E-commerce logistics order intelligent management method

Abstract

The invention relates to the technical field of order processing, and discloses an e-commerce logistics order intelligent management method, which aims at improving the order processing efficiency, and comprises the following steps: setting main order information, sub-order information, delivery order information and logistics order information, splitting the main order information into N pieces of sub-order information, each piece of sub-order information corresponds to one piece of shop information, and three order distribution strategies are set and are used for converting the main order information into the sub-order information, converting the sub-order information into the delivery order information, converting the delivery order information into the logistics order information, dividing the commodities into short-term commodities or long-term commodities, and according to the commodity types, dividing the commodities into the short-term commodities and the long-term commodities. And converting the delivery order information into one or more pieces of logistics order information, and executing a commodity arrival time matching strategy according to each piece of logistics order information so as to meet the demand of the user for taking the commodity in the same day.

Description

Technical Field

[0001] This invention relates to the field of order processing technology, specifically to an intelligent management method for e-commerce logistics orders. Background Technology

[0002] With the rapid development of e-commerce, the role of logistics management in the supply chain is becoming increasingly prominent. Especially in order processing, efficiently and accurately handling orders and ensuring timely and safe delivery of goods to consumers is one of the main challenges facing e-commerce logistics. However, existing logistics management systems have some shortcomings in order processing, inventory management, and delivery management. Particularly when generating logistics orders, optimizing outbound strategies and selecting unified arrival times for goods under the same order to facilitate simultaneous pickup by users have become urgent issues to be addressed in the e-commerce logistics field.

[0003] Traditional logistics management methods often rely on manual operation, which is inefficient and prone to errors. For example, in the order processing process, multiple steps are required, such as manually entering order information, confirming inventory, generating delivery notes, and notifying the warehouse to ship. These steps are not only time-consuming but also prone to human error. In addition, due to the lack of intelligent order management and order allocation strategies, order processing cannot be flexibly adjusted according to the actual logistics and inventory situation, which affects the efficiency and accuracy of order processing.

[0004] In terms of logistics order generation, existing methods often fail to achieve optimal outbound strategies. For example, some methods may not be able to intelligently select outbound goods based on factors such as storage duration and type, leading to insufficient utilization of warehouse space or excessive backlog of goods, affecting the freshness and quality of products. At the same time, due to the lack of effective delivery time matching strategies, different products may have different logistics routes and delivery times, causing users to be unable to pick up all their products at the same time, thus reducing the user's shopping experience.

[0005] Therefore, developing an intelligent management method for e-commerce logistics orders is of great significance for improving the automation level of order processing, optimizing logistics and delivery processes, and enhancing user experience. This new method needs to be able to achieve efficient integration of order information, intelligent order allocation, accurate warehouse address filtering and target product selection, and adjust delivery time according to user needs to meet the high-efficiency operation requirements of modern e-commerce logistics. Through this method, the level of intelligence in logistics management can be effectively improved, human error can be reduced, order processing efficiency can be increased, goods outbound strategies can be optimized, and a unified delivery time for goods under the same order can be achieved, thereby improving the overall logistics service quality and user satisfaction. Summary of the Invention

[0006] This invention provides an intelligent management method for e-commerce logistics orders, which helps to solve the problems mentioned in the background art.

[0007] This invention provides the following technical solution: an intelligent management method for e-commerce logistics orders, optionally including obtaining order information; The order information includes main order information, sub-order information, shipping order information, and logistics order information; The main order information includes the number of stores corresponding to the products purchased by the user and the store information corresponding to each store; The store information includes a store identifier ID and a warehouse identifier ID, wherein one store identifier ID corresponds to one or more warehouse identifier IDs, and one warehouse identifier ID includes one or more store identifier IDs; All items purchased by a user at the same time generate a master order, which is then split into N sub-orders, where N is a positive integer. The same moment refers to the order placement time for all goods purchased by a user in a single transaction record; For example, if a user submits a transaction order at 19:00, this transaction order is set as the main order. The main order contains one or more products, and the order time for these products is the same, which is 19:00. Set the initial order splitting strategy; Convert the master order information into a sub-order information or split it into multiple sub-order information; The sub-order information includes the store information corresponding to the product purchased by the user, and the product information corresponding to the product purchased by the user in that store; One sub-order information corresponds to one store information, and one store information corresponds to one or more product information; The product information includes product type, product category, number of products, product volume, and product weight. Set a second order splitting strategy; Convert sub-order information into a single shipping order or split it into multiple shipping order information; The contents of the shipping order information are the store identifier ID and product information; Set product types, dividing them into short-term and long-term products; Set a storage duration threshold to determine the type of product; Obtain the storage duration of the goods; If the storage time of a product exceeds the storage time threshold, the product will be marked as a long-term product. If the storage time of a product is less than or equal to the storage time threshold, the product will be marked as a short-term product. Based on the product type, set a third order splitting strategy; Convert the shipping order information into a single logistics order information or split it into multiple logistics order information; Based on the information of each logistics order, a product arrival time matching strategy is implemented.

