Picking task issuing method and device

Abstract

The invention discloses a picking task issuing method and device, and relates to the technical field of intelligent logistics. One specific embodiment of the method comprises the following steps: obtaining the production quantity of articles in a warehouse in a future period, and determining at least one article with the maximum production quantity in the future period as a target article of the warehouse; determining at least one target order structure containing the target article; and for to-be-processed orders with the same target order structure in the order pool of the warehouse, generating a centralized order picking task according to the current to-be-processed order in the order pool and issuing the centralized order picking task when it is judged that a centralized order picking condition preset for the target order structure is met. According to the embodiment, the warehouse production efficiency can be improved.

Description

Technical Field

[0001] The present invention relates to the field of smart logistics technology, and in particular to a method and device for issuing picking tasks. Background Art

[0002] In the current warehouse operation process, after the user order is submitted, it is generally split into multiple picking orders based on the warehouse where the items in the order are located, and enters the order pool of the corresponding warehouse. Then the WMS (Warehouse Management System) combines the current picking orders in the order pool in real time to generate picking tasks and sends them to the pickers. After the picking is completed, it enters the review and packaging and delivery stages in turn. After the items are shipped out, the warehouse production process is completed.

[0003] In actual scenarios, warehouses often have target items with large production quantities or target order structures with large order quantities during their production cycle. If the target items or target order structures can be centrally produced in some way (such as centralized picking, centralized review, centralized packaging) instead of decentralized production, production efficiency will be significantly improved. However, the existing process does not take into account the above characteristics of the target items or target order structures. Summary of the invention

[0004] In view of this, an embodiment of the present invention provides a method and device for issuing picking tasks, which can execute centralized production according to a target order structure, thereby improving warehouse production efficiency.

[0005] To achieve the above objective, according to one aspect of the present invention, a method for issuing picking tasks is provided.

[0006] The method for issuing picking tasks in an embodiment of the present invention includes: obtaining the production quantity of items in a warehouse in a future period, determining at least one item with the largest production quantity in the future period as the target item of the warehouse; determining at least one target order structure containing the target item; for pending orders with the same target order structure in an order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met, generating and issuing centralized picking tasks based on the current pending orders in the order pool.

[0007] Optionally, obtaining the production quantity of the items in the warehouse in the future period includes: determining the production quantity of the items in the future period according to the production quantity of the items in the historical period.

[0008] Optionally, determining at least one target order structure including the target item includes: determining a target order structure including only the target item for each target item, and the number of items included in each target order structure is 1.

[0009] Optionally, the centralized picking condition preset for any target order structure includes: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset first duration.

[0010] Optionally, the centralized picking condition preset for any target order structure includes: the number of pending orders currently having the target order structure in the order pool reaches a preset first number.

[0011] To achieve the above objective, according to another aspect of the present invention, a method for issuing picking tasks is provided.

[0012] The method for issuing picking tasks in an embodiment of the present invention includes: obtaining the order quantity of each order structure in the warehouse in the future period, and determining at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse; for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the preset centralized picking conditions of the target order structure are met, generating and issuing centralized picking tasks according to the current pending orders in the order pool.

[0013] Optionally, obtaining the order quantity of each order structure in the warehouse in the future period includes: determining the order quantity of the order structure in the future period according to the order quantity of the order structure in a historical period.

[0014] Optionally, the order structure includes: a single-item order structure containing one item, and the item is 1 or more pieces, and a multi-item order structure containing multiple items, and any one of the items is 1 or more pieces.

[0015] Optionally, the centralized picking condition preset for any target order structure includes: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset second time length.

[0016] Optionally, the centralized picking condition preset for any target order structure includes: the number of pending orders currently having the target order structure in the order pool reaches a preset second number.

[0017] To achieve the above objective, according to another aspect of the present invention, a device for issuing picking tasks is provided.

[0018] The picking task issuing device of the embodiment of the present invention includes: a prediction unit, used to obtain the production quantity of items in the warehouse in the future period, and determine at least one item with the largest production quantity in the future period as the target item of the warehouse; an order structure determination unit, used to determine at least one target order structure containing the target item; a centralized picking decision unit, used to generate and issue centralized picking tasks according to the current pending orders in the order pool for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met.

