Picking task generation method, picking task generation device and electronic device

Abstract

Embodiments of the present disclosure disclose a picking task generation method, a picking task generation device and an electronic device. A specific implementation of the method comprises: performing order grouping processing on candidate orders to be sorted according to item information in the candidate orders, to obtain a group order; determining a bin location of a temporary storage rack corresponding to the group order according to the storage location of each item in the group order, wherein the items in the same group order are placed in the same bin after being picked, and the temporary storage rack is arranged at one end of a storage area aisle; and grouping the orders in the group order according to a grouping strategy to generate a picking task. This implementation is related to warehouse logistics technology, and a temporary storage rack is set as a fixed picking container to replace a movable picking container. Thus, the need to frequently bind and unbind containers during the picking process is avoided, and the picking personnel also need to carry containers for picking, which increases the burden on the picking personnel and affects the picking efficiency.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of warehousing and logistics technology, specifically to a picking task generation method, a picking task generation device, and an electronic device. Background Technology

[0002] In the outbound process of a WMS (Warehouse Management System), a key step is to break down the goods in an order into picking tasks based on factors such as order type, special order identifier, product batch, storage area type, order timeliness, and flow direction.

[0003] However, the inventors discovered that existing picking processes often require the use of temporary picking containers to hold all the items for a picking task. This means that each time a picking task is assigned, it needs to be linked to a picking container, and the container must be carried around while picking. For short aisles, this frequent linking and unlinking of containers impacts picking efficiency. Furthermore, the picking containers increase the workload for picking personnel.

[0004] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0006] Some embodiments of this disclosure provide picking task generation methods, picking task generation apparatuses, electronic devices, computer-readable media, and computer program products to solve one or more of the technical problems mentioned in the background section above.

[0007] In a first aspect, some embodiments of this disclosure provide a picking task generation method, including: performing order aggregation processing on candidate orders to be sorted according to item information to obtain a collection order; determining the grid position of the temporary storage rack corresponding to the collection order according to the storage position of each item in the collection order, wherein items in the same collection order are placed in the same grid after picking, and the temporary storage rack is set at one end of the storage area aisle; and grouping the orders in the collection order according to the order grouping strategy to generate a picking task.

[0008] In some embodiments, the candidate orders are aggregated based on the item information in the candidate orders to be sorted, including: determining the order type of the candidate order based on the number of items in the candidate order, wherein the order type includes single-item orders and multi-item orders; and aggregating the candidate orders according to the order type, wherein different order types correspond to different aggregating methods.

[0009] In some embodiments, the candidate orders are aggregated according to the order type, including: in response to determining that a candidate order is a single-item order, the candidate order is aggregated with other single-item orders.

[0010] In some embodiments, the process of aggregating candidate orders according to order type further includes: in response to determining that a candidate order is a multi-item order, aggregating the candidate orders as a single order.

[0011] In some embodiments, grouping orders in a collection order according to a grouping strategy includes: grouping collection orders that match the temporary storage racks according to the temporary storage racks corresponding to each collection order; and generating picking tasks according to the orders in each collection order in the same group.

[0012] In some embodiments, the method further includes: for each picking task of a multi-item order, determining whether there is a matching item in each picking task; and in response to determining that there is a matching item, generating a combined picking task based on each picking task in which the item is located.

[0013] In some embodiments, the method further includes: selecting candidate orders from the order pool to be sorted according to the target storage area corresponding to the temporary storage rack, wherein the candidate orders are orders whose item storage location is located in the target storage area, the temporary storage rack includes multiple small compartments and at least one large compartment, and the aisle length of the target storage area is less than a set length.

[0014] Secondly, some embodiments of this disclosure provide a picking task generation apparatus, including: an order aggregation unit configured to aggregate candidate orders according to item information in the candidate orders to be sorted, to obtain an aggregate order; a grid location determination unit configured to determine the grid location of the temporary storage rack corresponding to the aggregate order according to the storage location of each item in the aggregate order, wherein items in the same aggregate order are placed in the same grid after picking, and the temporary storage rack is located at one end of the storage area aisle; and a task generation unit configured to aggregate the orders in the aggregate order according to an order grouping strategy to generate a picking task.

