographic location, the allocation priority of each military clothing production plant is determined based on this data, the types of military clothing to be allocated, and the location. Specifically, by comprehensively considering each plant's production resources, operational status, the types of military clothing required, and geographic location, the priority of each plant in the military clothing allocation task is evaluated and determined. Specifically, production resource data can be used to assess the adequacy of each plant's production resources, including raw materials, equipment, and manpower. Furthermore, the plant's resource utilization rate—that is, whether resources are being used effectively—can be examined. Military clothing production plants with high utilization rates demonstrate strong production capacity and may therefore receive higher allocation priority. Production operation data can also be used to determine the production efficiency and quality of each military clothing production plant. Plants with high production efficiency can complete production tasks more quickly and therefore may receive higher allocation priority. Military clothing quality is crucial, and plants with consistent production quality receive higher allocation priority. Next, we determine whether each military clothing production factory has a matching deployment type. If a matching military clothing production factory exists, it will be given a higher priority. Finally, we consider geographic location—that is, the transportation distance between the factory and the deployment destination. Closer factories may have lower transportation costs and faster response times during deployments, and thus may receive a higher priority.

[0083] Next, the geographical location of each military clothing production plant was used to determine the required transportation distance for the production resources. Specifically, detailed geographic information for each military clothing production plant, including latitude and longitude coordinates, location, and address, was collected. Using a geographic information system (GIS) or online mapping service, the actual transportation distance between the plant and the designated location was calculated.

[0084] After determining the dispatch and transportation distances for production resources, a production resource dispatch plan is determined based on these dispatch and transportation distances and dispatch priorities. Distance refers to the actual distance production resources must be transported from each production plant to the location of demand. Transportation distance directly impacts transportation costs, time, and resource loss during transportation. Allocation priority is the dispatch order assigned to each production plant or resource based on factors such as production demand, plant capacity, and resource importance. Specifically, in the dispatch plan, plants with high dispatch priorities are prioritized, and dispatch order is arranged based on their transport distances. When transport distance and dispatch priority conflict, a comprehensive trade-off is necessary. For example, Plant A has a high priority but a long transport distance, while Plant B has a low priority but a short transport distance. In this case, if demand is urgent and resource quality is high, Plant A may be prioritized. However, if time is more available and transportation costs are a key consideration, Plant B may be allocated more resources.

[0085] By using this technical solution to determine the allocation type for each production resource, the actual needs of different military clothing production plants can be determined, avoiding resource waste or shortages. Subsequently, based on production resource data, operational data, and geographic location, the allocation priority of each plant can be determined, effectively improving resource allocation efficiency and ensuring the production efficiency of military clothing production plants. By analyzing geographic location and selecting the shortest transportation route, logistics costs can be effectively reduced. By combining allocation priority and transportation distance, the optimal transportation plan can be planned to avoid empty vehicles or duplicate transportation, thereby optimizing transportation costs and reducing transportation time, thereby improving the efficiency of production resource allocation.

[0086] In one implementation of this embodiment, the production resource data includes the type and quantity of production resources, and the production operation data includes production capacity data, production quality data, and production efficiency data. Based on the production resource data, production operation data, allocation type, and geographic location, the allocation priority of each military clothing production factory is determined, including:

[0087] S210, obtaining the required quantity of production resources corresponding to each deployment type;

[0088] S220, constructing a factory production capacity indicator for each military clothing production factory based on the production and operation data of each military clothing production factory;

[0089] S230, determining a dynamic resource matching index based on the quantity of production resources required for each deployment type, the production resource types and quantity of production resources of each military clothing production factory, and the factory production capacity index;

[0090] S240. Determine the allocation priority of each military clothing production factory based on the factory production capacity index, dynamic resource matching index and geographical location of each military clothing production factory.

[0091] Figure 2 A schematic diagram of a process for determining the allocation priority of each military clothing production factory provided in an embodiment of the present application.

[0092] First, obtain the required quantity of production resources corresponding to each allocation type, that is, obtain the quantity required for the allocation type. For example, if production materials such as buttons and zippers need to be allocated, it is necessary to obtain the quantity required for the current buttons, zippers and other production materials.

[0093] Next, the production capacity index of each military clothing production factory is determined using the production and operation data of each factory. In this embodiment, the production capacity index is a comprehensive indicator used to measure each factory's ability and efficiency in producing military clothing. In this embodiment, the production and operation data includes production capacity data, production quality data, and production efficiency data. Specifically, the annual output and average monthly output of each military clothing production factory are determined using the production capacity data. Annual output refers to the total number of military clothing a factory can produce in a year, reflecting the factory's maximum production capacity and production scale. Average monthly output is the average of the annual output per month, reflecting the factory's production stability and sustainable production capacity. Production efficiency data can be used to determine indicators such as equipment utilization and per capita output. Equipment utilization refers to the ratio of the actual operating time of a factory's production equipment to the planned operating time, reflecting the efficiency of equipment use. Per capita output refers to the average number of military clothing produced per factory employee per month or year, reflecting the employee's production efficiency and labor productivity. Production quality data can be used to determine indicators such as product qualification rate and defective rate. The qualification rate refers to the ratio of qualified military clothing to total production, reflecting the factory's product quality control level; the defective rate refers to the ratio of unqualified military clothing to total production. Using these data indicators, we can ultimately determine the production capacity of each factory.

[0094] The dynamic resource matching index is determined based on the production resource requirements for each deployment type, the types of production resources for each military clothing production factory, the number of production resources, and the factory production capacity indicators. In other words, the degree of match between the production factory and the deployment requirements is comprehensively assessed using the production resource requirements for each deployment type, the types of production resources for each military clothing production factory, the number of production resources, and the factory production capacity indicators. The production resource requirements refer to the specific quantities of various resources required to produce military clothing based on the deployment type (e.g., clothing for different seasons or functions). For example, winter clothing may require a certain amount of thermal fabric, zippers, buttons, etc. The production resource types refer to the types of production resources available to each factory, such as fabrics, accessories, and equipment. Different factories may have different resource types, which determines the types of military clothing they can produce. The production resource quantities refer to the specific quantities of various production resources available to each factory. For example, a factory may have 1,000 meters of thermal fabric and 500 zippers. The factory production capacity index is used to comprehensively assess a factory's production capacity. Specifically, first, data on the types, quantities, and production capacity indicators of each factory's production resources, as well as the quantity of production resources required for each deployment type, were collected. For each military clothing production factory, the degree of match between its production resource types and the types of deployment requirements was determined (e.g., whether the factory's resource types cover the types required for deployment). Furthermore, the degree of match between the factory's production resource quantities and the deployment requirements was calculated (e.g., whether the factory's resource quantities meet the deployment requirements). A comprehensive assessment was conducted combining the factory's production capacity indicators and the aforementioned matching results to derive a dynamic resource matching index for each factory. This index, which can be a numerical value or a grade, represents the degree of match between the factory and the deployment requirements.

[0095] Finally, the allocation priority of each military clothing production factory is determined based on its factory production capacity indicator, dynamic resource matching index, and geographic location. In this embodiment, the factory production capacity indicator reflects the factory's production scale, efficiency, quality, and flexibility, such as annual output and equipment utilization. The dynamic resource matching index measures the degree of match between the type and quantity of a factory's production resources and the allocation requirements. Specifically, first, the production capacity indicator, dynamic resource matching index, and geographic location information of each factory are collected. Next, a comprehensive assessment is conducted for each factory, taking into account the weighting of production capacity, resource matching, and geographic location. For example, production capacity accounts for 50%, resource matching accounts for 30%, and geographic location accounts for 20%. Based on the weighting, a priority score is determined for each factory, with higher scores indicating higher priority. Finally, the factories are ranked according to their total scores to determine the allocation priority.