[0019] To achieve the above objective, according to another aspect of the present invention, a device for issuing picking tasks is provided.

[0020] The picking task issuing device of the embodiment of the present invention includes: an order quantity prediction unit, which is used to obtain the order quantity of each order structure in the warehouse in the future period, and determine at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse; a picking decision unit, which is used to generate and issue centralized picking tasks according to the current pending orders in the order pool for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the preset centralized picking conditions for the target order structure are met.

[0021] To achieve the above objective, according to another aspect of the present invention, an electronic device is provided.

[0022] An electronic device of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the picking task dispatching method provided by the present invention.

[0023] To achieve the above objective, according to yet another aspect of the present invention, a computer-readable storage medium is provided.

[0024] A computer-readable storage medium of the present invention stores a computer program, and when the program is executed by a processor, the method for issuing picking tasks provided by the present invention is implemented.

[0025] According to the technical solution of the present invention, the embodiments of the above invention have the following advantages or beneficial effects: The server determines a target order structure with a larger order quantity in the future period based on the warehouse's historical production data, or first determines the target items with a larger production quantity in the future period and then determines the target order structure containing the target items. After that, the server does not generate picking tasks in real time for pending orders with the target order structure in the order pool and issue them to execute picking. Instead, the order quantity is accumulated until the centralized picking conditions are met. For example, the order quantity reaches a threshold or the current time reaches a preset time. Only then will the server generate and issue centralized picking tasks based on pending orders with the same target order structure in the order pool to achieve centralized production of the same target order structure (for example, centralized picking, centralized review, and centralized packaging). This significantly improves warehouse production efficiency by leveraging the advantages of big data prediction and centralized production corresponding to a single order structure.

[0026] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with the specific implementation manner. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention. Figure 1 It is a schematic diagram of the main steps of the method for issuing picking tasks in the first embodiment of the present invention; Figure 2 This is a schematic diagram of specific execution steps of the method for issuing picking tasks in the first embodiment of the present invention; Figure 3 This is a schematic diagram of the main steps of the method for issuing picking tasks in the second embodiment of the present invention; Figure 4 This is a schematic diagram of specific execution steps of the method for issuing picking tasks in the second embodiment of the present invention; Figure 5 It is a schematic diagram of the components of the picking task issuing device in the third embodiment of the present invention; Figure 6 It is a schematic diagram of the components of the picking task issuing device in the fourth embodiment of the present invention; Figure 7 is an exemplary system architecture diagram in which embodiments of the present invention may be applied; Figure 8 It is a schematic diagram of the structure of an electronic device used to implement the method for issuing picking tasks in an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0029] In the existing warehouse operation process, after a user order is submitted, the user order can be split into multiple picking orders based on the warehouse where the items in the order are located (i.e., the warehouse used to produce the items), and each picking order corresponds to the same warehouse. Of course, if the items in a user order are produced by the same warehouse, the user order can be directly used as a picking order without going through the above splitting process. The order in the following description refers to the picking order. After the picking order is formed, it enters the order pool of the corresponding warehouse. In the picking task allocation stage, the WMS combines one or more orders in turn according to the current order situation in the order pool to generate a picking task and issue it to the picker. Generally speaking, after the picking order enters the order pool, the WMS will match other picking orders in a short time to generate and issue the picking task. The picker can be a picking staff member or a picking device such as an AGV (Automated Guided Vehicle). After the picking stage is completed, it enters the review stage, and the reviewer checks the items of each order. The basis of the review stage is the order data (including the items and quantity in the order) transmitted to the reviewer in advance. Finally, the packaging and delivery phase is entered. After the items are shipped out of the warehouse, the entire warehouse production process is completed. Hereinafter, this production mode of generating picking tasks in the order in which the orders enter the order pool and issuing them to perform picking, review, packaging and delivery is referred to as the existing production mode, and the production mode proposed in the embodiment of the present invention, which is opposite to the existing production model, is referred to as the centralized production mode.

