A network retail order management scheduling method and system

Abstract

The application provides a network retail order management scheduling method and system, wherein the method comprises the following steps: S1, obtaining new order information of target goods which a merchant needs to deliver on a network retail platform; S2, obtaining preset warehouse distribution information corresponding to the target goods; and S3, performing warehouse delivery scheduling based on the new order information and the warehouse distribution information; wherein the new order information comprises customer addresses and the quantity of goods; and the warehouse distribution information comprises a plurality of groups of one-to-one corresponding warehouses, warehouse addresses and the quantity of goods in stock. The network retail order management scheduling method and system can perform warehouse delivery scheduling when the merchant needs to deliver goods with new orders, without the need to interface with the delivery party, so that the error of the delivery interface can be avoided, and the delivery party does not need to determine a suitable delivery warehouse according to the customer addresses and the principle of proximity, so that the convenience is greatly improved.

Description

Technical Field

[0001] This invention relates to the field of online retail technology, and in particular to an online retail order management and scheduling method and system. Background Technology

[0002] Currently, some online retailers use a dropshipping model [for example: the merchant doesn't stock or pack goods, but contacts the manufacturer when an order comes in, and the manufacturer ships the goods; or the merchant stocks goods but doesn't pack them, transporting the stocked goods to the dropshipper's warehouse, and contacts the dropshipper when an order comes in, and the dropshipper packs and ships the goods from the warehouse]. However, when a new order comes in, the merchant needs to directly coordinate with the dropshipper, sending the order information, which is cumbersome, especially when only a portion of the merchant's products require dropshipping, potentially leading to errors in the coordination process. Furthermore, dropshippers often have multiple warehouses, requiring them to determine the most suitable warehouse based on the customer's address, reducing convenience.

[0003] Therefore, a solution is urgently needed. Summary of the Invention

[0004] One of the objectives of this invention is to provide a method for managing and scheduling online retail orders. When a merchant receives a new order for a product that needs to be shipped, the merchant can schedule the shipment from the warehouse itself without having to connect with the shipping agent, thus avoiding errors in the shipping process. Furthermore, the shipping agent does not need to determine a suitable warehouse based on the customer's address and proximity, which greatly improves convenience.

[0005] This invention provides a method for managing and scheduling online retail orders, comprising:

[0006] Step S1: Obtain new order information for the target products that the merchant needs to fulfill on the online retail platform;

[0007] Step S2: Obtain the preset warehouse distribution information corresponding to the target product;

[0008] Step S3: Based on the new order information and the warehouse distribution information, perform warehouse dispatch scheduling;

[0009] The new order information includes: customer address and quantity of goods;

[0010] The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining inventory.

[0011] Preferably, step S1: obtaining new order information for the target products that the merchant needs to fulfill on the online retail platform, including:

[0012] Obtain multiple products for sale from a merchant's online store on an online retail platform;

[0013] Obtain the merchant's pre-set product dropshipping contract;

[0014] Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale.

[0015] Obtain the order information for unshipped goods from the online store;

[0016] Determine the new order information for the target product from the unshipped product order information.

[0017] Preferably, step S3: based on the new order information and the warehouse distribution information, performing warehouse dispatch scheduling includes:

[0018] The warehouse corresponding to the remaining quantity of goods that is greater than or equal to the quantity of the goods shall be designated as the target warehouse;

[0019] Determine the shipping logistics route between the warehouse address corresponding to the target warehouse and the customer address from the preset shipping logistics route database;

[0020] The target warehouse corresponding to the shortest shipping logistics route shall be used as the shipping warehouse;

[0021] Based on a preset order picking and drop shipping task generation template, an order picking and drop shipping task is generated according to the customer address and quantity of the target product.

[0022] The picking and dispatching task is delivered to the preset task dispatch node corresponding to the shipping warehouse.

[0023] Preferably, before step S2: obtaining the preset warehouse distribution information corresponding to the target product, the method further includes:

[0024] The system acquires multiple operational behaviors generated by customers who purchase the target product within a preset time period after purchasing the target product on the online retail platform.

