Environment-friendly epoxy resin-based packaging material with flame retardant property and preparation method of environment-friendly epoxy resin-based packaging material

Abstract

The invention discloses an environment-friendly epoxy resin-based packaging material with flame retardant property and a preparation method thereof, and belongs to the technical field of capacity subpackaging, and the preparation method specifically comprises the following steps: obtaining order information of a to-be-produced order in a target area, and addresses and capacities of all subpackaging merchants in the target area; calculating the Euclidean distance between each order to be produced and all subpackage merchants, marking the address of each subpackage merchant as the initial center of mass of clustering, and allocating each order to be produced to the nearest initial center of mass to generate a category cluster; calculating a virtual distance between each order to be produced and the initial centroid according to the duration coefficient and the number coefficient; the centroid of each category cluster is updated and iterated according to the virtual distance, and the offset of the centroid is judged in each iteration until the offset of the centroid is smaller than a preset threshold value; and obtaining a virtual distance between any to-be-produced order after clustering and the final center of mass, allocating the productivity ratio of the to-be-produced order according to the virtual distance, and calculating the allocated productivity of the to-be-produced order according to the productivity ratio and the subpackaging provider productivity.

Description

Technical Field

[0001] The present invention relates to the technical field of capacity subcontracting, and in particular to an environmentally friendly epoxy resin-based packaging material with flame retardant properties and a preparation method thereof. Background Art

[0002] Capacity subcontracting is a production management strategy that assigns a company's production tasks to external suppliers or partners. This subcontracting model is typically based on specific production capabilities, technical expertise, or cost-effectiveness considerations, aiming to optimize resource allocation and improve production efficiency and flexibility. Through capacity subcontracting, companies can focus on their core competencies while leveraging external resources to meet production needs. This is particularly true when faced with order fluctuations or shifts in market demand, as capacity subcontracting allows for more efficient adjustments to production scale and pace.

[0003] As companies grow, they often expand their product offerings to diverse regions as market demand continues to expand and their business scope expands. This is especially true in remote areas where product demand is high. However, transporting products from the company's original factory to these distant locations often incurs high transportation costs. Therefore, to reduce transportation costs and improve market responsiveness, companies often consider establishing factories or production bases in the region.

[0004] However, before establishing a local factory, companies often partner with local subcontractors. This collaborative model allows them to fully leverage the resources and production capacity of local subcontractors, quickly meeting market demand while reducing investment risk and operating costs. However, when product orders are high and local subcontractors have limited production capacity, allocating production capacity becomes a complex issue that needs to be addressed.

[0005] In this situation, companies need to implement rational capacity planning and resource allocation to ensure timely order delivery and guaranteed quality. This may involve negotiating and collaborating with multiple subcontractors or finding other solutions to increase production capacity. Furthermore, companies need to consider factors such as optimizing supply chain management, improving production efficiency, and reducing operating costs. Summary of the Invention

[0006] The purpose of the present invention is to provide an environmentally friendly epoxy resin-based packaging material with flame retardant properties and a preparation method thereof, to solve the following technical problems:

[0007] How to allocate production capacity when there are many orders for a product and the local subcontractor's production capacity is limited?

[0008] The purpose of the present invention can be achieved through the following technical solutions:

[0009] An environmentally friendly epoxy resin-based packaging material with flame retardant properties and a preparation method thereof, comprising:

[0010] A data statistics module is used to obtain order information of pending production orders in the target area, marking the number of pending production orders as N. The order information includes the address, delivery time T, and product quantity O, as well as the addresses and production capacity P of all subcontractors in the target area, marking the number of subcontractors as M;

[0011] The clustering initial module is used to mark the address of the i-th production order as (X oi ,Y oi ), mark the address of the j-th subcontractor as (X pj ,Y pj ), i∈[1,N], j∈[1,M], calculate the Euclidean distance L between each pending production order and all subcontractors according to the Euclidean distance formula, mark each subcontractor address as the initial centroid of the cluster, and assign each pending production order to the initial centroid with the closest distance to generate a category cluster;

[0012] A clustering optimization module is used to normalize the delivery time and product quantity of all pending orders, obtain the normalized time coefficient α and quantity coefficient β of each pending order, and calculate the virtual distance L' between each pending order and the initial centroid based on the time coefficient and quantity coefficient, where L'=L(α+1)(2-β); and iteratively update the centroid of each category cluster based on the virtual distance, determining the centroid offset at each iteration until the centroid offset is less than a preset threshold.