[0096] By determining the allocation priority of each military clothing production factory through production resource data, production operation data, allocation type and geographical location, it can effectively improve resource allocation efficiency, enhance production flexibility, improve logistics efficiency and reduce logistics costs.

[0097] In one implementation of this embodiment, the factory production capacity index of each military clothing production factory is constructed based on the production operation data of each military clothing production factory, including:

[0098] S310. Determine capacity dimension indicators based on production capacity data;

[0099] S320, determine quality dimension indicators through production quality data;

[0100] S330. Determine efficiency dimension indicators based on production efficiency data;

[0101] S340: Obtain demand data for the deployment instruction task of each production resource, and determine the deployment instruction task type of each production resource, wherein the deployment instruction task type includes a capacity demand type, a quality demand type, and an efficiency demand type;

[0102] S350: Based on the type of the dispatching instruction task, using a preset dynamic weight optimization algorithm, respectively assign weight values ​​to the capacity dimension indicator, the quality dimension indicator, and the efficiency dimension indicator;

[0103] S360. Based on the weight values ​​of the capacity dimension indicators, quality dimension indicators and efficiency dimension indicators, the capacity dimension indicators, quality dimension indicators and efficiency dimension indicators are weighted and summed to determine the comprehensive score of each military clothing production factory. The comprehensive score is used to represent the factory production capacity indicators of each military clothing production factory.

[0104] First, production capacity data is used to determine capacity indicators. In this embodiment, capacity indicators are a series of quantitative indicators used to measure an enterprise's production capacity, reflecting its production capacity in different aspects and under different conditions. Specifically, monthly production capacity data is determined from production capacity data, and each month's production capacity data is used to characterize capacity indicators. Subsequently, production quality data is used to determine quality indicators. Quality indicators are a series of quantitative indicators used to measure product quality, reflecting the quality characteristics of products in different aspects and stages. Specifically, quality indicators can be determined by calculating the yield rate of each military clothing production plant. The yield rate is determined by calculating the percentage of good products in the total production volume, and the quality indicators are then derived. Quality indicators reflect the enterprise's product quality control level and are important indicators for evaluating product quality. Finally, production efficiency data is used to determine efficiency indicators. Efficiency indicators are a series of quantitative indicators used to measure an enterprise's production efficiency, reflecting the enterprise's resource utilization, production speed, and the degree to which production capacity is utilized during the production process. Efficiency indicators can be determined by calculating equipment utilization. The equipment utilization rate is obtained by the percentage between the actual operating time of the equipment and the actual operating time, and then the efficiency dimension index is determined. The efficiency dimension index reflects the utilization degree of the equipment.

[0105] Secondly, the demand data of the allocation instruction task of each production resource is obtained, and the allocation instruction task type of each production resource is determined, wherein the allocation instruction task type is a capacity demand type, a quality demand type, and an efficiency demand type. That is, the allocation instruction task type of each production resource is determined according to the demand of the allocation instruction task of each production resource. For example, the demand of the allocation instruction task is a capacity demand, which means that the amount of capacity required by the allocation instruction task is large. In this embodiment, the capacity demand type refers to the allocation instruction of production resources based on the production plan and market demand of the enterprise, which is intended to ensure that production can meet the predetermined capacity target; the quality demand type refers to the allocation instruction of production resources based on product quality standards and customer requirements, which is intended to ensure that the products produced meet the predetermined quality specifications; the efficiency demand type refers to the allocation instruction of production resources based on the requirements of production efficiency and cost control, which is intended to improve production efficiency and reduce production costs.

[0106] Subsequently, based on the type of the dispatching instruction task, the preset dynamic weight optimization algorithm is used to assign weight values ​​to the capacity dimension indicators, quality dimension indicators, and efficiency dimension indicators respectively. The dynamic weight optimization algorithm is an algorithm that automatically adjusts the weight values ​​of different dimension indicators based on specific goals or task types. In production resource allocation, the dynamic weight optimization algorithm can dynamically assign weights to indicators of each dimension based on the different requirements of capacity, quality, and efficiency. Specifically, first, clarify the task type of the dispatching instruction, whether it is a capacity requirement type, a quality requirement type, or an efficiency requirement type. Secondly, determine the capacity dimension indicators, quality dimension indicators, and efficiency dimension indicators, and set weight values ​​for each dimension indicator based on historical data. In this embodiment, if the allocation instruction task type is a capacity requirement type, the weights corresponding to the capacity dimension indicator, quality dimension indicator and efficiency dimension indicator are 0.6, 0.2 and 0.2 respectively; if the allocation instruction task type is a quality requirement type, the weights corresponding to the capacity dimension indicator, quality dimension indicator and efficiency dimension indicator are 0.2, 0.6 and 0.2 respectively; if the allocation instruction task type is an efficiency requirement type, the weights corresponding to the capacity dimension indicator, quality dimension indicator and efficiency dimension indicator are 0.2, 0.2 and 0.6 respectively.

[0107] Based on the weighted values ​​of the capacity, quality, and efficiency indicators, a weighted sum of these three dimensions is performed to determine a comprehensive score for each military clothing production factory. This comprehensive score is used to represent the factory production capacity indicator of each military clothing production factory. In other words, after obtaining the weighted values ​​of the capacity, quality, and efficiency indicators, the indicators of the three dimensions of capacity, quality, and efficiency are integrated into a single comprehensive score through weighted summation, which is used to quantitatively evaluate the overall production capacity of the military clothing production factory. The preset indicator values ​​for the three dimensions of capacity, quality, and efficiency are multiplied by the previously obtained weighted values ​​of the three dimensions, and then added together to obtain a comprehensive score. The comprehensive score is used to represent the factory production capacity indicator of each military clothing production factory.

[0108] By using production operation data to construct factory production capacity indicators for each military clothing production factory, we can comprehensively quantify and evaluate production capacity, ensure the comprehensiveness and accuracy of the evaluation, and intuitively reflect production capacity through comprehensive scores, which helps to make decisions quickly and accurately.

[0109] In one implementation of this embodiment, a dynamic resource matching index is determined based on the production resource demand quantity of each deployment type, the production resource type and quantity of each military clothing production factory, and the factory production capacity index, including:

[0110] S410, determining the allocation storage quantity corresponding to each allocation type in each military clothing production factory based on the production resource type and production resource quantity of each military clothing production factory;

[0111] S420: The ratio between the required quantity of production resources corresponding to the deployment type and the deployment storage capacity of each military clothing production factory is used as the basic matching degree;

[0112] S430. Determine the production capacity of each military clothing production factory through the production capacity indicators of each factory;

[0113] S440: Determine a type matching coefficient corresponding to each type of deployment instruction task according to the deployment instruction task type;

[0114] S450: The product of the basic matching degree and the type matching coefficient corresponding to each deployment instruction task type is used as the dynamic resource matching degree index.

[0115] Through the production resource types and production resource quantities of each military clothing production factory, determine the allocation storage quantity corresponding to each allocation type in each military clothing production factory. That is to say, determine each allocation type. Next, obtain the production resource quantities corresponding to all production resource types in each military clothing production factory, compare the allocation types among all production resource types, and determine the allocation storage quantity corresponding to each allocation type.