[0025] Based on the aforementioned operational behavior, predict whether the customer will cancel the purchase of the target product;

[0026] If so, the new order information corresponding to the customer will be temporarily suspended;

[0027] If the customer does not cancel the purchase of the target product within the preset waiting time, the new order information of the customer will be temporarily suspended.

[0028] Preferably, based on the operational behavior, predicting whether the customer will cancel the purchase of the target product includes:

[0029] Obtain sales information of the target product sold by the merchant on the online sales platform;

[0030] Based on the preset prediction trigger condition generation module, prediction trigger conditions are generated according to the sales information;

[0031] Establish a timeline, and place the operation on the timeline based on the time when the operation occurs;

[0032] Determine whether the operation meets the prediction triggering condition;

[0033] If so, the corresponding operation behavior shall be taken as the target operation behavior;

[0034] The operation behavior between the first and last target operation behavior on the timeline is used as the basis for prediction.

[0035] Based on a preset feature extraction template, features are extracted from the prediction basis to obtain multiple feature values;

[0036] Based on the aforementioned feature values, it is predicted whether the customer will cancel their purchase of the target product.

[0037] Preferably, predicting whether the customer will cancel the purchase of the target product based on the feature value includes:

[0038] The feature values ​​are input into a preset cancellation prediction model to predict whether the customer will cancel the purchase of the target product;

[0039] And / or,

[0040] Based on the feature values, a first description vector for the prediction basis is constructed;

[0041] Obtain a preset cancellation prediction library, which includes: multiple sets of one-to-one corresponding second description vectors and vector matching degree requirements;

[0042] Perform vector matching between the first description vector and any of the second description vectors to obtain the vector matching degree;

[0043] If the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, it is predicted that the customer will cancel the purchase of the target product.

[0044] Preferred online retail order management and scheduling methods also include:

[0045] When a customer who purchased the target product needs to return the product after receiving it, the system retrieves the pickup time slot and pickup address entered by the customer.

[0046] Multiple logistics personnel within a preset range around the pickup address are determined from a preset distribution map of logistics personnel corresponding to the city where the customer is located.

[0047] Obtain multiple future work schedules for the logistics personnel; the work schedules include: starting location, ending location, and latest arrival time;

[0048] Determine a target work schedule from the work schedule that allows the logistics personnel to conveniently pass through the pickup address during the pickup time period to pick up the target goods returned by the customer;

[0049] The corresponding logistics personnel have been set as target logistics personnel;

[0050] Based on the preset door-to-door pickup task generation module, a door-to-door pickup task is generated according to the door-to-door pickup time period, pickup address and target work schedule;

[0051] The door-to-door pickup task is pushed to the target logistics personnel.

[0052] Preferably, determining the target work schedule from the work schedule that facilitates the logistics personnel's passage through the pickup address to pick up the target goods returned by the customer during the pickup time period includes:

[0053] Within the logistics personnel distribution map, a target circle is drawn with the line connecting the starting position and the ending position of any of the work trips as its diameter;

[0054] If the pickup address is within the target circle, the corresponding work schedule will be selected as the work schedule to be selected.

[0055] Within the logistics personnel distribution map, the latest arrival time of the previous work trip of the candidate work trip is used as the departure time to predict the first arrival time of the logistics personnel from the starting position of the candidate work trip to the pickup address. At the same time, the first arrival time is used as the departure time to predict the second arrival time of the logistics personnel from the pickup address to the end position of the candidate work trip.

[0056] If the first arrival time falls within the door-to-door pickup time period and the second arrival time is before the latest arrival time of the candidate work schedule, the corresponding candidate work schedule will be used as the target work schedule.