[0015] In some embodiments, the order aggregation unit includes: a type determination subunit configured to determine the order type of the candidate order based on the number of items in the candidate order, wherein the order type includes single-item orders and multi-item orders; and a processing subunit configured to perform order aggregation processing on the candidate orders according to the order type, wherein different order types correspond to different order aggregation processing methods.

[0016] In some embodiments, the processing subunit is further configured to perform batch processing on the candidate order and other single orders in response to determining that the candidate order is a single-item order.

[0017] In some embodiments, the processing subunit is further configured to treat the candidate order as a set order in response to determining that the candidate order is a multi-item order.

[0018] In some embodiments, the task generation unit is further configured to group the collection orders that match the temporary storage racks according to the temporary storage racks corresponding to each collection order; and generate picking tasks according to the orders in each collection order in the same group.

[0019] In some embodiments, the picking task generation apparatus further includes a task combination unit configured to determine, for each picking task of a multi-item order, whether there is a matching item in each picking task; and in response to determining that there is a matching item, to generate a combined picking task based on each picking task in which the item is located.

[0020] In some embodiments, the picking task generation device further includes an order filtering unit configured to filter candidate orders from the order pool to be sorted according to the target storage area corresponding to the temporary storage rack, wherein the candidate orders are orders whose item storage location is located in the target storage area, the temporary storage rack includes multiple small compartments and at least one large compartment, and the aisle length of the target storage area is less than a set length.

[0021] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the picking task generation method described in any of the implementations of the first aspect above.

[0022] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the picking task generation method described in any of the implementations of the first aspect above.

[0023] Fifthly, some embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements the picking task generation method described in any of the implementations of the first aspect above.

[0024] The above-described embodiments of this disclosure have the following beneficial effects: the picking task generation method of some embodiments of this disclosure helps to simplify the operation of picking personnel and improve picking efficiency. Specifically, the method of this disclosure uses fixed temporary storage racks to replace temporary picking containers and allocates picking tasks accordingly. First, candidate orders can be aggregated according to the item information in the candidate orders. That is, orders are grouped. Next, the grid position of the temporary storage rack corresponding to the aggregated order is determined according to the storage location of the items. That is, the association between the aggregated order and the temporary storage rack is established. Afterwards, orders in the aggregated order can be grouped according to the grouping strategy to generate picking tasks. In this way, items in the same aggregated order can be placed in the same grid after picking. The method of this disclosure can avoid picking personnel binding and unbinding containers, especially for short-distance aisles, which helps to improve picking efficiency. Moreover, it eliminates the need to carry picking containers, thereby reducing the burden on picking personnel and further improving picking efficiency. Attached Figure Description

[0025] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0026] Figure 1 This is a flowchart of some embodiments of the picking task generation method disclosed herein;

[0027] Figure 2A This is a schematic diagram of the layout of the temporary storage racks in the storage area;

[0028] Figure 2B These are schematic diagrams of some embodiments of the temporary storage rack;

[0029] Figure 3A These are flowcharts of some other embodiments of the picking task generation method disclosed herein;

[0030] Figure 3B These are schematic diagrams illustrating some application scenarios of the publicly disclosed method for generating picking tasks;

[0031] Figure 4 This is a schematic diagram of the structure of some embodiments of the picking task generation device disclosed herein;

[0032] Figure 5 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0033] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0034] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0035] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0036] Furthermore, the terms “a” and “a plurality” used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as “one or more”.

[0037] Figure 1 A flow 100 of some embodiments of a picking task generation method according to the present disclosure is shown. The method may include the following steps:

[0038] Step 101: Based on the item information in the candidate orders to be sorted, perform order aggregation processing on the candidate orders to obtain aggregated orders.

[0039] In some embodiments, the executing entity of the picking task generation method of this disclosure (such as a warehouse management system (WMS)) can communicate with other electronic devices (such as picking terminals, verification terminals, etc.) via wired or wireless connections. Here, after receiving candidate orders to be picked, the executing entity can aggregate these candidate orders based on the item information in these orders, thereby obtaining at least one aggregated order. The candidate orders can be set according to actual needs, such as orders with a designated delivery address in a specified region, or orders with a designated user as the recipient.