[0008] Optionally, the step of converting the master order information into a sub-order information or splitting it into multiple sub-order information includes: Retrieve master order information; If the number of stores corresponding to the products purchased by the user in the main order information is equal to 1, then the main order information will be converted into a sub-order information, and the content of the sub-order information will be generated in the sub-order information. If the number of stores corresponding to the products purchased by the user in the main order information is greater than 1, then the number of stores in the main order information is obtained and marked as the first number. The main order information is then split into the first number of sub-order information, and each sub-order information corresponds to one store information. In each sub-order information, the corresponding sub-order information content is generated.

[0009] Optionally, the step of converting sub-order information into a single shipping order or splitting it into multiple shipping order information includes: Get the total number of items in the sub-order information, denoted as N; get the total volume of items in the sub-order information, denoted as V; get the total weight of items in the sub-order information, denoted as M. Set a threshold for the number of items, denoted as B; set a threshold for the volume of items, denoted as C; set a threshold for the weight of items, denoted as X. The product quantity threshold is used to determine the maximum number of products allowed within a single shipping order. The product volume threshold is used to determine the maximum allowed product volume within a single shipping order. The commodity weight threshold is used to determine the maximum allowed commodity weight within a single shipping order. In the sub-order information, if the total number of items N The threshold for the number of items is B, and the total volume of items is V. Product volume threshold C and total product weight M If the product weight threshold X is set, the sub-order information will be converted into a shipping order information; In the sub-order information, if the total number of items N If so, this condition is designated as Condition 1. The sub-order is then split into multiple shipping orders based on the total number of goods. The number of shipping orders is calculated using the following formula: Where A is the number of individual shipments split from the total number of goods. Indicates to Round up; In the sub-order information, if the total volume of the goods is V If so, this condition is designated as Condition Two. Based on the total volume of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where S is the number of individual shipments split from the total volume of the goods. Indicates to Round up; In the sub-order information, if the total weight of the goods is M If X is an example, then this condition is designated as condition three. Based on the total weight of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where D is the number of individual shipments split from the total volume of the goods. Indicates to Round up; During the process of splitting sub-orders, if conditions one, two, and three are met, the number of individual shipments is calculated and denoted as F, with the formula F=max(A,S,D). The max function is used to get the maximum value of the parameters inside the function.

[0010] Optionally, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information includes: The logistics order information includes warehouse identification ID, product type, product category, user's delivery address, and estimated delivery time; The product categories refer to the different categories of products in the logistics order information. For each different product in the logistics order information, it is recorded as a product category. Each type of product corresponds to one or more warehouse identifiers (IDs); This indicates that each type of product is allowed to be distributed across multiple warehouses; Obtain the warehouse address based on the warehouse identifier ID; Obtain the warehouse address corresponding to each product in the logistics information sheet, and form a set of warehouse addresses; Filter the elements in the set of warehouse addresses and mark the warehouses corresponding to the filtered warehouse addresses as target warehouses.

[0011] Optionally, the step of filtering the elements in the warehouse address set and marking the warehouses corresponding to the filtered warehouse addresses as target warehouses includes: The specific screening steps are as follows: Obtain the user's shipping address; Based on the set of warehouse addresses, obtain the distance between each element in the set and the user's delivery address, and calculate the estimated delivery time of each element in the set relative to the user's delivery address based on the distance. Select the warehouse address with the earliest estimated arrival time and mark that address as the target address.