[0030] The actual warehouse production process involves the concept of waves. A wave corresponds to the same cut-off time for order production. For example, a warehouse has four waves with cut-off times of 9:00, 12:00, 18:00, and 21:00. The actual latest delivery time of an order belonging to a wave is the cut-off time of the wave. For example, the wave to which an order belongs can be determined by calculating its theoretical latest delivery time and comparing the theoretical latest delivery time with the cut-off times of each wave of the warehouse.

[0031] In actual scenarios, warehouses often have a target item with a large production quantity or a target order structure with a large order quantity during their production cycle. If the target items or target order structure can be produced in a centralized manner (such as centralized picking, centralized review, centralized packaging) instead of decentralized production, production efficiency will be significantly improved. However, the existing process does not take into account the above characteristics of the target items or target order structure. This application solves the above problems existing in the prior art through the following description. It should be pointed out that the embodiments of the present invention and the technical features in the embodiments can be combined with each other without conflict.

[0032] Figure 1 Schematic diagram of the main steps of the method for issuing picking tasks in the first embodiment of the present invention. The method for issuing picking tasks can be executed by the WMS system or the computing device or computing module (hereinafter collectively referred to as the system) included in the WMS system. Figure 1 As shown, the specific execution steps are as follows: Step S101: Obtain the production quantity of items in the warehouse in the future period, and determine at least one item with the largest production quantity in the future period as the target item of the warehouse.

[0033] In this step, the system can call the big data prediction module to obtain the prediction result, that is, the production quantity of the items in the warehouse in the future period. The big data prediction module can pre-execute the prediction based on the known time series prediction model (such as the pre-trained long short-term memory network LSTM) or the time series prediction algorithm and the warehouse production data of the historical period. Of course, the system can also perform the above prediction by itself, that is, determine the production quantity of the items in the future period according to the production quantity of the items in the warehouse in the historical period. The prediction process can consider the production quantity of the items in the daily period and the production quantity of the items in the special period (such as the promotion period). It should be noted that the above future period and historical period are relative to the execution time of the action of "obtaining the production quantity of the items in the warehouse in the future period". Any time period earlier than the above execution time can be used as the above historical period, and any time period later than the above execution time can be used as the above future period. As a preferred solution, the above future period refers to the future time period for executing the centralized production mode. For example, the current time is 5 o'clock, and the system plans to execute the centralized production model from 7 to 12 o'clock and 14 to 21 o'clock on the same day. In this case, the two time periods from 7 to 12 o'clock on the same day and 14 to 21 o'clock on the same day can be used as future periods. The production quantity of an item refers to the number of items processed by the warehouse, which can be the number of items included in an order, the number of items picked, or the number of items shipped out of the warehouse. In one embodiment, the system can predict the production quantity of items for the entire day based on the production quantity of items in the past few days, or predict the production quantity of items in the relevant time periods of each wave of the day based on the production quantity of items in the past few days, or predict the production quantity of items in the future time periods of the day based on the actual production quantity of items that have been produced on the day. Optionally, the system determines the target items by arranging the items in descending order according to the production quantity, and taking the preset number of items in front as the target items.

[0034] Step S102: Determine at least one target order structure including a target item.

[0035] In an embodiment of the present invention, an order structure includes one or more fixed items and a fixed quantity of each item, that is, if two orders contain exactly the same items (an item can be uniquely identified by a stock keeping unit SKU) and the quantity of each item in the order is exactly the same, then the two orders have the same order structure; conversely, if the two orders contain different items or contain the same items but different quantities of items, then the order structures of the two orders are different. For example, in the table below, Order 1 and Order 2 contain the same items and the same quantity of items, so they have the same order structure; Order 1 and Order 3 contain different items, so their order structures are different; Order 1 and Order 4 contain the same items but contain different quantities of SKU-b, so their order structures are also different.

[0036]

[0037] The order structure can be divided into a single-item order structure and a multi-item order structure. A single-item order structure contains only one item, while a multi-item order structure contains multiple items.

[0038] In this step, the system may determine one or more target order structures based on the previously determined target items. Optionally, after the system determines multiple items (e.g., 50 or 100 items) with the largest production quantity in the future period as the target items of the warehouse, it may screen or pre-process the target items before determining the target order structure. For example, the system first filters out items that are not suitable for centralized production mode according to a predetermined strategy or by manual intervention, and then selects some of the target items to determine the target order structure. Of course, the system may also determine the target order structure based on all the target items.