[0040] It is understood that the embodiments of this disclosure primarily utilize temporary storage racks to replace temporary picking containers. Therefore, orders suitable for using temporary storage racks can be selected as candidate orders based on the positional relationship between the racks and the storage area. Specifically, the executing entity can select candidate orders from the pool of orders to be picked based on the target storage area corresponding to the temporary storage rack. Candidate orders are those whose item storage location is within the target storage area. The layout of the temporary storage racks can be configured according to actual needs, such as based on the order volume and the number of picking personnel in the warehouse. Figure 2A As shown, temporary storage racks can be placed at one end of the storage area aisle. Furthermore, to reduce the number of temporary storage racks and minimize space usage, they can be placed between two aisles, allowing two aisles to share a single temporary storage rack.

[0041] In some embodiments, the executing entity can aggregate candidate orders based on the item attributes in the order, such as storage location, item identifier SKU (Stock Keeping Unit), and / or quantity. For example, the executing entity can aggregate candidate orders whose storage locations are in the same aisle as a single order.

[0042] Optionally, the executing entity can determine the order type of a candidate order based on the number of items in the candidate order. The order type can include, but is not limited to, single-item orders and multi-item orders. Then, the candidate orders are aggregated according to the order type. Different order types require different aggregation processing methods. See [link to relevant documentation] for details. Figure 3A The relevant descriptions in the embodiments will not be repeated here.

[0043] In some embodiments, the executing entity can also split candidate orders into single-category orders and multi-category orders based on the number (category) of item identifiers in the order. A single-category order typically contains only one type of item identifier, and the quantity of the same item is not limited. A multi-category order typically contains at least two types of item identifiers, i.e., at least two different items. Then, the executing entity can aggregate these single-category orders based on constraints such as the number of items, quantity, volume, and weight of the aggregated order. For multi-category orders, a single multi-category order can be treated as a single aggregated order.

[0044] Step 102: Determine the slot location of the temporary storage rack corresponding to the collection list based on the storage location of each item in the collection list.

[0045] In some embodiments, the executing entity can determine the location of the storage rack corresponding to the collection order based on the storage location of each item in the collection order. Here, to improve the utilization rate of the storage rack and reduce the number of storage racks, one collection order typically corresponds to one storage rack. That is, items in the same collection order will be placed in the same storage rack after picking. Furthermore, the storage rack generally includes multiple small storage racks and at least one large storage rack. Figure 2B As shown, calculations show that a typical temporary storage rack can be set with 16 small compartments and 2 large compartments to meet the requirements of the warehouse.

[0046] In some embodiments, the executing entity can determine the aisle of the storage area where the items in the collection order are located based on their storage location. Then, based on the correspondence between aisles and temporary storage racks, the temporary storage rack corresponding to the collection order can be determined. Next, the location of the current collection order's corresponding grid can be determined based on the occupancy status of each grid cell in the temporary storage rack.

[0047] Optionally, when items in a collection order are located in different lanes, the executing entity can determine the lane corresponding to the collection order based on the proportion of items in each lane. For example, the lane containing the majority of items is the lane corresponding to that collection order. Alternatively, the executing entity can also determine the temporary storage rack corresponding to that collection order based on the usage of the temporary storage racks corresponding to these lanes.

[0048] Understandably, when there are no available slots on the corresponding temporary storage rack, an adjacent temporary storage rack can be used. Alternatively, it can be determined whether there are any batch orders that are about to be completed among the batch orders associated with this temporary storage rack. If so, it means that a slot will soon become available. In this case, the slot corresponding to the batch order that is about to be completed can be designated as the slot corresponding to the current batch order, and a prompt message can be generated or the picking task for this batch order can be delayed.

[0049] Step 103: Group the orders in the collection order according to the grouping strategy to generate picking tasks.

[0050] In some embodiments, the executing entity can group orders in a batch order according to a grouping strategy to generate picking tasks. For example, orders in a batch order can be grouped according to conditions such as the number of items, number of pieces, volume, and weight of the task order.