[0013] The capacity calculation module is used to obtain the virtual distance D between any pending production order and the final centroid after cluster optimization in any category cluster. i , according to the virtual distance D i Allocate the production capacity ratio K of the order to be produced i , The subcontractor capacity of the category cluster where the production order is located is marked as P j , then the allocated capacity p of the production order to be produced i =P j K i .

[0014] As a further solution of the present invention: the capacity calculation module is also used to count the total amount of all orders to be produced in each category cluster, compare the total amount with the capacity of the subcontractors in the category cluster, mark the subcontractors whose capacity is greater than the total amount as redundant capacity subcontractors, and mark the subcontractors whose capacity is less than the total amount as shortage subcontractors; for the category cluster where any redundant subcontractor is located, directly mark the allocated capacity of all orders to be produced as the number of products, and then mark the redundant capacity of each redundant capacity subcontractor as a redundant capacity pool; the capacity in the redundant capacity pool is secondary allocated to the orders to be produced of nearby shortage subcontractors according to the distance priority until the redundant capacity pool is fully allocated, and the distance priority is that the shortage subcontractor who is closer to the redundant capacity subcontractor is allocated first.

[0015] As a further solution of the present invention: in the capacity calculation module, the secondary allocation is specifically:

[0016] For any subcontractor in short supply, its original production capacity is P j , obtain the received redundant capacity and mark it as P k , then the contractor's capacity after secondary allocation is P j '=P j +P k .

[0017] As a further solution of the present invention: After the secondary allocation, the capacity calculation module selects the allocated capacity p for any category cluster. i Greater than the corresponding product quantity O i The production orders to be produced are marked as overcapacity orders, and the allocated capacity of overcapacity orders is changed from p i Replace with O i and aggregate excess capacity to generate an excess capacity pool;

[0018] Screening and allocation capacity p i Less than the corresponding product quantity O i The pending production orders are marked as under-capacity orders, and the capacity is allocated to the under-capacity orders three times in proportion from the excess capacity pool. If the allocated capacity of the under-capacity orders is still lower than the product quantity after three allocations, the difference between the allocated capacity and the product quantity will be supplemented by production by the original factory.

[0019] As a further solution of the present invention: in the clustering initial module, the calculation formula of the Euclidean distance L is:

[0020]

[0021] Among them, L ij It represents the distance from the address of the i-th pending production order to the address of the j-th subcontractor.

[0022] As a further solution of the present invention: in the cluster optimization module, the calculation formulas of the duration coefficient α and the quantity coefficient β are:

[0023]

[0024] Where T i represents the delivery time of the i-th pending production order, T min Indicates the minimum delivery time of all pending production orders, T max Indicates the maximum delivery time of all pending production orders, where O i represents the number of products to be produced in the i-th order, O min Indicates the minimum quantity of all products to be produced, O max Indicates the maximum value of all products to be produced.

[0025] As a further solution of the present invention: in the data statistics module, the production capacity P represents the number of products produced per unit time.

[0026] As a further solution of the present invention: in the capacity calculation module, when the allocated capacity value p of any pending production order is i If it is not an integer, then p i Round down.

[0027] Beneficial effects of the present invention:

[0028] This invention aims to address the complex capacity allocation issues faced by enterprises when expanding their business across regions. The data statistics module captures order and subcontractor data. In the initial clustering module, the Euclidean distance formula is used to calculate the distance between each pending order and all subcontractors, and the order is assigned to the closest subcontractor. This step ensures that orders are processed promptly, reducing shipping time and costs. In the clustering optimization module, the system normalizes the order delivery time and product quantity and calculates virtual distances to update the centroid of the iterative clusters. In this way, the system continuously optimizes the match between orders and subcontractors, improving overall production efficiency and ensuring that each order receives a certain amount of capacity, with an emphasis on maintaining customer orders with large product quantities. Furthermore, the system uses secondary and tertiary allocation mechanisms to further optimize resource allocation and reduce waste when dealing with redundant capacity between and within subcontractors. This intelligent capacity subcontracting system provides enterprises with a flexible and efficient solution, helping them maintain their leading position in a highly competitive market. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The present invention will be further described below with reference to the accompanying drawings.

[0030] Figure 1 It is a module schematic diagram of the present invention;

[0031] Figure 2 It is a schematic diagram of the process of clustering production orders in the present invention. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0033] See also Figure 1-Figure 2 As shown, the present invention is an environmentally friendly epoxy resin-based packaging material with flame retardant properties and a preparation method thereof, comprising:

[0034] The data statistics module is a core component of the intelligent capacity subcontracting system. Its primary function is to comprehensively collect and organize detailed information on all relevant orders and subcontractors within the target region. This module utilizes efficient data capture and processing technology to ensure that companies have the latest and most accurate information on market trends and production resource status.