[0039] In actual applications, the system can determine the target order structure based on a variety of methods. For example, the system determines a target order structure that only contains each target item (which can be each target item after screening or preprocessing), and the number of items contained in each target order structure is 1, that is, the system determines a single-item order structure that only contains this target item and the quantity is 1 for each target item. For another example, the system determines a target order structure that only contains this target item for each target item, and the number of items contained in each target order structure is 1 or more. For another example, the system combines multiple target items to determine a multi-item order structure containing the multiple target items, and the quantity of each target item in the multi-item order structure is 1 or more.

[0040] After determining each target order structure, the system can execute a centralized production mode for each target order structure. Different from the existing production mode that generates picking tasks and executes picking in a short period of time after receiving an order in the order pool, in the centralized production mode, the system does not rush to produce picking tasks for pending orders with a target order structure in the order pool, but continuously accumulates the number of orders with the same target order structure in the order pool (this process is called order accumulation) until the centralized picking conditions preset for the corresponding target order structure are reached, and then a centralized picking task is generated and sent to the picker to execute the picking. It can be understood that the target items and target order structures determined based on big data technology are likely to correspond to a large number of orders at a future time. A large number of orders with the same structure are accumulated through the order accumulation process, thereby triggering centralized picking and subsequent centralized review, centralized packaging and other steps. Compared with the existing decentralized production model, it can significantly improve the warehouse production efficiency of the target items and target order structures. In one embodiment, the orders that execute the centralized production mode can be set to be unable to be queried in the query page corresponding to the existing production mode to avoid the order being misassigned.

[0041] In one embodiment, the system may decide whether to cancel the order accumulation for the target order structure based on the change in the number of orders with the same target order structure in the order pool, that is, decide whether to cancel the centralized production mode for the target order structure. For example, if the system determines that the growth rate of the number of orders with the target order structure is lower than the preset rate, it means that the number of orders corresponding to the target order structure is insufficient. At this time, executing the centralized production mode may not significantly improve production efficiency. At this time, the order accumulation for the target order structure can be canceled through interface operation, and production can be restored to the existing production mode. In one embodiment, for the order structure that executes the existing production mode, if the system detects that the number of corresponding orders in the order pool reaches the threshold, a reminder of "whether to switch to centralized production mode" may be issued.

[0042] Step S103: For pending orders with the same target order structure in the warehouse's order pool, when it is determined that the preset centralized picking conditions for the target order structure are met, a centralized picking task is generated and issued according to the current pending orders in the order pool.

[0043] The centralized picking conditions can be flexibly configured according to the actual scenario. For example, they can be configured as conditions based on the order quantity, that is, the centralized picking conditions preset for any target order structure include: the number of pending orders with this target order structure in the order pool currently reaches a preset first number (hereinafter referred to as the first condition); they can also be configured as time-based conditions, that is, the centralized picking conditions preset for any target order structure include: the time interval between the current moment and the nearest wave cutoff moment in the future reaches a preset first duration (hereinafter referred to as the second condition). The centralized picking conditions can also be configured as conditions involving other dimensions, such as conditions in response to external picking instructions (which may include manual picking instructions).

[0044] The following is an explanation based on the first condition and the second condition. In actual scenarios, the first condition and the second condition can be used arbitrarily or in combination. In scenarios where only the first condition is used, the system can periodically detect whether the number of pending orders with the same target order structure in the order pool has reached the first number: if it has not reached the first number, the order accumulation continues; if it has reached the first number, the system generates a centralized picking task based on the current pending orders and issues it to a small number of pickers (which can be one or more pickers) to perform the picking. The monitoring of the number of orders can prevent the accumulated number of orders from being too large and causing the generation to fail on time. In scenarios where only the second condition is used, the system detects that the time interval between the current moment and the nearest future wave deadline reaches the first duration. For example, the nearest future wave deadline is 12 o'clock, and the first duration is 1 hour, that is, the system detects that the current moment is 11 o'clock. At this time, the system generates a centralized picking task based on the current pending orders with the same target order structure and issues it to a small number of pickers to perform the picking, thereby completing the order production in sufficient time before the wave deadline.