[0012] Furthermore, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information also includes: Each type of commodity in the logistics order information is labeled as the first commodity type, and together they form the first commodity type set; The information corresponding to each element in the first set of product categories includes the product category and the corresponding number of products, and the corresponding number of products is marked as the second number; Based on the first set of product categories, obtain the target warehouse for each element in the set, where each first product category corresponds to one target warehouse; Based on the target warehouse, obtain the product type of the first product category in that warehouse, as well as the inbound time of all products under the first product category; The storage period is the maximum permissible storage time for the goods; For example, under certain conditions, the storage period for apples is about 20 days. The warehousing time is the time recorded when the goods are received into the warehouse; Obtain the inbound time of all products under the first product category, and sort them according to the order of inbound time to form the first sequence; Retrieve all items in the first sequence whose serial numbers are from the first to the second number, and mark them as the target items; All selected target products are grouped into a set, denoted as the target product set; In the logistics order information, each first product category corresponds to a set of target products.

[0013] Optionally, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information further includes: Based on the logistics order information, obtain the product type for each first product category; If all the first commodity categories in the logistics order information are either short-term or long-term commodities, then the shipping order information will be converted into a single logistics order information. If the first product category in the logistics order information contains both short-term and long-term products, the shipping order information will be split, and the specific splitting rules are as follows: The set of target products corresponding to the first category of short-term goods is divided into one logistics order information; The set of target products corresponding to the first product category, which belongs to long-term products, is divided into a single logistics order information.

[0014] Furthermore, the step of executing a goods arrival time matching strategy based on each logistics order information includes: After obtaining the master order information and executing the first to third order splitting strategies, all logistics order information is split off; Based on the logistics order information, obtain the target warehouse and the corresponding estimated delivery time for each first product category; In the logistics order information, each first product category corresponds to an estimated arrival time; Get the first product category with the earliest estimated arrival time and mark the earliest estimated arrival time as the earliest arrival time; Get the first product category with the latest estimated arrival time, and mark the latest estimated arrival time as the latest arrival time; Using a 24-hour day as the unit, one day represents 1, and two days represent 2; If the difference between the earliest and latest delivery times is greater than or equal to 1 day, then obtain the user's delivery method. The delivery method refers to whether the user chooses to complete the pickup of goods purchased in one transaction record on the same day; If a user's delivery method is to pick up all the goods purchased in one transaction record on the same day, then the estimated arrival time of each logistics order in one transaction record will be changed to the latest arrival time. If a user's delivery method involves picking up goods purchased in a single transaction record on different days, no action will be taken on the logistics order information. If the difference between the earliest and latest delivery times is less than 1 day, no action will be taken on the logistics order information.

[0015] The present invention has the following beneficial effects: 1. This intelligent e-commerce logistics order management method automatically acquires order information and splits it into sub-orders, shipping documents, and logistics documents, improving the speed and efficiency of order processing. After a user submits an order, it identifies the store corresponding to the goods in the order and, according to a preset order splitting strategy, converts the main order into multiple sub-orders for processing, thereby accelerating order turnover. Based on the product type (short-term and long-term goods) and the user's delivery address, it intelligently selects the most suitable warehouse for delivery, helping to rationally allocate logistics resources during peak periods, reduce warehouse pressure, and ensure timely order processing and delivery. During peak periods, customer orders... The delivery time of each order is of particular concern. By implementing a product delivery time matching strategy, the estimated delivery time of the logistics order information is adjusted according to the user's delivery method to meet the user's need for same-day pickup. This flexible delivery time adjustment mechanism can improve customer satisfaction, especially during holidays or promotional activities when customers have a more urgent need for fast delivery. Based on the product's storage duration threshold, products are marked as short-term or long-term products, and storage and delivery strategies are adjusted accordingly to reduce inventory backlog and logistics costs. In addition, delivery orders can be intelligently split based on the product's volume and weight thresholds to avoid individual delivery orders exceeding weight or volume limits, thereby reducing transportation costs.

[0016] 2. This intelligent e-commerce logistics order management method, through an initial order splitting strategy, obtains the number of stores corresponding to the products purchased by the user, and transforms the main order information into single or multiple sub-order information. When the products purchased by the user involve only one store, a sub-order is generated, reducing unnecessary splitting steps. For orders involving multiple stores, sub-orders are split according to the number of stores, ensuring that each sub-order can be processed for a specific store. By splitting the main order into sub-orders corresponding to the stores and allocating logistics, especially during peak periods or promotional periods, it can ensure that orders for each store are processed and shipped in a timely manner. Detailed sub-order information is generated for each store, including product type, quantity, etc. This helps improve the customer experience, as customers can more clearly understand their order status, especially when tracking orders for multiple products. By splitting orders to various stores, errors and omissions in the processing process are reduced, and each sub-order corresponds to one store, reducing the probability of errors in the order processing process.