[0057] An embodiment of the present invention provides an online retail order management and scheduling system, comprising:

[0058] The first acquisition module is used to acquire new order information for the target products that merchants need to dropship on the online retail platform;

[0059] The second acquisition module is used to acquire preset warehouse distribution information corresponding to the target product;

[0060] The scheduling module is used to perform warehouse dispatch scheduling based on the new order information and the warehouse distribution information;

[0061] The new order information includes: customer address and quantity of goods;

[0062] The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining inventory.

[0063] Preferably, the first acquisition module acquires new order information for the target products that the merchant needs to fulfill on the online retail platform, including:

[0064] Obtain multiple products for sale from a merchant's online store on an online retail platform;

[0065] Obtain the merchant's pre-set product dropshipping contract;

[0066] Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale.

[0067] Obtain the order information for unshipped goods from the online store;

[0068] Determine the new order information for the target product from the unshipped product order information.

[0069] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0070] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0071] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0072] Figure 1 This is a schematic diagram of a network retail order management and scheduling method according to an embodiment of the present invention;

[0073] Figure 2 This is a schematic diagram of an online retail order management and scheduling system according to an embodiment of the present invention. Detailed Implementation

[0074] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0075] This invention provides a method for managing and scheduling online retail orders, such as... Figure 1 As shown, it includes:

[0076] Step S1: Obtain new order information for the target products that the merchant needs to fulfill on the online retail platform;

[0077] Step S2: Obtain the preset warehouse distribution information corresponding to the target product;

[0078] Step S3: Based on the new order information and the warehouse distribution information, perform warehouse dispatch scheduling;

[0079] The new order information includes: customer address and quantity of goods;

[0080] The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining inventory.

[0081] The working principle and beneficial effects of the above technical solution are as follows:

[0082] When a customer purchases a target product from a merchant on an online retail platform, a new order is generated and retrieved. This new order information includes the customer's address and the quantity of goods purchased. Pre-defined warehouse distribution information for the target product includes the warehouse where the target product is stored, the warehouse address, and the remaining quantity of the target product within that warehouse. Based on the new order information and warehouse distribution information, warehouse dispatching is performed. For example, a warehouse with a remaining quantity greater than or equal to the customer's purchase quantity is identified. Then, based on the customer's address, a suitable shipping warehouse within that warehouse is selected based on proximity. Picking and packing personnel within the shipping warehouse are then dispatched to pick, pack, and ship the goods according to the new order information.

[0083] When a merchant receives a new order for a product that needs to be shipped, this application allows the merchant to manage the warehouse shipping schedule independently without having to liaise with the shipping provider, thus avoiding errors in shipping coordination. It also eliminates the need for the shipping provider to determine a suitable warehouse based on the customer's address and proximity, greatly improving convenience.

[0084] In one embodiment, step S1: obtaining new order information for the target products that the merchant needs to fulfill on the online retail platform includes:

[0085] Obtain multiple products for sale from a merchant's online store on an online retail platform;

[0086] Obtain the merchant's pre-set product dropshipping contract;

[0087] Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale.

[0088] Obtain the order information for unshipped goods from the online store;

[0089] Determine the new order information for the target product from the unshipped product order information.

[0090] The working principle and beneficial effects of the above technical solution are as follows:

[0091] When merchants require dropshipping, they sign a dropshipping contract with the dropshipping provider. This contract specifies the products to be dropshipped. Therefore, based on this contract, the target products for dropshipping are determined from the merchant's online store on the online retail platform, considering multiple products currently for sale. The online store's backend aggregates all unshipped order information for all currently sold products, and new order information for the target products is then identified from this unshipped order information. Determining the target products for dropshipping based on the contract and identifying new order information for these target products from the online store's unshipped order information improves the rationality of obtaining new order information for the target products.

[0092] In one embodiment, step S3: performing warehouse dispatch scheduling based on the new order information and the warehouse distribution information, includes:

[0093] The warehouse corresponding to the remaining quantity of goods that is greater than or equal to the quantity of the goods shall be designated as the target warehouse;

[0094] Determine the shipping logistics route between the warehouse address corresponding to the target warehouse and the customer address from the preset shipping logistics route database;

[0095] The target warehouse corresponding to the shortest shipping logistics route shall be used as the shipping warehouse;

[0096] Based on a preset order picking and drop shipping task generation template, an order picking and drop shipping task is generated according to the customer address and quantity of the target product.