[0051] It should be noted that for storage areas with long aisles, picking personnel often need to spend a certain amount of time traveling back and forth. Furthermore, to maximize work efficiency, picking personnel typically need to pick a large number of items at a time. In this case, temporary picking containers are often needed to assist picking personnel in completing the picking task. However, for storage areas with short aisles, picking personnel can travel back and forth frequently and quickly, and may not need to pick too many items at a time. In this case, frequently binding and unbinding temporary picking containers increases unnecessary operations, increases picking time, and affects picking efficiency. Therefore, the method of this disclosure embodiment is applicable to application scenarios with relatively short aisles. Fixed temporary storage racks are used to replace temporary picking containers. That is, in this disclosure embodiment, the aisle length of the target storage area is typically less than a set length, such as 5-10 meters.

[0052] As described above, the picking task generation method of some embodiments of this disclosure helps simplify the operation of picking personnel and improve picking efficiency. Specifically, the method of this disclosure uses fixed temporary storage racks instead of temporary picking containers to allocate picking tasks. First, candidate orders can be aggregated according to the item information in the candidate orders. That is, orders are grouped. Next, the grid position of the temporary storage rack corresponding to the aggregated order is determined according to the storage location of the items. That is, the association between the aggregated order and the temporary storage rack is established. Afterwards, orders in the aggregated order can be grouped according to the grouping strategy to generate picking tasks. In this way, items in the same aggregated order can be placed in the same grid after picking. The method of this disclosure can avoid picking personnel binding and unbinding containers, especially for short-distance aisles, which helps to improve picking efficiency. Moreover, it eliminates the need to carry picking containers, thereby reducing the burden on picking personnel and further improving picking efficiency.

[0053] See also Figure 3A The diagram illustrates flowcharts of other embodiments of the picking task generation method of this disclosure. The method may include the following steps:

[0054] Step S01: The system determines whether a temporary storage rack exists in the logical area (i.e., the storage area) where the order is located. If a temporary storage rack exists, the order is marked with a special identifier. Based on this special identifier, this type of order is assigned a separate picking task.

[0055] Step S02: Calculate the order type based on the number of items in the order.

[0056] Step S03: Determine if it is a single order. If it is a single order, continue to S04; otherwise, proceed to S07.

[0057] Step S04: Split the bundled orders into multiple bundled orders based on the number of items, pieces, volume, and weight restrictions. In other words, if a candidate order is determined to be a single-item order, it can be bundled with other single-item orders.

[0058] Step S05: In each collection order, add up the order details in sequence, and continue to split the task orders according to the number of items, number of pieces, volume and weight restrictions of the task.

[0059] Step S06: Determine if the number of orders exceeds the expected number of single items. If the number exceeds the expected number, the process ends, or the process ends after all orders are allocated.

[0060] Step S07: For multiple orders, the task is split according to the rule that each order constitutes a set order. That is, if a candidate order is determined to be a multiple order, the candidate order can be treated as a set order. First, obtain the basic data of the warehouse, that is, the relationship diagram between aisles and temporary storage racks, and associate the location details of the multiple orders with the temporary storage racks based on the located aisles.

[0061] Step S08: Group the location details according to the temporary storage racks, and each group of temporary storage racks constitutes a picking task. That is, for multiple orders in a collection, collections with matching (e.g., identical) temporary storage racks can be grouped together. Picking tasks are then generated based on the orders within each collection in the same group.

[0062] It's understandable that if different batch orders correspond to the same temporary rack, it means the items in these batch orders are stored in similar locations, such as in the same or adjacent aisles. Grouping items by temporary rack to generate picking tasks allows for picking multiple batch orders together, improving overall picking efficiency.

[0063] Step S09: Finally, group picking orders with the same product and storage location into a combined task. That is, for each picking task of a multi-item order, it can be further determined whether there are matching items in each picking task. If matching items are found, a combined picking task can be generated based on the picking tasks containing that item. For example... Figure 3B As shown, both Order 1 and Order 6 contain product A located at storage location C. At this point, the corresponding picking orders can be generated into a combined picking order A. That is, based on the picking products and storage locations, the same product and the same storage location can be combined into a "hot-selling product" task. This way, multiple orders can be picked in one picking operation, thus reducing the number of picking operations.