[0035] In the data statistics module, detailed records of pending production orders are first kept. Each order contains several key pieces of information, including the shipping address, estimated delivery time (T), and product quantity (O). This information is crucial for subsequent order allocation and production planning. For example, address information helps the system calculate distances to various subcontractors, thereby optimizing logistics costs; delivery time is related to the urgency and priority of orders; and product quantity directly affects the calculation of production capacity requirements.

[0036] The data statistics module also collects detailed information about all potential subcontractors within the target area. This includes, but is not limited to, the subcontractor's specific location and production capacity (P). Production capacity is a key metric, representing the number of products a subcontractor can produce per unit time. Understanding each subcontractor's production capacity helps the system more rationally plan resources when allocating orders, avoiding overcapacity or undercapacity. Furthermore, the number of subcontractors is recorded, labeled M, to facilitate unified management and scheduling during subsequent processing.

[0037] The initial clustering module is used to assign orders to manufacturers by clustering. We need to cluster based on the distance between the order address and the manufacturer address. The specific steps are as follows:

[0038] Calculate distance: First, you need to calculate the geographical distance between each order and each manufacturer.

[0039] Clustering method selection: Select an appropriate clustering algorithm, such as K-means or hierarchical clustering.

[0040] Allocate orders: Based on the clustering results, assign the orders to the nearest manufacturers.

[0041] The specific steps are:

[0042] Mark the address of the i-th production order as (X oi ,Y oi ), mark the address of the j-th subcontractor as (X pj ,Y pj ), i∈[1,N], j∈[1,M], the Euclidean distance L between each pending production order and all subcontractors is calculated in sequence according to the Euclidean distance formula. The calculation formula of the Euclidean distance L is:

[0043]

[0044] Among them, L ij It represents the distance from the address of the i-th pending production order to the address of the j-th subcontractor.

[0045] The present invention selects K-means algorithm for clustering. K-means is an iterative algorithm for assigning data points to K clusters so that the distance between data points in the cluster is minimized.

[0046] Mark each subcontractor address as the initial centroid of the cluster, and assign each pending production order to the nearest initial centroid to generate a category cluster;

[0047] The clustering optimization module is used to normalize the delivery time and product quantity of all pending production orders, and obtain the normalized time coefficient α and quantity coefficient β of each pending production order. The calculation formula of the time coefficient α and the quantity coefficient β is:

[0048]

[0049] Where T i represents the delivery time of the i-th pending production order, T min Indicates the minimum delivery time of all pending production orders, T max Indicates the maximum delivery time of all pending production orders, where O i represents the number of products to be produced in the i-th order, O min Indicates the minimum quantity of all products to be produced, O max Indicates the maximum value of all products to be produced.

[0050] And the virtual distance L' between each order to be produced and the initial centroid is calculated according to the duration coefficient and the quantity coefficient, L'=L(α+1)(2-β); in the present invention, when the delivery time of the order is shorter, the production priority of the order is higher, and the corresponding distance to the initial centroid is closer, the initial Euclidean distance is adjusted by the normalized duration coefficient, and α+1 is used to prevent the virtual distance from being 0 due to the coefficient value; in the present invention, when the number of products in the order is greater, the production priority of the order is higher, and the corresponding distance from the initial centroid is closer, the Euclidean distance is adjusted by the normalized quantity coefficient, because the higher the order quantity, the smaller the corresponding quantity coefficient β, so 2-β is used to convert the size relationship, and to prevent the virtual distance from being 0 due to the coefficient value;

[0051] Iterate the centroid of each category cluster according to the virtual distance, and determine the offset of the centroid in each iteration until the centroid offset is less than a preset threshold;

[0052] The formula for calculating the center of mass is:

[0053]

[0054] Among them, A i represents the i-th order, μ k represents the center of mass, where C k is the set of orders assigned to the kth centroid, |C k | is the size of the set. The K-means algorithm iteratively optimizes the clustering and the location of cluster centroids. In each iteration, data points are first assigned to the nearest cluster based on the current cluster centroid. Then, the mean of all data points within each cluster is recalculated as the new cluster centroid. This process repeats until a stopping condition is met.

[0055] The capacity calculation module is a crucial part of the intelligent capacity subcontracting system. After clustering optimization is completed, it is responsible for reasonably allocating the capacity ratio based on the virtual distance between each to-be-produced order and the centroid of the final category cluster, ensuring maximum production efficiency and resource utilization.