[0116] The basic matching degree is then calculated as the ratio between the required production resource quantity for each deployment type and the allocated storage capacity of each military clothing production plant. Specifically, the required production resource quantity and allocated storage capacity for each deployment type are determined. The basic matching degree is determined by calculating the ratio of the required quantity to the allocated storage capacity. The basic matching degree quantifies the relationship between demand and storage capacity to assess the resource matching degree of the military clothing production plant. For example, if the required production resource quantity for the deployment type is 200 kg and the allocated storage capacity is 400 kg, the basic matching degree is 400 kg / 200 kg = 2, resulting in a basic matching degree of 2.

[0117] After determining the production capacity of each military clothing production plant, the corresponding type matching coefficient is determined based on the dispatch order task type. The type matching coefficient is used to quantitatively assess the degree of match between the plant's capacity and task requirements, with higher values ​​indicating a better match. Specifically, the corresponding type matching coefficient is determined based on the dispatch order task type (capacity requirement type, quality requirement type, and efficiency requirement type). The type matching coefficient for the capacity requirement type can be calculated by taking the ratio of the plant's maximum capacity to the task requirement and combining it with the capacity coefficient of the military clothing production plant. The capacity coefficient is a quantitative indicator used to measure a plant's ability to meet production requirements in various aspects of a military clothing production plant. The quality requirement type can be determined by calculating the yield rate stability, which can be determined by comparing the historical yield rate standard deviation to the industry benchmark standard deviation. The efficiency requirement type can be calculated by taking the ratio between the plant's dispatch demand cycle and the actual production cycle and combining it with the capacity coefficient of the military clothing production plant. For example, if the dispatch order task type is capacity-demanding, the factory's maximum capacity is 20 tons, and the task demand is 10 tons, the type matching coefficient for the capacity demand type is 10t / 20t = 0.5. Similarly, if the dispatch order task type is quality-demanding, the historical yield rate standard deviation is 1.15%, and the industry benchmark standard deviation is 2%. Therefore, the type matching coefficient for the quality demand type is 1.73. If the dispatch order task type is efficiency-demanding, the factory's dispatch demand cycle is 100 products per hour, and the actual production cycle is 120 products. Therefore, the type matching coefficient for the efficiency demand type is 100 / 120 = 1.2.

[0118] Next, the dynamic resource matching index is calculated by multiplying the basic matching degree by the type matching coefficient corresponding to each dispatch instruction task type. This index quantitatively assesses the degree of match between factory capabilities and task requirements by combining the basic matching degree and the type matching coefficient. In other words, the dynamic resource matching index is calculated by multiplying the type matching coefficient corresponding to each dispatch instruction task type by the basic matching degree.

[0119] By determining a dynamic resource matching index, we can intuitively quantify the degree to which each military clothing production plant meets the resource requirements for a specific allocation type. By combining the plant's production capacity indicators and the type of allocation order, we dynamically adjust the matching index, ensuring that the evaluation results are more closely aligned with actual production needs and providing a more accurate assessment of resource matching.

[0120] In one implementation of this embodiment, determining the type matching coefficient corresponding to each deployment instruction task type according to the deployment instruction task type includes:

[0121] S501. Determine the capacity coefficient of each military clothing production factory using a preset multi-dimensional evaluation quantitative model based on the production capacity and deployment instruction task type of the military clothing production factory;

[0122] S502: Divide the production resource demand quantity corresponding to the deployment type by the deployment storage capacity of each military clothing production factory to obtain a production capacity demand ratio;

[0123] S503. Multiply the capacity demand ratio by the capacity coefficient corresponding to the military clothing production factory to obtain the capacity demand matching coefficient;

[0124] S504. Obtain the number of inspected products corresponding to the quality requirement type and the total number of products of each military clothing production factory;

[0125] S505: Divide the number of inspected products by the total number of products as the quality inspection coverage rate;

[0126] S506. Obtain the industry benchmark standard deviation corresponding to the efficiency requirement type and the historical yield rate standard deviation of each military clothing production factory;

[0127] S507. Calculate the yield rate stability based on the historical yield rate standard deviation and the industry benchmark standard deviation;

[0128] S508. Multiply the yield rate stability and the quality inspection coverage rate to obtain a quality matching coefficient;

[0129] S509: Obtain the deployment demand tact corresponding to the efficiency demand type and the actual production tact of each military clothing production factory;

[0130] S510. Multiply the ratio between the deployment demand tact and the actual production tact by the capacity coefficient corresponding to the military clothing production factory to obtain an efficiency matching coefficient.

[0131] Through the production capacity and deployment instruction task type of the military clothing production factory, the capacity coefficient of each military clothing production factory is determined using a preset multi-dimensional evaluation quantitative model. In this embodiment, the preset multi-dimensional evaluation quantitative model is a quantitative model for measuring the capacity of the military clothing production factory in multiple aspects. Specifically, the production capacity and deployment instruction task type are input into the preset multi-dimensional evaluation quantitative model, and a quantitative index calculated by the multi-dimensional evaluation quantitative model is used to measure the factory's ability to meet specific deployment instruction task types. This coefficient comprehensively considers the factory's performance in multiple dimensions. The main purpose of the multi-dimensional evaluation quantitative model is to map the factory's production capacity and deployment instruction task type into a unified quantitative index (capacity coefficient). Its mathematical essence can be expressed as:

[0132] Capacity coefficient = f(production capacity vector, task demand vector | θ);

[0133] Among them, the production capacity vector includes dimensions such as equipment capacity, personnel skills, process stability, and supply chain flexibility; the task demand vector includes the key requirements of the corresponding task type (such as capacity threshold, quality standards, delivery cycle, etc.); θ represents the model parameters, which can be obtained through training and learning; f represents the fusion function, which is usually a weighted sum or a neural network nonlinear transformation.

[0134] First, the factory collects production data from a preset time period (such as equipment OEE, on-time delivery rate, and quality defect rate). This data is then divided into training, validation, and test sets, with task types (such as "emergency order" and "routine production") and corresponding capacity coefficients (expert ratings or actual completion rates) labeled. A neural network algorithm is then used to fit the relationship between input and output (capacity coefficient) using historical data.

[0135] After determining the capacity coefficient for each military clothing production plant, the required production resource quantity for each deployment type is divided by the deployment storage capacity of each military clothing production plant to obtain the capacity demand ratio. The capacity demand ratio reflects the ability of the plant's current storage capacity to meet the needs of a specific deployment task. The capacity demand ratio is calculated by dividing the required production resource quantity for each deployment type by the deployment storage capacity of each military clothing production plant. This ratio reflects the ability of the plant's current storage capacity to meet the needs of a specific deployment task. Specifically, based on the specific deployment instruction task, the required production resource type and quantity are determined, and the current production resource storage capacity available for deployment at each plant is queried. The capacity demand ratio is calculated by dividing the required production resource quantity for the deployment type by the plant's deployment storage capacity.