[0064] As described above, a single-item order (SKU) is a single product. Such orders can share a large storage slot, minimizing the number of slots required. Task allocation strategy: Multiple single-item orders form a single-item collection order, which occupies one large storage slot. Orders are broken down into picking tasks based on factors such as order type, order identifier, product batch, storage area type, order timeliness, and flow direction. Pickers can pick multiple orders' worth of goods in one go using a specific path, and then redistribute the goods to individual orders during review. For multi-item orders, each order forms a single collection order, which occupies one small storage slot. This is equivalent to pre-sorting, avoiding the process of merging orders during the picking stage and then resorting them during the review stage, thus reducing operational steps.

[0065] It should be noted that in existing task allocation processes, order pools are typically first split according to production type. Within each order pool, it is further divided into N independent order pools based on specific independent group order identifiers (pre-sale, cosmetics, city delivery, special delivery, etc.). That is, for multiple items in the same order, if these items are located in different storage areas, the order is usually split, resulting in multiple sub-orders. Thus, the same order is split into multiple packages for shipment. The candidate orders in this embodiment are typically orders (or sub-orders) that have undergone the above processing.

[0066] Further reference Figure 4 As a response to the above Figure 1-3B The present disclosure provides some embodiments of a picking task generation apparatus to implement the method shown. These apparatus embodiments are similar to... Figure 1-3B The methods and embodiments shown correspond to those described. This device can be specifically applied to various electronic devices.

[0067] like Figure 4 As shown, the picking task generation device 400 in some embodiments may include: an order aggregation unit 401, configured to aggregate candidate orders according to item information in the candidate orders to be sorted, to obtain an aggregate order; a grid location determination unit 402, configured to determine the grid location of the temporary storage rack corresponding to the aggregate order according to the storage location of each item in the aggregate order, wherein items in the same aggregate order are placed in the same grid after picking, and the temporary storage rack is set at one end of the storage area aisle; and a task generation unit 403, configured to aggregate orders in the aggregate order according to an order grouping strategy to generate picking tasks.

[0068] In some embodiments, the order aggregation unit 401 may include: a type determination subunit (not shown in the figure), configured to determine the order type of the candidate order based on the number of items in the candidate order, wherein the order type includes single-item orders and multi-item orders; and a processing subunit (not shown in the figure), configured to perform order aggregation processing on the candidate orders according to the order type, wherein different order types correspond to different order aggregation processing methods.

[0069] In some embodiments, the processing subunit may be further configured to combine the candidate order with other single orders in response to determining that the candidate order is a single-item order.

[0070] In some embodiments, the processing subunit may be further configured to treat the candidate order as a set order in response to determining that the candidate order is a multi-item order.

[0071] In some embodiments, the task generation unit 403 may be further configured to group the collection orders that match the temporary storage racks according to the temporary storage racks corresponding to each collection order; and generate picking tasks according to the orders in each collection order in the same group.

[0072] In some embodiments, the picking task generation apparatus 400 may further include a task combination unit (not shown) configured to determine, for each picking task of a multi-item order, whether there is a matching item in each picking task; and in response to determining that there is a matching item, to generate a combined picking task based on each picking task in which the item is located.

[0073] In some embodiments, the picking task generation device 400 may further include an order filtering unit (not shown in the figure), configured to filter candidate orders from the order pool to be sorted according to the target storage area corresponding to the temporary storage rack, wherein the candidate orders are orders whose item storage location is located in the target storage area, the temporary storage rack includes a plurality of small compartments and at least one large compartment, and the aisle length of the target storage area is less than a set length.

[0074] It is understandable that the units described in the picking task generation device 400 are related to the reference. Figure 1-3B The steps in the described method correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the picking task generation device 400 and the units contained therein, and will not be repeated here.

[0075] The following is for reference. Figure 5 It shows a schematic diagram of the structure of an electronic device 500 suitable for implementing some embodiments of the present disclosure. Figure 5 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0076] like Figure 5 As shown, the electronic device 500 may include a processing unit 501 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0077] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, speakers, vibrators, etc.; storage devices 508 including, for example, hard disks, disks, memory cards, etc.; and communication devices 509. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 An electronic device 500 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 5 Each box shown can represent a device or multiple devices as needed.