[0097] The picking and dispatching task is delivered to the preset task dispatch node corresponding to the shipping warehouse.

[0098] The working principle and beneficial effects of the above technical solution are as follows:

[0099] The warehouse's remaining inventory should be sufficient for shipment. Therefore, warehouses with inventory equal to or greater than the quantity of goods are designated as target warehouses. Logistics routes (such as those of courier and logistics companies) do not run directly from the origin to the destination. For example, a package shipped from Yancheng, Jiangsu to Shanghai may transit through Huai'an. Therefore, a pre-defined shipping route database is introduced, containing numerous routes between origin and destination. The shortest shipping route between the target warehouse's address and the customer's address is selected, and the target warehouse with the shortest route is designated as the shipping warehouse. This improves the rationality and efficiency of warehouse selection. The pre-defined picking and dispatching task generation template is: "Please arrange dispatching: xx items of target goods, address: xx." Based on this template, picking and dispatching tasks are generated according to the customer's address and the quantity of the target goods. The pre-defined task distribution node corresponding to the shipping warehouse is a network node that communicates with the terminals (mobile phones and PDAs, etc.) used by the personnel responsible for dispatching within the warehouse. Picking and dispatching tasks are delivered to the task distribution node, and the corresponding personnel can view and arrange dispatching. This improves scheduling efficiency.

[0100] In one embodiment, before step S2: obtaining the preset warehouse distribution information corresponding to the target product, the method further includes:

[0101] The system acquires multiple operational behaviors generated by customers who purchase the target product within a preset time period after purchasing the target product on the online retail platform.

[0102] Based on the aforementioned operational behavior, predict whether the customer will cancel the purchase of the target product;

[0103] If so, the new order information corresponding to the customer will be temporarily suspended;

[0104] If the customer does not cancel the purchase of the target product within the preset waiting time, the new order information of the customer will be temporarily suspended.

[0105] The working principle and beneficial effects of the above technical solution are as follows:

[0106] To improve shipping efficiency and avoid order backlogs and delays, especially during major online sales events like "Double Eleven," warehouses typically arrange packing and shipping immediately upon receiving a shipping task. However, during this period, customers may cancel their orders (e.g., comparing with other similar products and then canceling the order to purchase other similar items). If shipping has already been arranged, this could result in unusable packing boxes, wasted printed shipping labels, and wasted resources for picking and packing personnel or equipment. Therefore, this system collects multiple customer actions on the online retail platform within a preset timeframe (e.g., 15 minutes) after purchase (e.g., browsing product information such as price, type, and number and duration of browsing product details pages). Based on these actions, it predicts whether the customer will cancel their purchase of the target product (e.g., whether the user is comparing prices with similar products; if so, there is a possibility of order cancellation). If so, the customer's new order information is temporarily suspended, and shipping is not arranged. If the customer does not cancel their purchase of the target product within a preset waiting time (e.g., 20 minutes), the temporary suspension is lifted, and shipping is arranged. It is highly applicable in avoiding a series of problems caused by customer order cancellations when arranging shipment.

[0107] In one embodiment, predicting whether the customer will cancel the purchase of the target product based on the operational behavior includes:

[0108] Obtain sales information of the target product sold by the merchant on the online sales platform;

[0109] Based on the preset prediction trigger condition generation module, prediction trigger conditions are generated according to the sales information;

[0110] Establish a timeline, and place the operation on the timeline based on the time when the operation occurs;

[0111] Determine whether the operation meets the prediction triggering condition;

[0112] If so, the corresponding operation behavior shall be taken as the target operation behavior;

[0113] The operation behavior between the first and last target operation behavior on the timeline is used as the basis for prediction.