[0136] Next, the capacity-demand ratio is multiplied by the capacity coefficient corresponding to the military clothing production plant to obtain the capacity-demand matching coefficient. This coefficient is calculated by multiplying the capacity-demand ratio by the capacity coefficient corresponding to the military clothing production plant. It measures the degree of match between a plant's ability to meet specific production demands and demand. The capacity-demand ratio is the ratio of production demand to the plant's current production capacity, reflecting the relative relationship between production demand and the plant's capacity. If the ratio is greater than 1, demand exceeds capacity; if it is less than 1, there is excess capacity. The capacity-demand matching coefficient comprehensively reflects the degree of match between the plant's capacity and demand, as well as the plant's ability to meet demand. A higher matching coefficient indicates that the plant is able to meet demand while maintaining a more stable production process and ensuring better quality. For example, suppose a military clothing production plant has a production demand of 1,000 uniforms and a current production capacity of 800 uniforms, with a capacity coefficient of 1.2. The capacity-demand ratio of 1,000 / 800 = 1.25, meaning that demand exceeds capacity by 25%. The capacity-demand matching coefficient is 1.25×1.2=1.5. The matching coefficient is 1.5. Comparing the capacity-demand matching coefficient with the preset capacity matching coefficient shows that although demand exceeds capacity, the factory's production capacity is strong and can make up for the insufficient capacity to a certain extent, meeting demand while ensuring quality.

[0137] Next, we need to obtain the number of inspected products corresponding to each quality requirement type and the total number of products for each military clothing production plant. First, we need to identify the quality requirement type for military clothing. This typically includes aspects such as product size, material, performance, and appearance. For example, for military uniforms, quality requirements might include dimensional accuracy, fabric wear resistance, and color stability. For each quality requirement type, we need to determine the number of products to be inspected. This often depends on factors such as production scale, quality requirements, and testing costs. For example, if the production scale is large and quality requirements are stringent, we may need to increase the number of inspected products to ensure quality. Next, to count the total number of products for each military clothing production plant, we can review the plant's production records to obtain the number of products in each production batch. After obtaining the number of inspected products corresponding to the quality requirement type and the total number of products for each military clothing production plant, we can integrate and analyze the data. For example, we can calculate the proportion of inspected products to the total number of products for each plant to assess the plant's inspection coverage and quality control level.

[0138] The value of the number of tested products divided by the total number of products is the quality inspection coverage rate. The quality inspection coverage rate refers to the ratio of the number of products actually tested to the total number of products that should be tested. The calculation formula is:

[0139] Quality inspection coverage = (number of inspected products / total number of products) × 100%

[0140] The higher the quality inspection coverage rate, the more comprehensive the quality inspection work is, and it can cover more products, making it more likely to discover potential quality problems.

[0141] After obtaining the quality inspection coverage rate, we can obtain the industry benchmark standard deviation corresponding to the efficiency requirement type and the historical yield standard deviation for each military clothing production plant. This can be obtained by consulting relevant industry reports. The industry benchmark standard deviation is a statistic used to measure the degree of dispersion of a set of data within an industry. It indicates the fluctuation of data values ​​relative to the industry average. The historical yield standard deviation can be collected from the factory's production records, quality management system, or database. Historical yield data refers to the ratio of qualified products to all products produced during the military clothing production process over a period of time.

[0142] After obtaining the industry benchmark standard deviation and the historical yield rate standard deviation, the yield rate stability is calculated based on the historical yield rate standard deviation and the industry benchmark standard deviation. Yield rate stability refers to the ability of the yield rate to remain relatively consistent over a certain period of time during the production process, reflecting the reliability of the production system and the level of quality control. The calculation formula is:

[0143] Yield stability = 1-(historical yield standard deviation / industry benchmark standard deviation);

[0144] After determining the yield rate stability, multiply the yield rate stability and the quality inspection coverage rate to obtain the quality matching coefficient. The quality matching coefficient is a comprehensive indicator obtained by multiplying the yield rate stability and the quality inspection coverage rate. It is used to measure the level of quality assurance during the production process. The yield rate stability reflects the degree of fluctuation in the yield rate over a certain period of time during the production process. Higher stability indicates a more reliable production process and more consistent product quality. The quality inspection coverage rate indicates the proportion of products that actually undergo quality inspection during the production process. The higher the coverage rate, the more comprehensive the quality inspection. The quality matching coefficient is obtained by multiplying the yield rate stability and the quality inspection coverage rate. A higher quality matching coefficient indicates that the production process is both stable and fully controlled, and product quality is more assured. A lower coefficient may indicate insufficient stability (such as equipment aging or process fluctuations) or insufficient coverage (such as an incomplete quality inspection process).

[0145] Obtain the dispatch demand tact time corresponding to each efficiency requirement type and the actual production tact time of each military clothing production plant. Efficiency requirement types may include different production goals, such as meeting a specific order volume, improving production efficiency, and reducing production costs. Depending on the different efficiency requirement types, corresponding production plans and tact times need to be developed. The dispatch demand tact time is usually calculated based on customer order demand and available production time. The calculation formula is:

[0146] Demand allocation tact time = available production time ÷ customer order demand.

[0147] For example, if a military clothing production plant has 8 hours of effective production time per day (480 minutes) and a customer order requires 240 products, the dispatching tact time is to complete the production of one product every 2 minutes. The actual production tact time of each military clothing production plant can be collected through channels such as production management systems and personnel attendance systems.

[0148] The efficiency matching coefficient is then multiplied by the ratio of the dispatched demand cycle to the actual production cycle and the corresponding capacity coefficient of the military clothing production plant. The efficiency matching coefficient is a comprehensive indicator used to assess the degree of match between production efficiency and demand at military clothing production plants. It is calculated by multiplying the ratio of the dispatched demand cycle to the actual production cycle and the corresponding capacity coefficient of the military clothing production plant. The efficiency matching coefficient is used to assess the degree of match between production efficiency and demand. An efficiency matching coefficient close to 1 indicates that actual production efficiency is basically matched with demand; a coefficient greater than 1 indicates that actual production efficiency is lower than demand, indicating the possibility of production bottlenecks or resource waste; a coefficient less than 1 indicates that actual production efficiency is higher than demand, indicating the potential for further output increases.

[0149] By determining the type matching coefficient corresponding to each type of deployment instruction task, the production plant capacity can be accurately evaluated, which helps to accurately evaluate the production capacity of each plant, provide a scientific basis for subsequent deployment decisions, and help achieve the optimal match between production capacity, quality and efficiency, thereby improving overall production efficiency.

[0150] In one implementation of this embodiment, the allocation priority of each military clothing production factory is determined based on the factory production capacity index, dynamic resource matching index, and geographical location of each military clothing production factory, including:

[0151] S610, obtaining current production capacity data of each military clothing production factory, the current production capacity data including the factory's average production rate and current production capacity;

[0152] S620. Obtaining the maximum production capacity corresponding to the factory production capacity indicator of each military clothing production factory from a preset database;

[0153] S630: Subtract the current production capacity from the maximum production capacity to obtain the available production capacity, and divide the available production capacity by the average production rate of the factory to obtain the shortest response time of each military clothing production factory;

[0154] S640. Sort the dynamic resource matching index of each military clothing production factory from largest to smallest to form a first ranking sequence;

[0155] S650, determining the transportation distance based on the geographical location of each military clothing production factory in the first arrangement sequence;

[0156] S660: Calculate the transportation time based on the transportation distance and the preset average transportation speed;

[0157] S670. Determine the upper limit of the processing time for each military clothing production factory to process the production resource allocation instruction task type based on the shortest response time and transportation time;

[0158] S680: Sort the processing time limits from small to large, wherein the allocation priority of the one with the smallest processing time limit is the highest.