[0045] In the scenario where the first condition and the second condition are used in combination, the system performs the detection of the number of pending orders and the current time in parallel, that is, it detects in parallel whether the number of pending orders with the same target order structure in the order pool currently reaches the first number, and whether the time interval between the current time and the deadline of the nearest wave in the future reaches the first duration. When any of the conditions is met, the system generates a centralized picking task and issues it to perform picking. If the first condition is met and the second condition is not met, the system can continue to perform the detection of the first condition and the second condition after issuing the centralized picking task; if the second condition is met, the system can end the centralized production mode of the current wave after issuing the centralized picking task, completing the centralized picking, centralized review and centralized packaging and delivery, and prepare to start the centralized production mode of the next wave. It can be understood that the centralized picking process based on the same target order structure will have a higher efficiency, and the centralized review and centralized packaging links will also have a higher efficiency due to the centralized advantages based on the same target order structure. In the next wave, the target items and target order structure of the previous wave can be used and a similar centralized production mode can be executed, or the target items and target order structure can be re-determined through prediction. Through the above order accumulation process and centralized production mode, the embodiment of the present invention can utilize the centralized production advantages corresponding to a single order structure to significantly improve the warehouse production efficiency in each link.

[0046] Figure 2 is a schematic diagram of specific execution steps of the method for issuing picking tasks in the first embodiment of the present invention, see Figure 2 . Step S201: The system obtains the production quantity of items in the warehouse in the future period based on big data prediction technology; Step S202: The system determines at least one item with the largest production quantity in the future period as the target item of the warehouse; Step S203: The system determines at least one target order structure containing the target item; Step S204: The system accumulates orders with the target order structure in the order pool; Step S205: The system monitors the number of orders with the same target order structure in the order pool; If it is determined that the order quantity reaches the first quantity, step S207 is executed; If it is determined that the order quantity does not reach the first quantity, step S206 is executed; Step S206: Monitor the time interval between the current moment and the nearest wave cut-off moment in the future; If it is determined that the time interval reaches the first duration, step S207 is executed; If it is determined that the time interval does not reach the first duration, return to step S204; Step S207: Generate and issue centralized picking tasks according to the orders with the same target order structure in the order pool.

[0047] Figure 3 This is a schematic diagram of the main steps of the method for issuing picking tasks in the second embodiment of the present invention, see Figure 3In the picking task dispatching method of this embodiment, the order structure with the largest number of orders at a future moment is directly determined as the target order structure based on the big data prediction technology, and then the order accumulation and centralized production mode for the target order structure is similar to the first embodiment. The picking task dispatching method of this embodiment includes the following steps.

[0048] Step S301: Obtain the order quantity of each order structure in the warehouse in the future period, and determine at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse.

[0049] In this embodiment, the system can determine the order quantity of the order structure in the future period according to the order quantity of the order structure in the historical period. The above order structure may include a single-item order structure containing one item, and the item is 1 or more, and may also include a multi-item order structure containing multiple items, and any of the items contained is 1 or more.

[0050] In this step, the system can call the big data prediction module to obtain the prediction result, that is, the number of orders for each order structure in the warehouse in the future period. The big data prediction module can perform the prediction in advance based on the known time series prediction model or time series prediction algorithm and the warehouse production data of the historical period. Of course, the system can also perform the above prediction by itself, that is, determine the number of orders for the order structure in the future period according to the number of orders in the historical period of the order structure in the warehouse. The prediction process can consider the number of orders in the daily period and the number of orders in the special period (such as the promotion period). In one embodiment, the system can predict the number of orders for the whole day based on the number of orders in the past few days, and can also predict the number of orders for each wave of the day based on the number of orders in the past few days. The number of orders in the future time period of the day can also be predicted based on the actual number of orders that have been generated on the day. Optionally, the system determines the target order structure in the following way: arrange each order structure in descending order according to the predicted order quantity, and use the order structure with the preset number in front as the target order structure.

[0051] Step S302: For pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the preset centralized picking conditions for the target order structure are met, a centralized picking task is generated and issued according to the current pending orders in the order pool.