[0114] Based on a preset feature extraction template, features are extracted from the prediction basis to obtain multiple feature values;

[0115] Based on the aforementioned feature values, it is predicted whether the customer will cancel their purchase of the target product.

[0116] The working principle and beneficial effects of the above technical solution are as follows:

[0117] The sales information for the target product includes its selling price and product type. The preset prediction trigger condition generation module is as follows: the prediction trigger condition is browsing other products of the same product type as the target product and / or browsing other products with similar or substitutable functions (e.g., robot vacuums and vacuum cleaners, face towels and hand towels, etc.) and / or the selling price of other products of the same product type being close to the selling price of the target product (the absolute value of the difference is less than a certain value) and / or the selling price of other products with similar or substitutable functions being close to the selling price of the target product. Based on the prediction trigger condition generation module, prediction trigger conditions are generated according to the sales information. If the action meets the prediction trigger condition, it indicates that the action reflects the customer's potential order cancellation, and this is considered the target action. Generally, during the period when the customer performs the target action, other actions that may influence the prediction of order cancellation may also occur, such as checking the merchant's store qualifications, indicating that the customer is not entirely certain about purchasing from that merchant. Therefore, the actions between the first and last target action on the timeline are used as the basis for prediction. The system extracts feature values ​​for prediction, including: the total number of views and total viewing time of other products of the same type as the target product; the total number of views and total viewing time of other products with similar or substitutable functions to the target product; the total number of other products of the same type as the target product whose selling price is close to its selling price; and the total number of other products with similar or substitutable functions to the target product whose selling price is close to its selling price. Prediction is then made based on these feature values. Prediction trigger conditions are introduced to filter out predictive criteria that can be used to determine whether a user will cancel their order, improving the system's efficiency and the accuracy of the selected prediction criteria.

[0118] In one embodiment, predicting whether a customer will cancel their purchase of the target product based on the feature value includes:

[0119] The feature values ​​are input into a preset cancellation prediction model to predict whether the customer will cancel the purchase of the target product;

[0120] And / or,

[0121] Based on the feature values, a first description vector for the prediction basis is constructed;

[0122] Obtain a preset cancellation prediction library, which includes: multiple sets of one-to-one corresponding second description vectors and vector matching degree requirements;

[0123] Perform vector matching between the first description vector and any of the second description vectors to obtain the vector matching degree;

[0124] If the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, it is predicted that the customer will cancel the purchase of the target product.

[0125] The working principle and beneficial effects of the above technical solution are as follows:

[0126] There are two methods for predicting whether a customer will cancel a purchase of a target product based on feature values: 1. Input a pre-defined cancellation prediction model. This model is a neural network trained to convergence using a large number of feature values ​​that indicate a customer might cancel an order (e.g., a high total number of views and a long total viewing time for other products of the same type as the target product, indicating the user is indecisive or comparing). 2. Introduce a second descriptive vector and a vector matching requirement. The second descriptive vector is constructed from the feature values ​​indicating a customer might cancel an order. The vector matching requirement is the requirement that the first and second descriptive vectors used for prediction should meet when predicting whether a customer will cancel an order. For example, a vector matching degree greater than or equal to 85. The first descriptive vector is matched with any second descriptive vector to obtain the matching degree. If the matching degree meets the corresponding vector matching degree requirement, it indicates that the user might cancel the order. Introducing these two methods for predicting whether a customer will cancel a purchase of a target product based on feature values ​​improves the system's applicability and, more importantly, the prediction efficiency of whether a customer will cancel an order.

[0127] In one embodiment, the online retail order management and scheduling method further includes:

[0128] When a customer who purchased the target product needs to return the product after receiving it, the system retrieves the pickup time slot and pickup address entered by the customer.

[0129] Multiple logistics personnel within a preset range around the pickup address are determined from a preset distribution map of logistics personnel corresponding to the city where the customer is located.