[0017] 3. This intelligent e-commerce logistics order management method, through a second order splitting strategy, sets thresholds for the number of goods, the volume of goods, and the weight of goods to control the quantity, volume, and weight of goods in each shipping order, ensuring the rationality and feasibility of the shipping orders. If the total number of goods, the total volume, and the total weight of goods in a sub-order are all within the allowable range, then the sub-order can be directly converted into a single shipping order, simplifying the processing flow. When the quantity, volume, or weight of goods in a sub-order exceeds the threshold, the sub-order is split into multiple shipping orders based on these conditions, improving the automation level of order processing. The number of shipping orders to be split is automatically calculated based on the total volume and total weight of the goods, ensuring that each shipping order can be transported efficiently in the logistics network.

[0018] 4. This intelligent e-commerce logistics order management method, through a third-stage order splitting strategy, transforms shipping order information into logistics order information, including detailed information such as warehouse identifier ID, product type, and product details. This detailed record helps track the logistics path of each product, improving logistics efficiency and accuracy. It obtains warehouse addresses based on warehouse identifier IDs and filters target warehouses, selecting the most suitable one from multiple possibilities for shipment. Optimization is performed based on factors such as distance, inventory status, and transportation costs to ensure goods reach their destination at the fastest speed and lowest cost. By filtering the set of warehouse addresses, the warehouse with the earliest estimated arrival time is selected as the target warehouse, improving delivery efficiency, reducing transportation time, and enhancing customer satisfaction. Especially when there are strict requirements for delivery speed, since each product may be distributed across multiple warehouses, the shipping strategy is adjusted based on real-time inventory and customer demand to ensure timely order completion. By controlling the estimated arrival time in the logistics order information and selecting the best warehouse for shipment, the customer shopping experience is improved. Customers can more accurately anticipate the arrival time of goods, reducing the uncertainty of waiting, and selecting the warehouse with the earliest estimated arrival time as the target warehouse.

[0019] 5. This intelligent e-commerce logistics order management method manages each product in the logistics order information by marking each product as the first product category and forming a set. Based on the product's storage cycle and warehousing time, it optimizes inventory turnover and reduces the risk of expired goods. Especially for products with short storage cycles, by filtering and sorting the warehousing time, it can prioritize the earliest warehousing products, thereby improving delivery efficiency, reducing inventory backlog, and reducing inventory costs by delivering the earliest warehousing products in a timely manner. It also avoids losses due to expired goods. Customers have high requirements for the freshness and timeliness of goods; prioritizing the delivery of the earliest warehousing products can improve customer satisfaction. It allows for flexible inventory allocation based on product type and warehouse conditions, ensuring timely delivery of each product category to meet market demand.

[0020] 6. This intelligent e-commerce logistics order management method implements a product arrival time matching strategy, considering the estimated arrival time of each product category to ensure that customers receive all products within their expected time. Especially when customers choose to pick up their orders on the same day, it can unify the arrival time, improving the customer experience. Based on the earliest and latest arrival times of the products, it helps optimize logistics scheduling, ensuring that all products arrive within the customer's expected time, reducing the waste of logistics resources. It allows for flexible adjustment of arrival times based on the customer's receiving method. For customers who wish to pick up all their orders at once, it will automatically adjust the arrival times of all products to meet their needs. Attached Figure Description

[0021] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0022] 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 creative efforts are within the scope of protection of the present invention.

[0023] Example 1: Obtaining order information; The order information includes main order information, sub-order information, shipping order information, and logistics order information; The main order information includes the number of stores corresponding to the products purchased by the user and the store information corresponding to each store; The store information includes a store identifier ID and a warehouse identifier ID, wherein one store identifier ID corresponds to one or more warehouse identifier IDs, and one warehouse identifier ID includes one or more store identifier IDs; All items purchased by a user at the same time generate a master order, which is then split into N sub-orders, where N is a positive integer. The same moment refers to the order placement time for all goods purchased by a user in a single transaction record; For example, if a user submits a transaction order at 19:00, this transaction order is set as the main order. The main order contains one or more products, and the order time for these products is the same, which is 19:00. Set the initial order splitting strategy; Convert the master order information into a sub-order information or split it into multiple sub-order information; The sub-order information includes the store information corresponding to the product purchased by the user, and the product information corresponding to the product purchased by the user in that store; One sub-order information corresponds to one store information, and one store information corresponds to one or more product information; The product information includes product type, product category, number of products, product volume, and product weight. Set a second order splitting strategy; Convert sub-order information into a single shipping order or split it into multiple shipping order information; The contents of the shipping order information are the store identifier ID and product information; Set product types, dividing them into short-term and long-term products; Set a storage duration threshold to determine the type of product; Obtain the storage duration of the goods; If the storage time of a product exceeds the storage time threshold, the product will be marked as a long-term product. If the storage time of a product is less than or equal to the storage time threshold, the product will be marked as a short-term product. Based on the product type, set a third order splitting strategy; Convert the shipping order information into a single logistics order information or split it into multiple logistics order information; Based on the information of each logistics order, a product arrival time matching strategy is implemented.