[0159] Obtain the current production capacity data for each military clothing production factory. Current production capacity data includes the factory's average production rate and current production capacity. This capacity data can be directly obtained by accessing the production management system. The factory's average production rate refers to the average number of military clothing the factory can produce within a certain period of time (such as a day, week, or month). Current production capacity refers to the number of military clothing the factory has produced at a certain moment or within a certain period of time.

[0160] Subsequently, the maximum production capacity corresponding to the factory production capacity index of each military clothing production factory is obtained from a preset database. Maximum production capacity refers to the maximum number of military clothing that can be produced within a certain period of time (such as a year, a month, etc.) under normal production conditions, using all the factory's equipment, factory buildings, and other fixed assets. First, it is necessary to access the preset database that stores data related to military clothing production factories and locate indicator data related to factory production capacity in the database. These indicators may include the number of equipment, equipment productivity, working hours, etc. The maximum production capacity data of each military clothing production factory is searched in the production capacity index to obtain the maximum production capacity corresponding to the factory production capacity index.

[0161] Subtract the current production capacity from the maximum production capacity to obtain the available production capacity, and divide the available production capacity by the factory's average production rate to obtain the shortest response time for each military clothing production factory. The maximum production capacity refers to the number of military clothing that the factory can produce at its maximum production capacity; the current production capacity refers to the number of military clothing that the factory has currently produced; and the factory's average production rate refers to the average number of military clothing that the factory can produce within a certain period of time. The available production capacity refers to the number of military clothing that the factory can still produce before reaching its maximum production capacity. The calculation formula is:

[0162] Available production capacity = maximum production capacity - current production capacity

[0163] Minimum response time refers to the shortest amount of time a military clothing production plant can take to respond to and meet additional production demands, based on its existing production capacity. This is calculated by dividing available production capacity by the plant's average production rate. The result is the shortest amount of time a plant can take to respond to additional production demands without impacting current production. This time reflects the plant's flexibility and responsiveness in resource allocation.

[0164] For example, if a factory's maximum production capacity is 1,000 pieces per day and its current production capacity is 800 pieces per day, then its available production capacity is 200 pieces per day. If the factory's average production rate is 50 pieces per hour, then the minimum response time is 200 pieces ÷ (50 pieces per hour × the number of hours in a 24-hour day, but we'll use daily calculations here for simplicity) = 4 hours, meaning the factory can start and complete some additional production tasks within 4 hours.

[0165] Next, the dynamic resource matching index of each military clothing production factory is sorted from largest to smallest, forming the first ranking sequence. In other words, the collected dynamic resource matching indexes are sorted from largest to smallest. After the sorting is complete, the results are saved as a sequence, the first ranking sequence. This sequence shows the ranking of each military clothing production factory from highest to lowest according to their dynamic resource matching index.

[0166] The transportation distance is determined based on the geographic location of each military clothing production factory in the first sequence. Specifically, the precise geographic location of each military clothing production factory in the first sequence is obtained, typically expressed as longitude and latitude coordinates. Based on the longitude and latitude coordinates of the military clothing production factories, the straight-line transportation distance between any two military clothing production factories can be calculated by subtracting the horizontal and vertical coordinates of the longitude and latitude coordinates.

[0167] The transportation time is calculated by the transportation distance and the preset average transportation speed. The preset average transportation speed can be determined according to the actual situation. The calculation formula is:

[0168] Transportation time = transportation distance / preset average transportation speed

[0169] Next, based on the minimum response time and transportation time, the upper limit of the processing time for each military clothing production factory to handle the production resource allocation instruction task type is determined. The upper limit of processing time refers to the maximum time required for the military clothing production factory to complete production and prepare for transportation when processing the production resource allocation instruction. The upper limit of processing time should include the minimum response time and transportation time to ensure that the factory has sufficient time to complete production and prepare for transportation. The calculation formula is:

[0170] Maximum processing time = minimum response time + shipping time

[0171] The processing time limits are sorted from smallest to largest, with the lowest processing time limit receiving the highest allocation priority. The processing time limit refers to the maximum time it takes for a military clothing production factory to process a production resource allocation order, from receiving the order to completing production and preparing for shipment. In other words, the collected processing time limits are sorted from smallest to largest. Once sorted, the factory with the lowest processing time limit receives the highest allocation priority, and so on, determining the allocation priority.

[0172] By obtaining each factory's current and maximum production capacity data, we can accurately understand each factory's production capacity and remaining capacity, allowing for more effective allocation of production resources. By calculating the minimum response time, we can determine how long it would take each factory to reach maximum capacity, starting from its current state and operating at its average production rate. This facilitates the rapid selection of the fastest-responding factory in emergency situations. By prioritizing factories with shorter processing times, we ensure more efficient utilization of production resources, reducing waiting and idle time, and ultimately improving overall production efficiency to meet the military's demand for rapid response and high-volume clothing production.

[0173] In one implementation of this embodiment, the method further includes:

[0174] S710. When the shortest response time of each military clothing production factory is the same, execute the production resource allocation rule;

[0175] Among them, production resource allocation rules include:

[0176] S1. Obtain the processing time of the deployment instruction task of n production resources;

[0177] S2. Randomly assign m deployment instruction tasks whose processing time is less than a preset time threshold to m military clothing production factories in sequence, where m < n and m and n are both positive integers;

[0178] S3. Record the completion time of each deployment instruction task of m military clothing production factories, and mark the military clothing production factory that completes the deployment instruction task first;

[0179] S4. Assign the m+1th dispatch order task to the military clothing production factory that completed the dispatch order task first;

[0180] Steps S3 and S4 are executed in a loop until the allocation of the n production resource allocation instructions is completed.

[0181] When the shortest response time of each military clothing production factory is the same, the production resource allocation rules are executed, that is, when the shortest response time of each military clothing production factory is the same, the production resource allocation rules are executed to ensure efficient, fair and orderly production.

[0182] To execute production resource allocation rules, first obtain the processing time for the allocation instructions for n production resources. This can be obtained through the Manufacturing Execution System (MES), which automatically records the time it takes to start, run, and stop equipment. Processing time refers to the time required to complete a deployment instruction, including all steps such as task receipt, resource allocation, instruction issuance, and execution feedback.

[0183] After obtaining the processing times of the production resource allocation tasks, m allocation tasks with processing times less than a preset time threshold are randomly assigned to m military clothing production factories, where m < n, and both m and n are positive integers. First, m tasks are screened from all allocation tasks with processing times less than the preset time threshold. All n military clothing production factories are listed, and the m selected tasks are randomly sorted. The sorted tasks are then assigned to the selected m factories, with each factory being assigned one task.

[0184] Record the completion time of each dispatch order task for each of the m military clothing production factories and mark the factory that completed the dispatch order task first. Specifically, create a table or data structure (such as an array, list, or database table) to record the completion status of each dispatch order task at each factory. The table should contain the following fields: task number, factory number, completion time, and whether it was the earliest completed task. When a factory completes a dispatch order task, it should send a completion notification containing the task number and completion time. After receiving the completion notification, find the record corresponding to the task number and factory number in the task record table and update the completion time field of that record to the completion time in the notification. For each dispatch order task, iterate through its records at each factory. Compare the completion times of all records to find the earliest completion time and mark the "whether it was the earliest completed" field in the factory record corresponding to the earliest completion time as "yes."