[0130] Obtain multiple future work schedules for the logistics personnel; the work schedules include: starting location, ending location, and latest arrival time;

[0131] Determine a target work schedule from the work schedule that allows the logistics personnel to conveniently pass through the pickup address during the pickup time period to pick up the target goods returned by the customer;

[0132] The corresponding logistics personnel have been set as target logistics personnel;

[0133] Based on the preset door-to-door pickup task generation module, a door-to-door pickup task is generated according to the door-to-door pickup time period, pickup address and target work schedule;

[0134] The door-to-door pickup task is pushed to the target logistics personnel.

[0135] The working principle and beneficial effects of the above technical solution are as follows:

[0136] If a customer is not satisfied with the goods after receiving them, they can return them. Currently, returns are mostly handled by a courier picking up the goods at the customer's door. The customer inputs the pickup time slot and pickup address. A preset logistics personnel distribution map for the customer's city is a city map showing the real-time locations of various logistics personnel (couriers, etc.) within the city. Multiple logistics personnel within a preset range around the pickup address (a circle with a diameter of 2 kilometers centered on the pickup address) are identified from the logistics personnel distribution map. Multiple future work schedules of the logistics personnel are obtained. A target work schedule is determined from these schedules, ensuring the logistics personnel can conveniently pass through the pickup address to pick up the returned goods within the specified pickup time slot. If so, this is designated as the target work schedule. The preset door-to-door pickup task generation module displays: "Hello! You can pick up the goods at xxxx [target work schedule] at xxxx [pickup address]. There is ample time; the arrival time is xxxx [pickup time slot]." Based on the door-to-door pickup task generation module, a door-to-door pickup task is generated according to the pickup time slot, pickup address, and the target work schedule, and then pushed to the target logistics personnel. Generally, return pickups are scheduled for the next day to allow delivery personnel to plan their routes (delivery and pickup) according to their schedules. This results in lower efficiency and impacts the merchant's inventory turnover. This invention, however, rationally determines suitable personnel for convenient mid-journey pickup based on the logistics staff's work schedule and the customer's input pickup time and address. Furthermore, it generates pickup tasks based on the target work schedule, allowing for schedule planning by the logistics staff. This eliminates the need for further schedule planning by the staff, making the process more user-friendly, improving return efficiency, and enhancing overall intelligence.

[0137] In one embodiment, determining a target work schedule from the work schedule that facilitates the logistics personnel's passage through the pickup address to pick up the target goods returned by the customer during the pickup time period includes:

[0138] Within the logistics personnel distribution map, a target circle is drawn with the line connecting the starting position and the ending position of any of the work trips as its diameter;

[0139] If the pickup address is within the target circle, the corresponding work schedule will be selected as the work schedule to be selected.

[0140] Within the logistics personnel distribution map, the latest arrival time of the previous work trip of the candidate work trip is used as the departure time to predict the first arrival time of the logistics personnel from the starting position of the candidate work trip to the pickup address. At the same time, the first arrival time is used as the departure time to predict the second arrival time of the logistics personnel from the pickup address to the end position of the candidate work trip.

[0141] If the first arrival time falls within the door-to-door pickup time period and the second arrival time is before the latest arrival time of the candidate work schedule, the corresponding candidate work schedule will be used as the target work schedule.

[0142] The working principle and beneficial effects of the above technical solution are as follows:

[0143] Within the logistics personnel distribution map, a target circle is drawn with the line connecting the starting and ending points as its diameter. If the pickup address is within the target circle, it indicates that the logistics personnel will not take a significant detour if they pick up the goods midway through the work trip, and this is considered a candidate work trip. The latest arrival time of the previous work trip (the latest time to complete the previous work trip) is used as the departure time to predict the first arrival time of the logistics personnel from the starting point of the candidate work trip to the pickup address. (Arrival time prediction falls within the scope of existing technology; for example, navigation apps display estimated arrival times during route planning.) The second arrival time of the logistics personnel from the pickup address to the ending point of the candidate work trip is predicted using the arrival time as the departure time. If the first arrival time is within the door-to-door pickup time slot and the second arrival time is before the latest arrival time of the candidate work trip, it indicates that the logistics personnel can pick up the goods within the customer's desired time slot without delaying the execution of the work trip, and this is considered the target work trip. This invention improves the accuracy and rationality of predictions by determining alternative workflows that logistics personnel will not take detours based on the target circle and determining whether it is convenient for logistics personnel to pick up goods en route based on arrival time prediction.