[0024] By implementing a product arrival time matching strategy, the estimated arrival time of logistics orders is adjusted according to the user's delivery method to meet the user's demand for same-day pickup. This flexible arrival time adjustment mechanism can improve customer satisfaction, especially during holidays or promotional events when customers have a more urgent need for fast delivery. Products are marked as short-term or long-term based on storage duration thresholds, and storage and shipping strategies are adjusted accordingly to reduce inventory backlog and logistics costs. Furthermore, shipping orders can be intelligently split based on product volume and weight thresholds to prevent individual orders from exceeding weight or volume limits, thereby reducing transportation costs. Optionally, the step of converting the master order information into a sub-order information or splitting it into multiple sub-order information includes: Retrieve master order information; If the number of stores corresponding to the products purchased by the user in the main order information is equal to 1, then the main order information will be converted into a sub-order information, and the content of the sub-order information will be generated in the sub-order information. If the number of stores corresponding to the products purchased by the user in the main order information is greater than 1, then the number of stores in the main order information is obtained and marked as the first number. The main order information is then split into the first number of sub-order information, and one sub-order information corresponds to one store information. In each sub-order information, the corresponding sub-order information content is generated. By employing an initial order splitting strategy, the number of stores corresponding to the user's purchased goods is obtained. The main order information is then converted into single or multiple sub-order information. When a user's purchased goods involve only one store, a sub-order is generated, reducing unnecessary splitting steps. For orders involving multiple stores, sub-orders are split according to the number of stores, ensuring that each sub-order can be processed for a specific store. By splitting the main order into sub-orders corresponding to the stores, logistics are allocated, especially during peak periods or promotional periods, ensuring that orders for each store are processed and shipped in a timely manner. Detailed sub-order information is generated for each store, including product type, quantity, etc. This helps improve the customer experience, as customers can more clearly understand their order status, especially when tracking orders for multiple products. By splitting orders to various stores, errors and omissions in the processing process are reduced, and each sub-order corresponds to one store, lowering the probability of errors in the order processing process. Optionally, the step of converting sub-order information into a single shipping order or splitting it into multiple shipping order information includes: Get the total number of items in the sub-order information, denoted as N; get the total volume of items in the sub-order information, denoted as V; get the total weight of items in the sub-order information, denoted as M. Set a threshold for the number of items, denoted as B; set a threshold for the volume of items, denoted as C; set a threshold for the weight of items, denoted as X. The product quantity threshold is used to determine the maximum number of products allowed within a single shipping order. The product volume threshold is used to determine the maximum allowed product volume within a single shipping order. The commodity weight threshold is used to determine the maximum allowed commodity weight within a single shipping order. In the sub-order information, if the total number of items N The threshold for the number of items is B, and the total volume of items is V. Product volume threshold C and total product weight M If the product weight threshold X is set, the sub-order information will be converted into a shipping order information; In the sub-order information, if the total number of items N If so, this condition is designated as Condition 1. The sub-order is then split into multiple shipping orders based on the total number of goods. The number of shipping orders is calculated using the following formula: Where A is the number of individual shipments split from the total number of goods. Indicates to Round up; In the sub-order information, if the total volume of the goods is V If so, this condition is designated as Condition Two. Based on the total volume of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where S is the number of individual shipments split from the total volume of the goods. Indicates to Round up; In the sub-order information, if the total weight of the goods is M If X is an example, then this condition is designated as condition three. Based on the total weight of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where D is the number of individual shipments split from the total volume of the goods. Indicates to Round up; During the process of splitting sub-orders, if conditions one, two, and three are met, the number of individual shipments is calculated and denoted as F, with the formula F=max(A,S,D). The max function is used to get the maximum value of the parameters inside the function; By employing a second order splitting strategy, thresholds for the number of goods, the volume of goods, and the weight of goods are set to control the quantity, volume, and weight of goods within each shipping order, ensuring the rationality and feasibility of the shipping orders. If the total number of goods, the total volume, and the total weight of goods in a sub-order are all within the allowable range, then the sub-order can be directly converted into a single shipping order, simplifying the processing flow. When the quantity, volume, or weight of goods in a sub-order exceeds the threshold, the sub-order is split into multiple shipping orders based on these conditions, improving the automation level of order processing. The number of shipping orders to be split is automatically calculated based on the total volume and total weight of the goods, ensuring that each shipping order can be transported efficiently in the logistics network. Optionally, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information includes: The logistics order information includes warehouse identification ID, product type, product category, user's delivery address, and estimated delivery time; The product categories refer to the different categories of products in the logistics order information. For each different product in the logistics order information, it is recorded as a product category. Each type of product corresponds to one or more warehouse identifiers (IDs); This indicates that each type of product is allowed to be distributed across multiple warehouses; Obtain the warehouse address based on the warehouse identifier ID; Obtain the warehouse address corresponding to each product in the logistics information sheet, and form a set of warehouse addresses; Filter the elements in the set of warehouse addresses and mark the warehouses corresponding to the filtered warehouse addresses as target warehouses.