[0185] Next, the m+1th dispatch order task is assigned to the military clothing production factory that completed the dispatch order task first. Steps S3 and S4 are repeated repeatedly until all dispatch order tasks for n production resources are assigned. In other words, starting with the m+1th task, the following steps are performed sequentially until all tasks are assigned. First, the factory that completed the task earliest is selected. The factory list is then traversed to find the factory with the earliest completion time. Once the factory with the earliest completion time is found, the task is assigned and the completion time is updated. The loop ends when all n tasks have been assigned.

[0186] By implementing production resource allocation rules, we can effectively prevent certain factories from being overloaded due to excessive tasks, thereby balancing the production load across factories, balancing loads, and improving production efficiency. Furthermore, by looping through the steps, we can dynamically adjust task allocation based on the factory's completion time, ensuring that tasks are always assigned to the factory that completes the task first. This dynamic adjustment adapts to production changes and improves production efficiency.

[0187] In one implementation of this embodiment, determining a production resource allocation plan based on the allocation transportation distance and the allocation priority includes:

[0188] S810. Obtain the historical transportation volume, average transportation cost, and historical dispatch transportation distance of each military clothing production factory;

[0189] S820. Construct a cost objective function based on historical transportation volume, average transportation cost, and historical dispatch transportation distance;

[0190] S830: Use the maximum allocation priority as a constraint condition;

[0191] S840. Combine the cost objective function and constraints to build a production resource allocation model;

[0192] S850: Input the dispatching transportation distance and dispatching priority into the production resource dispatching model to obtain a production resource dispatching plan.

[0193] The historical transportation volume, average transportation cost and historical allocation transportation distance of each military clothing production factory can be obtained through the internal management system or logistics system. The historical transportation volume refers to the total amount of goods transported by the military clothing production factory through the logistics system in the past period of time; the average transportation cost refers to the transportation cost allocated to each unit of transportation volume; the historical allocation transportation distance refers to the average distance that the military clothing production factory transported goods in the past period of time.

[0194] Next, a cost objective function is constructed based on historical transport volume, average transport cost, and historical dispatched transport distance. Specifically, variables are first defined. In this embodiment, these variables are historical transport volume, average transport cost, and historical dispatched transport distance. Specifically, Qi is set to the historical transport volume of the i-th transport task; Ci is set to the average transport cost (unit cost) of the i-th transport task; and Di is set to the historical dispatched transport distance of the i-th transport task. Subsequently, a cost objective function is constructed. The cost objective function Z is shown below:

[0195]

[0196] Among them, n is the total number of transportation tasks; Qi is the historical transportation volume of the i-th transportation task; Ci is the average transportation cost of the i-th transportation task; Di is the historical dispatch transportation distance of the i-th transportation task; Z is the total transportation cost.

[0197] The maximum allocation priority is used as a constraint. This means that when allocating resources or scheduling tasks, the highest-priority tasks are prioritized. Once these high-priority tasks are satisfied, lower-priority tasks are considered. Subsequently, a production resource allocation model is constructed based on the cost objective function and constraints.

[0198] The allocation transportation distance and allocation priority are input into the production resource allocation model to obtain the production resource allocation plan. In other words, in order to achieve efficient allocation of production resources, the transportation distance and priority need to be used as core input parameters, and the production resource allocation plan is output through the production resource allocation model.

[0199] In this embodiment, the production resource allocation plan is determined based on the allocation transportation distance and the allocation priority. The method further includes the following steps:

[0200] Obtain cost data, average transportation time, and available resources for each deployment type;

[0201] Based on the cost data corresponding to each deployment type, a first production cost sub-objective function and a second production cost sub-objective function are determined respectively;

[0202] The available resource quantity corresponding to each deployment type is used as the first sub-constraint, and the average transportation time corresponding to each deployment type is used as the second sub-constraint;

[0203] Based on the first production cost sub-objective function and the first sub-constraint condition, a production sub-planning model is constructed;

[0204] Based on the second production cost sub-objective function and the second sub-constraint condition, a production sub-allocation model is constructed;

[0205] The optimal solution for each allocation type is obtained through the production sub-plan model, and the optimal planned allocation value corresponding to each allocation type is obtained;

[0206] The optimal plan allocation value is coupled with the production resource allocation model to obtain the production plan target value;

[0207] Input the production plan target value into the production sub-allocation model to obtain the actual production scheduling value;

[0208] Compare the production plan target value with the actual production scheduling value to determine the deviation value;

[0209] If the deviation value exceeds a preset threshold, a preset penalty function is triggered, and the preset penalty function is used to adjust the first production cost sub-objective function and the second production cost sub-objective function;

[0210] If the deviation value is less than the preset threshold, the optimal production resource allocation plan is output.

[0211] First, obtain the cost data, average transportation time, and available resource quantity corresponding to each allocation type. In this embodiment, the cost data corresponding to each allocation type is the historical average production cost, historical storage cost, historical product transportation cost, and historical product shortage penalty cost corresponding to each allocation type. The relevant cost data, average transportation time, and available resource quantity can be obtained through the production management system within the factory. The historical product shortage penalty cost refers to the cost incurred by the enterprise in the historical production and operation process due to the shortage of materials, funds, manpower and other resources, which leads to the inability to meet market demand in a timely manner. The available resource quantity refers to the total amount of resources that the enterprise can use for production, operation, or to meet market demand in the current or specific time period.

[0212] Subsequently, based on the cost data corresponding to each allocation type, the first production cost sub-objective function and the second production cost sub-objective function are determined respectively. That is to say, based on the historical average production cost, historical storage cost, historical product transportation cost and historical product shortage penalty cost corresponding to each allocation type, the first production cost sub-objective function and the second production cost sub-objective function are constructed respectively. The production cost sub-objective function includes the production cost sub-function, the inventory cost sub-function, the transportation cost sub-function and the shortage cost sub-function. Specifically,

[0213] The production cost sub-function formula is:

[0214] C1=∑ t∈T ∑ s∈si EiP s,t

[0215] This formula calculates the total cost of producing all planned products Si during the entire planning period T. Where Ei is the unit cost of producing the product, and Ps,t is the quantity of product s produced at time t.

[0216] The inventory cost sub-function formula is:

[0217]

[0218] This formula calculates the total cost of delivering all planned products Si to various locations during the entire planning period T. is the unit cost of storing product s, I s,t is the quantity of product s produced at time t.

[0219] The transportation cost sub-function formula is:

[0220] C3=∑ t∈T ∑ s∈Si φ s Q s,t

[0221] This formula calculates the total cost of delivering all planned products Si to various locations during the entire planning period T. s is the unit cost of transporting product s, Q s,t is the quantity of product s shipped at time t.

[0222] The out-of-stock cost sub-function is:

[0223] C4=∑ t∈T ∑ s∈Si σ s N s,t

[0224] This formula calculates the total cost of delivering all planned products Si to various locations during the entire planning period T. s is the additional cost per unit when product s is out of stock, N s,t is the quantity of product s that is out of stock at time t.