[0056] In the capacity calculation module, we first need to obtain the virtual distance D between any pending production order and the centroid of the final category cluster after cluster optimization. i This virtual distance is calculated by comprehensively considering the order's delivery time, product quantity, and other relevant factors, and it reflects the degree of match between the order and the subcontractor.

[0057] Based on the virtual distance D i , the system will further calculate the production capacity ratio K of the order to be produced i , Capacity ratio is a key metric that determines how much capacity an order should be allocated to. Specifically, if an order is closer to a subcontractor (i.e., the virtual distance is smaller), then the order is more likely to be assigned to that subcontractor, resulting in a higher capacity ratio. Conversely, if the distance is greater, the order is likely to receive a lower capacity ratio.

[0058] Next, the subcontractor capacity of the category cluster where the production order is located is marked as P j Here P j Indicates the number of products that the subcontractor can produce per unit time. i and subcontractor capacity P j Multiplying them together, we can get the specific allocated capacity p of the order to be produced. i , that is, p i =P j K i .

[0059] However, in the actual production process, due to various reasons (such as equipment limitations, human resources, etc.), the allocated capacity value p i In order to solve this problem, the system will i This means that if the calculated p i If the value contains a decimal part, the decimal part will be discarded and only the integer part will be retained as the final allocated capacity. This is done to ensure the feasibility of capacity allocation and the simplicity of actual operation.

[0060] The capacity calculation module in the intelligent capacity subcontracting system not only allocates capacity based on virtual distance but also carries out the crucial task of counting and optimizing resources. Through meticulous data analysis, this module ensures the appropriate capacity allocation for pending orders within each cluster and effectively manages both redundant and insufficient capacity, thereby improving overall production efficiency and resource utilization.

[0061] First, the capacity calculation module calculates the total number of pending orders for each category cluster. This step is crucial because it provides the baseline data for subsequent capacity comparisons. By summarizing the product quantities of all orders, the system can clearly understand the actual demand scale for each category cluster.

[0062] Next, this total is compared with the subcontractor capacity within its category cluster. If a subcontractor's capacity exceeds the total number of orders for its category cluster, it is marked as having excess capacity. Conversely, if a subcontractor's capacity is less than the total number of orders, it is marked as having insufficient capacity. This classification helps the system quickly identify which subcontractors have sufficient production capacity to handle additional orders and which subcontractors may require external support to meet current production needs.

[0063] For any redundant subcontractor's category cluster, the system directly marks the allocated capacity for all pending production orders as product quantities. This means these orders will receive full capacity support without further adjustments. Each redundant subcontractor then marks its unused capacity (i.e., redundant capacity) as a redundant capacity pool. The capacity in this pool can be treated as a reserve resource to respond to emergencies or optimize overall capacity allocation.

[0064] To more efficiently utilize this excess capacity, the system reassigns it to nearby subcontractors with insufficient capacity based on proximity. Specifically, the system prioritizes subcontractors with insufficient capacity closest to the subcontractor with excess capacity. This location-based allocation strategy helps reduce logistics costs, speed up delivery, and improve customer satisfaction.

[0065] The entire re-allocation process continues until all redundant capacity has been allocated. Throughout this process, the system continuously updates the capacity status of each subcontractor, ensuring that each allocation is based on the most up-to-date data. Furthermore, if unmet order demand remains after re-allocation, the system can consider further optimization of supply chain management, such as adding new partners or adjusting existing production plans.

[0066] The secondary distribution is specifically:

[0067] For any subcontractor in short supply, its original production capacity is P j , obtain the received redundant capacity and mark it as P k , then the contractor's capacity after secondary allocation is P j '=P j +P k .

[0068] After the secondary allocation, the capacity calculation module filters the production orders in any category cluster to find out those with allocated capacity p i Greater than the corresponding product quantity O i For such orders, the system will allocate the over-capacity p i Adjust to the actual product quantity required Oi To ensure the rational use of resources. At the same time, the excess production capacity (i.e. the original allocated capacity p i With the adjusted product quantity O i The difference between the two) is aggregated to form a surplus capacity pool. The capacity in this pool can be regarded as a flexible resource reserve for subsequent possible capacity demand or optimization.

[0069] Then, the system again selects the allocated capacity p i Less than the corresponding product quantity O i The system then allocates capacity three times proportionally from the previously generated excess capacity pool. This proportional allocation method aims to fairly redistribute excess capacity to meet the needs of all undercapacity orders.