[0144] This invention provides an online retail order management and scheduling system, such as... Figure 2 As shown, it includes:

[0145] The first acquisition module 1 is used to acquire new order information of the target products that the merchant needs to ship on the online retail platform;

[0146] The second acquisition module 2 is used to acquire preset warehouse distribution information corresponding to the target product;

[0147] Scheduling module 3 is used to perform warehouse dispatch scheduling based on the new order information and the warehouse distribution information;

[0148] The new order information includes: customer address and quantity of goods;

[0149] The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining inventory.

[0150] In one embodiment, the first acquisition module acquires new order information for target products that the merchant needs to fulfill on the online retail platform, including:

[0151] Obtain multiple products for sale from a merchant's online store on an online retail platform;

[0152] Obtain the merchant's pre-set product dropshipping contract;

[0153] Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale.

[0154] Obtain the order information for unshipped goods from the online store;

[0155] Determine the new order information for the target product from the unshipped product order information.

[0156] This is contrary to the spirit and scope of the invention. Therefore, if these modifications and variations of the invention fall within the scope of the claims of the invention and their equivalents, the invention also intends to include these modifications and variations.

Claims

1. A method for managing and scheduling online retail orders, characterized in that, include: Step S1: Obtain new order information for the target products that the merchant needs to fulfill on the online retail platform; Step S2: Obtain the preset warehouse distribution information corresponding to the target product; Step S3: Based on the new order information and the warehouse distribution information, perform warehouse dispatch scheduling; The new order information includes: customer address and quantity of goods; The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining product quantities; Also includes: When a customer who purchased the target product needs to return the product after receiving it, the system retrieves the pickup time slot and pickup address entered by the customer. Multiple logistics personnel within a preset range around the pickup address are determined from a preset distribution map of logistics personnel corresponding to the city where the customer is located. Obtain multiple future work schedules for the logistics personnel; the work schedules include: starting location, ending location, and latest arrival time; Determine a target work schedule from the work schedule that allows the logistics personnel to conveniently pass through the pickup address during the pickup time period to pick up the target goods returned by the customer; The corresponding logistics personnel can be set as the target logistics personnel; Based on the preset door-to-door pickup task generation module, a door-to-door pickup task is generated according to the door-to-door pickup time period, pickup address and target work schedule; The door-to-door pickup task is sent to the target logistics personnel; The process of determining a target work schedule from the work schedule that facilitates the logistics personnel's passage through the pickup address to pick up the target goods returned by the customer during the pickup time period includes: Within the logistics personnel distribution map, a target circle is drawn with the line connecting the starting position and the ending position of any of the work trips as its diameter; If the pickup address is within the target circle, the corresponding work schedule will be selected as the work schedule to be selected. Within the logistics personnel distribution map, the latest arrival time of the previous work trip of the candidate work trip is used as the departure time to predict the first arrival time of the logistics personnel from the starting position of the candidate work trip to the pickup address. At the same time, the first arrival time is used as the departure time to predict the second arrival time of the logistics personnel from the pickup address to the end position of the candidate work trip. If the first arrival time falls within the door-to-door pickup time period and the second arrival time is before the latest arrival time of the candidate work schedule, the corresponding candidate work schedule will be used as the target work schedule.

2. The online retail order management and scheduling method as described in claim 1, characterized in that, Step S1: Obtain new order information for the target products that the merchant needs to fulfill on the online retail platform, including: Obtain multiple products for sale from a merchant's online store on an online retail platform; Obtain the merchant's pre-set product dropshipping contract; Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale. Obtain the order information for unshipped goods from the online store; Determine the new order information for the target product from the unshipped product order information.