[0225] The formula of the first production cost sub-objective function is: D1 = C1 + C2;

[0226] The formula of the second production cost sub-objective function is: D2 = C3 + C4;

[0227] Next, the number of available resources corresponding to each allocation type is taken as the first sub-constraint, and the average transportation time corresponding to each allocation type is taken as the second sub-constraint. For each allocation type, there is a corresponding limit on the number of available resources. This limit may be based on inventory levels, production capacity, capital budget or other related resources. In the model, this limit can be expressed as an inequality constraint. Suppose there are n types of allocations, the number of available resources for the i-th allocation type is Ri, and the number of resources required for this type of allocation is r (this is usually a decision variable, indicating the quantity we plan to allocate). Then, the first sub-constraint can be expressed as:

[0228] r≤Ri, i=1,2,...,n;

[0229] This constraint ensures that the planned allocation quantity for each allocation type does not exceed its available resource quantity.

[0230] In addition to resource quantity constraints, the average transportation time for each allocation type also needs to be considered. This constraint may be based on customer demand, delivery deadlines, or logistics capabilities. In the model, this constraint can be expressed as a time-related constraint. Assume that the average transportation time for the i-th allocation type is Ti, and there is an overall time limit Tmax (for example, the total transportation time for all allocation types cannot exceed a certain value). Then, the second sub-constraint can be expressed as:

[0231]

[0232] This constraint ensures that the total transport time of all allocation types does not exceed the given time limit. Note that here we assume that the transport time is proportional to the allocation quantity, which may need to be adjusted according to specific circumstances in actual applications.

[0233] Subsequently, a production sub-planning model is constructed based on the first production cost sub-objective function and the first sub-constraint. In other words, after obtaining the first production cost sub-objective function and the first sub-constraint, the production sub-planning model is constructed using these two conditions. The production sub-planning model helps companies minimize production costs while meeting resource constraints. By optimizing production plans, companies can more efficiently utilize resources, reduce production costs, and improve efficiency and customer satisfaction.

[0234] Similarly, a production sub-allocation model is constructed based on the second production cost sub-objective function and the second sub-constraint. This model is used to help companies minimize transportation and stockout costs while meeting resource constraints. By optimizing production plans, companies can more efficiently utilize resources, reduce production costs, and improve efficiency and customer satisfaction.

[0235] The production sub-planning model then optimizes each allocation type, obtaining the optimal planned allocation value for each allocation type. Specifically, within the production sub-planning model, mathematical optimization algorithms (such as linear programming, integer programming, and dynamic programming) are used to determine the production quantity for each allocation type. This solution considers various constraints and the objective function. The optimal solution results in an optimal production quantity for each allocation type. This quantity achieves the optimal value of the objective function while satisfying all constraints. The optimal planned allocation value refers to the production quantity for each allocation type in the optimal solution. This value is one of the key outputs of the production sub-planning model, representing the optimal production scale for each allocation type under the current constraints and objective function. After obtaining the optimal planned allocation value for each allocation type, the company can use it to formulate specific production plans, including procurement of raw materials, arrangement of production equipment, and allocation of human resources. This allows the company to meet market demand while minimizing production costs and improving production efficiency.

[0236] The optimal plan allocation values ​​are coupled with the production resource allocation model to derive the production plan target value. This coupling process combines the optimal plan allocation values ​​(i.e., the optimal production quantity for each allocation type) with the production resource allocation model. Through model calculations, the production plan target value is derived that meets production demand and optimizes resource utilization. This process ensures that production plans are formulated in accordance with market demand while fully considering the company's internal resource constraints and capacity. The optimal production quantity for each allocation type is calculated using a production sub-plan model or related optimization algorithms. These values ​​are based on a comprehensive consideration of multiple factors, including market demand, production costs, and resource constraints. The optimal plan allocation values ​​are used as input parameters and fed into the production resource allocation model. Through model calculations, resource utilization and production efficiency under different production scenarios are simulated. Based on the simulation results, the production plan is adjusted until a production scenario that meets all constraints and optimizes resource utilization is found. The coupled production plan should clearly define key information, such as the production quantity, production time, and required resources for each allocation type. This information together constitutes the production plan target value, providing a basis for the company to formulate specific production plans and resource allocation plans.

[0237] The production plan target values ​​are input into the production sub-scheduling model to generate actual production scheduling values. Production plan target values ​​represent an enterprise's production expectations for a specific period of time. They are determined based on a comprehensive set of factors, including market demand, resource availability, and production capacity. These targets typically include key information such as the production quantity, production time, and required resources for each product. Inputting production plan target values ​​into the production sub-scheduling model uses these expected production requirements as input parameters, providing a basis for subsequent production scheduling. Upon receiving the production plan target values, the production sub-scheduling model performs calculations and analysis based on actual factors such as the enterprise's internal resource availability, production capacity, and transportation conditions. The model attempts different production scheduling scenarios, comparing and evaluating them to find one that meets the production plan target values ​​while maximizing resource utilization and minimizing production costs. After optimization, the production sub-scheduling model outputs actual production scheduling values. These actual values, including detailed information such as the specific production time, production location, required resources, and transportation methods for each product, form the basis for the enterprise's actual production scheduling. Production scheduling actuals refer to the specific production arrangements and execution results determined during the production scheduling process, based on the production plan and actual production conditions. They reflect the actual operational status and resource allocation of the production system and serve as a crucial basis for monitoring and adjusting production activities in production management.

[0238] Next, compare the production plan target values ​​with the actual production schedule values ​​to determine the deviation. The production plan target values ​​include the planned production quantity, production time, and required resources for each product. The actual production schedule values ​​include the actual production quantity, production time, resource usage, and other values. Subtract the production plan target values ​​from the actual production schedule values ​​to determine the deviation.

[0239] If the deviation value exceeds the preset threshold, the preset penalty function is triggered, and the preset penalty function is used to adjust the first production cost sub-objective function and the second production cost sub-objective function. According to the production characteristics and management requirements of the enterprise, reasonable preset thresholds are set for various types of deviation values. The penalty function is a mathematical tool used to quantify penalties for deviations that exceed the preset threshold. Specifically, when the deviation value exceeds the preset threshold, the penalty function can be used to convert the deviation value into a specific cost increase or benefit reduction, thereby guiding production activities to move closer to the planned goals. When any type of deviation value exceeds its corresponding preset threshold, the preset penalty function is triggered. The specific form of the penalty function can be designed according to the actual situation of the enterprise, such as linear penalty, quadratic penalty, etc. When the penalty function is triggered, the penalty value is included in the first production cost sub-objective function and the second production cost sub-objective function as an additional cost item. By adding the penalty item, the first production cost sub-objective function and the second production cost sub-objective function become larger when the deviation value exceeds the preset threshold, thereby guiding production activities to adjust to reduce the deviation. In this embodiment, the penalty function can be in the following form:

[0240] P(x)=k·max(0,|xx target |-δ)

[0241] Among them, x represents the actual value; x target represents the target value; δ represents the deviation threshold; k represents the penalty coefficient, and P(x) represents the penalty function. In this embodiment, the penalty coefficient can be determined according to actual conditions.

[0242] If the deviation is less than the preset threshold, the optimal production resource allocation plan is output to determine whether the deviation is less than the preset threshold. If the deviation is less than the preset threshold, production activities are confirmed to be generally in compliance with the plan requirements. The optimal production resource allocation plan aims to maximize production efficiency, minimize costs, and optimize overall enterprise benefits by scientifically and rationally allocating and scheduling various production resources while meeting production needs.

[0243] By building a production resource allocation model based on historical data and priority constraints, efficient allocation of military clothing production resources is achieved, which can effectively reduce transportation costs and improve decision-making efficiency. It can not only enhance the stability of the supply chain, but also improve the overall management level.