[0070] However, if, after three allocations, the allocated capacity for some under-capacity orders still falls short of their required quantity, this means the current excess capacity cannot fully fill the gap. In this case, the system takes further action: the unmet portion (i.e., the difference between the allocated capacity and the quantity) is directed to the original manufacturer for production. This arrangement ensures that orders can be completed on time even in exceptional circumstances, avoiding production delays or customer dissatisfaction caused by insufficient capacity.

[0071] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0072] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0073] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An environmentally friendly epoxy resin-based packaging material with flame retardant properties and a preparation method thereof, characterized in that: include: A data statistics module is used to obtain order information of pending production orders in the target area, marking the number of pending production orders as N. The order information includes the address, delivery time T, and product quantity O, as well as the addresses and production capacity P of all subcontractors in the target area, marking the number of subcontractors as M; The clustering initial module is used to mark the address of the i-th production order as (X oi ,Y oi ), mark the address of the j-th subcontractor as (X pj ,Y pj ), i∈[1,N], j∈[1,M], calculate the Euclidean distance L between each pending production order and all subcontractors according to the Euclidean distance formula, mark each subcontractor address as the initial centroid of the cluster, and assign each pending production order to the initial centroid with the closest distance to generate a category cluster; A clustering optimization module is used to normalize the delivery time and product quantity of all pending orders, obtain the normalized time coefficient α and quantity coefficient β of each pending order, and calculate the virtual distance L' between each pending order and the initial centroid based on the time coefficient and quantity coefficient, where L'=L(α+1)(2-β); and iteratively update the centroid of each category cluster based on the virtual distance, determining the centroid offset at each iteration until the centroid offset is less than a preset threshold. The capacity calculation module is used to obtain the virtual distance D between any pending production order and the final centroid after cluster optimization in any category cluster. i , according to the virtual distance D i Allocate the production capacity ratio of the pending production order The subcontractor capacity of the category cluster where the production order is located is marked as P j , then the allocated capacity p of the production order to be produced i =P j K i .

2. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 1, characterized in that: The capacity calculation module is further configured to count the total amount of all pending production orders in each category cluster, compare the total amount with the capacity of the subcontractors in the category cluster, mark subcontractors whose capacity is greater than the total amount as redundant capacity subcontractors, and mark subcontractors whose capacity is less than the total amount as short-capacity subcontractors; for the category cluster where any redundant subcontractor is located, directly mark the allocated capacity of all pending production orders as the number of products, and then mark the redundant capacity of each redundant capacity subcontractor as a redundant capacity pool; The capacity in the redundant capacity pool is secondary allocated to the pending production orders of nearby shortage subcontractors according to the distance priority until the redundant capacity pool is fully allocated. The distance priority is that the shortage subcontractor closer to the redundant capacity subcontractor is allocated first.

3. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 2, characterized in that: In the capacity calculation module, the secondary allocation is specifically as follows: For any subcontractor in short supply, its original production capacity is P j , obtain the received redundant capacity and mark it as P k , then the contractor's capacity after secondary allocation is P j '=P j +P k .

4. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 3, characterized in that: In the capacity calculation module, after the secondary allocation, for any category cluster, the allocated capacity p is screened out. i Greater than the corresponding product quantity O i The production orders to be produced are marked as overcapacity orders, and the allocated capacity of overcapacity orders is changed from p i Replace with O i and aggregate excess capacity to generate an excess capacity pool; Screening and allocation capacity p i Less than the corresponding product quantity O i The pending production orders are marked as under-capacity orders, and the capacity is allocated to the under-capacity orders three times in proportion from the excess capacity pool. If the allocated capacity of the under-capacity orders is still lower than the product quantity after three allocations, the difference between the allocated capacity and the product quantity will be supplemented by production by the original factory.

5. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 1, characterized in that: In the clustering initial module, the calculation formula of the Euclidean distance L is: Among them, L ij It represents the distance from the address of the i-th pending production order to the address of the j-th subcontractor.

6. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 1, characterized in that: In the cluster optimization module, the calculation formulas for the duration coefficient α and the quantity coefficient β are as follows: Where T i represents the delivery time of the i-th pending production order, T min Indicates the minimum delivery time of all pending production orders, T max Indicates the maximum delivery time of all pending production orders, where O i represents the number of products to be produced in the i-th order, O min Indicates the minimum quantity of all products to be produced, O max Indicates the maximum value of all products to be produced.

7. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 1, characterized in that: In the data statistics module, the production capacity P represents the number of products produced per unit time.

8. The flame-retardant, environment-friendly epoxy resin-based packaging material and preparation method thereof according to claim 1, characterized in that: In the capacity calculation module, when the allocated capacity value p of any pending production order is i If it is not an integer, then p i Round down.

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