[0025] Optionally, the step of filtering the elements in the warehouse address set and marking the warehouses corresponding to the filtered warehouse addresses as target warehouses includes: The specific screening steps are as follows: Obtain the user's shipping address; Based on the set of warehouse addresses, obtain the distance between each element in the set and the user's delivery address, and calculate the estimated delivery time of each element in the set relative to the user's delivery address based on the distance. Select the warehouse address with the earliest estimated arrival time and mark that address as the target address; Through a third order splitting strategy, shipping order information is transformed into logistics order information, including detailed information such as warehouse identifier ID, product type, and product category. This detailed record helps track the logistics path of each product, improving logistics efficiency and accuracy. Warehouse addresses are obtained based on warehouse identifier IDs, and target warehouses are filtered out. The most suitable warehouse is selected from multiple possibilities for shipment. Optimization is performed based on factors such as distance, inventory status, and transportation costs to ensure that goods reach their destination at the fastest speed and lowest cost. By filtering the set of warehouse addresses, the warehouse with the earliest estimated arrival time is selected as the target warehouse, improving delivery efficiency, reducing transportation time, and enhancing customer satisfaction. This is especially important when there are strict requirements for delivery speed. Since each product may be distributed across multiple warehouses, the shipping strategy is adjusted based on real-time inventory and customer demand to ensure timely order completion. By controlling the estimated arrival time in the logistics order information and selecting the best warehouse for shipment, the customer shopping experience is improved. Customers can more accurately anticipate the arrival time of goods, reducing the uncertainty of waiting, and selecting the warehouse with the earliest estimated arrival time as the target warehouse. Furthermore, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information also includes: Each type of commodity in the logistics order information is labeled as the first commodity type, and together they form the first commodity type set; The information corresponding to each element in the first set of product categories includes the product category and the corresponding number of products, and the corresponding number of products is marked as the second number; Based on the first set of product categories, obtain the target warehouse for each element in the set, where each first product category corresponds to one target warehouse; Based on the target warehouse, obtain the product type of the first product category in that warehouse, as well as the inbound time of all products under the first product category; The storage period is the maximum permissible storage time for the goods; For example, under certain conditions, the storage period for apples is about 20 days. The warehousing time is the time recorded when the goods are received into the warehouse; Obtain the inbound time of all products under the first product category, and sort them according to the order of inbound time to form the first sequence; Retrieve all items in the first sequence whose serial numbers are from the first to the second number, and mark them as the target items; All selected target products are grouped into a set, denoted as the target product set; In the logistics order information, each first product category corresponds to a set of target products.