3. The online retail order management and scheduling method as described in claim 1, characterized in that, Step S3: Based on the new order information and the warehouse distribution information, perform warehouse dispatch scheduling, including: The warehouse corresponding to the remaining quantity of goods that is greater than or equal to the quantity of the goods shall be designated as the target warehouse; Determine the shipping logistics route between the warehouse address corresponding to the target warehouse and the customer address from the preset shipping logistics route database; The target warehouse corresponding to the shortest shipping logistics route shall be used as the shipping warehouse; Based on a preset order picking and drop shipping task generation template, an order picking and drop shipping task is generated according to the customer address and quantity of the target product. The picking and dispatching task is delivered to the preset task dispatch node corresponding to the shipping warehouse.

4. The online retail order management and scheduling method as described in claim 1, characterized in that, Before step S2: obtaining the preset warehouse distribution information corresponding to the target product, the method further includes: The system acquires multiple operational behaviors generated by customers who purchase the target product within a preset time period after purchasing the target product on the online retail platform. Based on the aforementioned operational behavior, predict whether the customer will cancel the purchase of the target product; If so, the new order information corresponding to the customer will be temporarily suspended; If the customer does not cancel the purchase of the target product within the preset waiting time, the new order information of the customer will be temporarily suspended.

5. The online retail order management and scheduling method as described in claim 4, characterized in that, Based on the aforementioned operational behavior, predicting whether the customer will cancel the purchase of the target product includes: Obtain sales information of the target product sold by the merchant on the online retail platform; Based on the preset prediction trigger condition generation module, prediction trigger conditions are generated according to the sales information; Establish a timeline, and place the operation on the timeline based on the time when the operation occurs; Determine whether the operation meets the predicted triggering condition; If so, the corresponding operation behavior shall be taken as the target operation behavior; The operation behavior between the first and last target operation behavior on the timeline is used as the basis for prediction. Based on a preset feature extraction template, features are extracted from the prediction basis to obtain multiple feature values; Based on the aforementioned feature values, it is predicted whether the customer will cancel their purchase of the target product.

6. The online retail order management and scheduling method as described in claim 5, characterized in that, The step of predicting whether the customer will cancel the purchase of the target product based on the feature value includes: The feature values ​​are input into a preset cancellation prediction model to predict whether the customer will cancel the purchase of the target product; And / or, Based on the feature values, a first description vector for the prediction basis is constructed; Obtain a preset cancellation prediction library, which includes: multiple sets of one-to-one corresponding second description vectors and vector matching degree requirements; Perform vector matching between the first description vector and any of the second description vectors to obtain the vector matching degree; If the vector matching degree meets the vector matching degree requirement corresponding to the second description vector for matching, it is predicted that the customer will cancel the purchase of the target product.

7. A system for implementing the online retail order management and scheduling method as described in claim 1, characterized in that, include: The first acquisition module is used to acquire new order information for the target products that merchants need to dropship on the online retail platform; The second acquisition module is used to acquire preset warehouse distribution information corresponding to the target product; The scheduling module is used to perform warehouse dispatch scheduling based on the new order information and the warehouse distribution information; The new order information includes: customer address and quantity of goods; The warehouse distribution information includes: multiple sets of one-to-one corresponding warehouses, warehouse addresses, and remaining inventory.

8. The system of the online retail order management and scheduling method as described in claim 7, characterized in that, The first acquisition module acquires new order information for the target products that the merchant needs to fulfill on the online retail platform, including: Obtain multiple products for sale from a merchant's online store on an online retail platform; Obtain the merchant's pre-set product dropshipping contract; Based on the aforementioned product dropshipping contract, the target products to be dropshipped are determined from the products on sale. Obtain the order information for unshipped goods from the online store; Determine the new order information for the target product from the unshipped product order information.

Images