[0244] An embodiment of the present application also provides a machine-readable storage medium having instructions stored thereon, the instructions being used to enable a machine to execute the above-mentioned method for intelligent allocation of military clothing production resources based on big data.

[0245] An embodiment of the present application further provides an electronic device, including:

[0246] a memory configured to store instructions; and

[0247] The processor is configured to call instructions from the memory and implement the above-mentioned big data-based intelligent allocation method for military clothing production resources when executing the instructions.

[0248] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0249] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0250] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0251] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0252] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0253] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0254] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0255] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0256] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for intelligent allocation of military clothing production resources based on big data, characterized in that: The method includes: In response to receiving a deployment instruction task for a plurality of production resources, determining a deployment type for each production resource; Obtain production resource data, production operation data, and geographic location of each military clothing production factory; Determine the allocation priority for each military clothing production plant based on production resource data, production operation data, allocation type and geographical location; Determine the deployment and transportation distance of production resources based on the geographical location of each military clothing production factory; Determine the production resource allocation plan based on the allocation transportation distance and allocation priority.

2. The method according to claim 1, characterized in that Production resource data includes the types and quantities of production resources, and production operation data includes production capacity data, production quality data, and production efficiency data. Based on production resource data, production operation data, allocation type, and geographic location, the allocation priority of each military clothing production factory is determined, including: Obtain the required quantity of production resources for each deployment type; Determine the factory production capacity index of each military clothing production factory through the production and operation data of each military clothing production factory; Determine the dynamic resource matching index based on the production resource requirements of each deployment type, the production resource types of each military clothing production factory, the production resource quantity and the factory production capacity indicators; The allocation priority of each military clothing production factory is determined based on the factory production capacity indicators, dynamic resource matching index and geographical location of each military clothing production factory.

3. The method according to claim 2, characterized in that Through the production and operation data of each military clothing production factory, the factory production capacity indicators of each military clothing production factory are constructed, including: Determine capacity dimension indicators through production capacity data; Determine quality dimension indicators through production quality data; Determine efficiency dimension indicators through production efficiency data; Obtaining demand data for the dispatch instruction task of each production resource and determining the dispatch instruction task type of each production resource, wherein the dispatch instruction task type includes a capacity demand type, a quality demand type, and an efficiency demand type; According to the type of dispatching instruction task, the preset dynamic weight optimization algorithm is used to assign weight values ​​to the capacity dimension indicators, quality dimension indicators, and efficiency dimension indicators respectively; Based on the weight values ​​of the production capacity dimension indicators, quality dimension indicators and efficiency dimension indicators, the production capacity dimension indicators, quality dimension indicators and efficiency dimension indicators are weighted and summed to determine the comprehensive score of each military clothing production factory. The comprehensive score is used to represent the factory production capacity indicators of each military clothing production factory.

4. The method according to claim 3, characterized in that Based on the production resource requirements for each deployment type, the production resource types and quantities of each military clothing production factory, and the factory production capacity indicators, a dynamic resource matching index is determined, including: Determine the allocation storage quantity corresponding to each allocation type in each military clothing production factory through the production resource type and production resource quantity of each military clothing production factory; The ratio between the production resource demand corresponding to each deployment type and the deployment storage capacity of each military clothing production factory is used as the basic matching degree; According to the deployment instruction task type, determine the type matching coefficient corresponding to each deployment instruction task type; The product of the basic matching degree and the type matching coefficient corresponding to each deployment instruction task type is used as the dynamic resource matching degree index.

5. The method according to claim 3, characterized in that According to the deployment instruction task type, determine the type matching coefficient corresponding to each deployment instruction task type, including: By using the production capacity indicators and deployment instruction task types of military clothing production factories and a preset multi-dimensional evaluation quantitative model, the capacity coefficient of each military clothing production factory is determined; Divide the production resource demand corresponding to the deployment type by the deployment storage capacity of each military clothing production factory to obtain the capacity demand ratio; The capacity-demand ratio is multiplied by the capacity coefficient corresponding to the military clothing production factory to obtain the capacity-demand matching coefficient; Obtain the number of inspected products corresponding to the quality requirement type and the total number of products in each military clothing production factory; The value of dividing the number of tested products by the total number of products is taken as the quality inspection coverage rate; Obtain the industry benchmark standard deviation corresponding to the efficiency demand type and the historical yield standard deviation of each military clothing production factory; Calculate yield stability based on historical yield standard deviation and industry benchmark standard deviation; Multiply the yield rate stability and quality inspection coverage to obtain the quality matching coefficient; Obtain the deployment demand rhythm corresponding to the efficiency demand type and the actual production rhythm of each military clothing production factory; The efficiency matching coefficient is obtained by multiplying the ratio between the deployment demand rhythm and the actual production rhythm with the capacity coefficient corresponding to the military clothing production factory.

6. The method according to claim 2, characterized in that Determine the deployment priority of each military clothing production factory based on its factory production capacity indicators, dynamic resource matching index, and geographical location, including: Obtain the current production capacity data of each military clothing production factory, including the factory's average production rate and current production capacity; Obtain the maximum production capacity corresponding to the factory production capacity indicator of each military clothing production factory from a preset database; Subtract the current production capacity from the maximum production capacity to obtain the available production capacity, and divide the available production capacity by the average production rate of the factory to obtain the shortest response time of each military clothing production factory; Sort the dynamic resource matching index of each military clothing production factory from large to small to form the first ranking sequence; Determine the transportation distance based on the geographical location of each military clothing production factory in the first arrangement sequence; Calculate the transportation time based on the transportation distance and the preset average transportation speed; Determine the upper limit of the processing time for each military clothing production factory to handle the production resource allocation instruction task type based on the shortest response time and transportation time; Sort the processing time limits from small to large, where the allocation with the smallest processing time limit has the highest priority.

7. The method according to claim 6, characterized in that The method further includes: When the shortest response time of each military clothing production factory is the same, the production resource allocation rule is executed; Among them, production resource allocation rules include: S1. Obtain the processing time of the deployment instruction task of n production resources; S2. Randomly assign m deployment instruction tasks whose processing time is less than a preset time threshold to m military clothing production factories in sequence, where m < n and m and n are both positive integers; S3. Record the completion time of each deployment instruction task of m military clothing production factories, and mark the military clothing production factory that completes the deployment instruction task first; S4. Assign the m+1th dispatch order task to the military clothing production factory that completed the dispatch order task first; Steps S3 and S4 are executed in a loop until the allocation of the n production resource allocation instructions is completed.

8. The method according to claim 1, characterized in that Determine the production resource allocation plan based on the allocation transportation distance and allocation priority, including: Obtain the historical transportation volume, average transportation cost and historical dispatch transportation distance of each military clothing production factory; Construct a cost objective function based on historical transportation volume, average transportation cost and historical dispatch transportation distance; Use the maximum deployment priority as a constraint; Combine the cost objective function and constraints to build a production resource allocation model; Input the dispatching transportation distance and dispatching priority into the production resource dispatching model to obtain the production resource dispatching plan.

9. A machine-readable storage medium, characterized in that The machine-readable storage medium stores instructions for enabling a machine to execute the method for intelligent allocation of military clothing production resources based on big data according to any one of claims 1 to 8.

10. An electronic device, characterized in that: include: a memory configured to store instructions; as well as The processor is configured to call the instructions from the memory and to implement the intelligent allocation method for military clothing production resources based on big data according to any one of claims 1 to 8 when executing the instructions.

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