[0078] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, it performs the functions defined in the methods of some embodiments of this disclosure.

[0079] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0080] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and end-to-end networks (e.g., ad hoc end-to-end networks), as well as any currently known or future-developed networks.

[0081] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device. The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to: perform order aggregation processing on the candidate orders to be sorted according to the item information in the candidate orders to obtain a aggregate order; determine the slot position of the temporary storage rack corresponding to the aggregate order according to the storage location of each item in the aggregate order, wherein items in the same aggregate order are placed in the same slot after picking, and the temporary storage rack is set at one end of the storage area aisle; and aggregate the orders in the aggregate order according to the order aggregation strategy to generate a picking task.

[0082] Furthermore, computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​and conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0083] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0084] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor may be described as including a collection unit, a grid determination unit, and a task generation unit. The names of these units do not necessarily limit the specific unit; for example, a collection unit may also be described as a "unit that performs collection processing on candidate orders."

[0085] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0086] Some embodiments of this disclosure also provide a computer program product, including a computer program that, when executed by a processor, implements any of the picking task generation methods described above.

[0087] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A method for generating picking tasks, comprising: Based on the item information in the candidate orders to be sorted, the candidate orders are aggregated to obtain aggregated orders; Based on the storage location of each item in the collection list, the location of the slot of the temporary storage rack corresponding to the collection list is determined. Items in the same collection list are placed in the same slot after being picked. The temporary storage rack is set at one end of the storage area aisle. The orders in the aggregate order are grouped according to the grouping strategy to generate picking tasks.

2. The picking task generation method according to claim 1, wherein, The step of performing order aggregation processing on the candidate orders based on the item information in the candidate orders to be sorted includes: The order type of the candidate order is determined based on the number of items in the candidate order, wherein the order type includes single-item orders and multi-item orders; The candidate orders are aggregated according to their order types, with different aggregation processing methods for different order types.

3. The picking task generation method according to claim 2, wherein, The step of aggregating the candidate orders according to order type includes: In response to determining that the candidate order is a single-item order, the candidate order is combined with other single-item orders for order aggregation processing.

4. The picking task generation method according to claim 2, wherein, The step of aggregating the candidate orders according to order type further includes: In response to determining that the candidate order is a multi-item order, the candidate order is treated as a single aggregate order.

5. The picking task generation method according to claim 4, wherein, The step of grouping orders in the aggregated order according to the grouping strategy includes: Based on the temporary storage rack corresponding to each collection order, the collection orders that match the temporary storage racks are grouped together; Picking tasks are generated based on the orders in each collection of orders within the same group.

6. The picking task generation method according to claim 4, wherein, The method further includes: For each picking task of a multi-item order, determine whether there are matching items in each picking task; In response to the determination that a matching item exists, a combined picking task is generated based on the picking tasks to which the item belongs.

7. The picking task allocation method according to any one of claims 1-6, wherein, The method further includes: Based on the target storage area corresponding to the temporary storage rack, candidate orders are selected from the order pool to be sorted. The candidate orders are those whose item storage location is located in the target storage area. The temporary storage rack includes multiple small compartments and at least one large compartment, and the aisle length of the target storage area is less than a set length.

8. A picking task generation device, comprising: The order collection unit is configured to perform order collection processing on the candidate orders to be sorted based on the item information in the candidate orders to be sorted, so as to obtain a collection order; The compartment determination unit is configured to determine the compartment position of the temporary storage rack corresponding to the collection order based on the storage position of each item in the collection order, wherein items in the same collection order are placed in the same compartment after being picked, and the temporary storage rack is located at one end of the storage area aisle. The task generation unit is configured to group the orders in the aggregate order according to the grouping strategy to generate picking tasks.

9. An electronic device, comprising: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors implement the picking task generation method as described in any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, wherein, When the computer program is executed by the processor, it implements the picking task generation method as described in any one of claims 1-7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the picking task generation method as described in any one of claims 1-7.

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