[0026] Optionally, the step of converting the shipping order information into a single logistics order or splitting it into multiple logistics order information further includes: Based on the logistics order information, obtain the product type for each first product category; If all the first commodity categories in the logistics order information are either short-term or long-term commodities, then the shipping order information will be converted into a single logistics order information. If the first product category in the logistics order information contains both short-term and long-term products, the shipping order information will be split, and the specific splitting rules are as follows: The set of target products corresponding to the first category of short-term goods is divided into one logistics order information; The set of target products corresponding to the first product category, which belongs to long-term products, is divided into a single logistics order information; By labeling each product as a primary product category and grouping them into sets, the system manages each product in the logistics order information. Based on the product's storage cycle and warehousing time, it optimizes inventory turnover and reduces the risk of expired goods. Especially for products with short storage cycles, by screening and sorting the warehousing time of products, it can prioritize the processing of those that entered the warehouse earliest, thereby improving delivery efficiency, reducing inventory backlog, and reducing inventory costs by delivering the earliest-entered products in a timely manner. It also avoids losses caused by expired products. Customers have high requirements for the freshness and timeliness of products. By prioritizing the delivery of the earliest-entered products, it can improve customer satisfaction. It allows for flexible allocation of inventory based on product type and warehouse conditions, ensuring that each product category can be delivered in a timely manner to meet market demand. Furthermore, the step of executing a goods arrival time matching strategy based on each logistics order information includes: After obtaining the master order information and executing the first to third order splitting strategies, all logistics order information is split off; Based on the logistics order information, obtain the target warehouse and the corresponding estimated delivery time for each first product category; In the logistics order information, each first product category corresponds to an estimated arrival time; Get the first product category with the earliest estimated arrival time and mark the earliest estimated arrival time as the earliest arrival time; Get the first product category with the latest estimated arrival time, and mark the latest estimated arrival time as the latest arrival time; Using a 24-hour day as the unit, one day represents 1, and two days represent 2; If the difference between the earliest and latest delivery times is greater than or equal to 1 day, then obtain the user's delivery method. The delivery method refers to whether the user chooses to complete the pickup of goods purchased in one transaction record on the same day; If a user's delivery method is to pick up all the goods purchased in one transaction record on the same day, then the estimated arrival time of each logistics order in one transaction record will be changed to the latest arrival time. If a user's delivery method involves picking up goods purchased in a single transaction record on different days, no action will be taken on the logistics order information. If the difference between the earliest and latest delivery times is less than 1 day, no action will be taken on the logistics order information. Implement a product arrival time matching strategy, taking into account the estimated arrival time of each product category, to ensure that customers receive all products within the expected time. Especially when customers choose to pick up their goods on the same day, it can unify the arrival time, improve the customer experience, and optimize logistics scheduling based on the earliest and latest arrival times of the products, ensuring that all products arrive within the customer's expected time and reducing the waste of logistics resources. It allows for flexible adjustment of arrival times based on the customer's receiving method. For customers who wish to pick up their goods all at once, the arrival time of all products will be automatically adjusted to meet the customer's needs. It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0027] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intelligent management of e-commerce logistics orders, characterized in that, include: All items purchased by a user at the same time generate a master order, which is then split into N sub-orders. Convert the master order information into a sub-order information or split it into multiple sub-order information; Convert sub-order information into a single shipping order or split it into multiple shipping order information; Convert the shipping order information into a single logistics order or split it into multiple logistics order information; obtain the warehouse address corresponding to each product in the logistics order and form a set of warehouse addresses; filter the elements in the warehouse address set and mark the warehouses corresponding to the filtered warehouse addresses as the target warehouses; Each type of commodity in the logistics order information is labeled as the first commodity type, and together they form the first commodity type set; The information corresponding to each element in the first product category set includes the product category and the corresponding number of products, and the corresponding number of products is marked as the second number; based on the first product category set, the target warehouse for each element in the set is obtained, where each first product category corresponds to one target warehouse; Based on the target warehouse, obtain the product type of the first product category in that warehouse, as well as the inbound time of all products under the first product category; Take the inbound time of all goods under the first product category and sort them according to the order of inbound time to form the first sequence; obtain all goods in the first sequence from the first number to the second number, and mark them as target goods; form a set of all selected target goods, denoted as the target goods set; obtain the product type of each first product category according to the logistics order information; if the product type of the first product category in the logistics order information contains both short-term and long-term goods, then split the shipping order information, the specific splitting rules are as follows: divide the target goods set corresponding to the first product category with the product type of short-term goods into one logistics order information; divide the target goods set corresponding to the first product category with the product type of long-term goods into one logistics order information; Match the arrival time of the goods based on the information of each logistics order.

2. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, The process of converting the master order information into a sub-order information or splitting it into multiple sub-order information includes: Retrieve master order information; If the number of stores corresponding to the products purchased by the user in the main order information is equal to 1, then the main order information will be converted into a sub-order information, and the content of the sub-order information will be generated in the sub-order information. If the number of stores corresponding to the products purchased by the user in the main order information is greater than 1, then the number of stores in the main order information is obtained and marked as the first number. The main order information is then split into the first number of sub-order information, and each sub-order information corresponds to one store information. In each sub-order information, the corresponding sub-order information content is generated.

3. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, The process of converting sub-order information into a single shipping order or splitting it into multiple shipping order information includes: Get the total number of items in the sub-order information, denoted as N; get the total volume of items in the sub-order information, denoted as V; get the total weight of items in the sub-order information, denoted as M. Set a threshold for the number of items, denoted as B; set a threshold for the volume of items, denoted as C; set a threshold for the weight of items, denoted as X. The product quantity threshold is used to determine the maximum number of products allowed within a single shipping order. The product volume threshold is used to determine the maximum allowed product volume within a single shipping order. The commodity weight threshold is used to determine the maximum allowed commodity weight within a single shipping order. In the sub-order information, if the total number of items N The threshold for the number of items is B, and the total volume of items is V. Product volume threshold C and total product weight M If the product weight threshold X is set, the sub-order information will be converted into a shipping order information; In the sub-order information, if the total number of items N If so, then the condition is recorded as condition one, and the sub-order is split into multiple shipping orders based on the total number of goods. The number of shipping orders is calculated using the following formula: Where A is the number of individual shipments split from the total number of goods. Indicates to Round up; In the sub-order information, if the total volume of the goods is V If so, this condition is designated as Condition Two. Based on the total volume of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where S is the number of individual shipments split from the total volume of the goods. Indicates to Round up; In the sub-order information, if the total weight of the goods is M If X is an example, then this condition is designated as condition three. Based on the total weight of the goods, the sub-order is split into multiple shipping orders, and the number of shipping orders is calculated using the following formula: Where D is the number of individual shipments split from the total volume of the goods. Indicates to Round up; During the process of splitting sub-orders, if conditions one, two, and three are met, the number of individual shipments is calculated and denoted as F, with the formula F=max(A,S,D). The max function is used to get the maximum value of the parameters inside the function.

4. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, The logistics order information includes warehouse identification ID, product type, product category, user's delivery address, and estimated delivery time; The product categories refer to the different categories of products in the logistics order information. For each different product in the logistics order information, it is recorded as a product category. Each type of product corresponds to one or more warehouse identifiers (IDs); Obtain the warehouse address based on the warehouse identifier ID.

5. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, include: The specific steps for filtering elements in the warehouse address set and marking the warehouses corresponding to the filtered warehouse addresses as target warehouses are as follows: Obtain the user's shipping address; Based on the set of warehouse addresses, obtain the distance between each element in the set and the user's delivery address, and calculate the estimated delivery time of each element in the set relative to the user's delivery address based on the distance. Select the warehouse address with the earliest estimated arrival time and mark that address as the target address.

6. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, In the logistics order information, each first product category corresponds to a set of target products.

7. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, include: If all the first commodity categories in the logistics order information are either short-term or long-term commodities, then the shipping order information will be converted into a single logistics order information.

8. The intelligent management method for e-commerce logistics orders according to claim 1, characterized in that, The process of matching the arrival time of goods based on each logistics order information includes: After obtaining the master order information and executing the first to third order splitting strategies, all logistics order information is split off; Based on the logistics order information, obtain the target warehouse and the corresponding estimated delivery time for each first product category; In the logistics order information, each first product category corresponds to an estimated arrival time; Get the first product category with the earliest estimated arrival time and mark the earliest estimated arrival time as the earliest arrival time; Get the first product category with the latest estimated arrival time, and mark the latest estimated arrival time as the latest arrival time; Using a 24-hour day as the unit, one day represents 1, and two days represent 2; If the difference between the earliest and latest delivery times is greater than or equal to 1 day, then obtain the user's delivery method. The delivery method refers to whether the user chooses to complete the pickup of goods purchased in one transaction record on the same day; If a user's delivery method is to pick up all the goods purchased in one transaction record on the same day, then the estimated arrival time of each logistics order in one transaction record will be changed to the latest arrival time. If a user's delivery method involves picking up goods purchased in a single transaction record on different days, no action will be taken on the logistics order information. If the difference between the earliest and latest delivery times is less than 1 day, no action will be taken on the logistics order information.

Images