[0052] In this embodiment, the centralized picking conditions can be flexibly set as needed. For example, the centralized picking conditions preset for any target order structure may include: the time interval between the current moment and the deadline of the nearest wave in the future reaches a preset second time length; the centralized picking conditions preset for any target order structure include: the number of pending orders currently having this target order structure in the order pool reaches a preset second number.

[0053] Figure 4 is a schematic diagram of specific execution steps of the method for issuing picking tasks in the second embodiment of the present invention, see Figure 4 . Step S401: The system obtains the order quantity of each order structure in the warehouse in the future period based on big data prediction technology; Step S402: The system determines at least one order structure with the largest order quantity in the future period as the target order structure; Step S403: The system accumulates orders with the target order structure in the order pool; Step S404: The system monitors the number of orders with the same target order structure in the order pool; if it is determined that the order quantity reaches the first quantity, step S406 is executed; if it is determined that the order quantity does not reach the first quantity, step S405 is executed; Step S405: Monitor the time interval between the current moment and the nearest wave deadline in the future; if it is determined that the time interval reaches the first duration, step S406 is executed; if it is determined that the time interval does not reach the first duration, return to step S403; Step S406: Generate and issue centralized picking tasks based on orders with the same target order structure in the order pool.

[0054] In the technical solution of the embodiment of the present invention, the server determines a target order structure with a larger order quantity in the future period based on the historical production data of the warehouse, or first determines the target items with a larger production quantity in the future period and then determines the target order structure containing the target items. After that, the server does not generate picking tasks in real time for the pending orders with the target order structure in the order pool and issue them to execute picking. Instead, the order quantity is accumulated until the centralized picking conditions are met. For example, the order quantity reaches a threshold or the current time reaches a preset time. Only then is the centralized picking task generated and issued based on the pending orders with the same target order structure in the order pool to achieve centralized production of the same target order structure, thereby utilizing the advantages of big data prediction and centralized production corresponding to a single order structure to significantly improve the production efficiency of the target items or target order structure.

[0055] It should be noted that the collection, collection, update, analysis, processing, use, transmission, storage and other aspects of user personal information that may be involved in the technical solution of the present invention are in compliance with the provisions of relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken for user personal information to prevent illegal access to user personal information data and maintain the security of user personal information, network security and national security.

[0056] For the above-mentioned method embodiments, for the convenience of description, they are expressed as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the order of the actions described, and some steps can actually be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary to implement the present invention.

[0057] In order to better implement the above-mentioned solution of the embodiment of the present invention, relevant devices for implementing the above-mentioned solution are also provided below.

[0058] See also Figure 5 As shown, the picking task issuing device 500 provided in the embodiment of the present invention may include: a prediction unit 501, an order structure determination unit 502 and a centralized picking decision unit 503.

[0059] Among them, the prediction unit 501 is used to obtain the production quantity of items in the warehouse in the future period, and determine at least one item with the largest production quantity in the future period as the target item of the warehouse; the order structure determination unit 502 is used to determine at least one target order structure containing the target item; the centralized picking decision unit 503 is used to generate and issue centralized picking tasks according to the current pending orders in the order pool for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met.

[0060] In the embodiment of the present invention, the prediction unit 501 may be further configured to determine the production quantity of the item in the future period according to the production quantity of the item in the historical period.

[0061] As a preferred solution, the order structure determining unit 502 may be further configured to determine, for each target item, a target order structure that only contains the target item, and the number of items contained in each target order structure is 1.

[0062] Preferably, the centralized picking conditions preset for any target order structure include: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset first duration.

[0063] In one embodiment, the centralized picking condition preset for any target order structure includes: the number of pending orders currently having the target order structure in the order pool reaches a preset first number.

[0064] See also Figure 6 As shown, the picking task issuing device 600 provided in the embodiment of the present invention may include: an order quantity prediction unit 601 and a picking decision unit 602.

[0065] Among them, the order quantity prediction unit 601 is used to obtain the order quantity of each order structure in the warehouse in the future period, and determine at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse; the picking decision unit 602 is used to generate and issue centralized picking tasks according to the current pending orders in the order pool for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the preset centralized picking conditions for the target order structure are met.

[0066] In practical applications, the order quantity prediction unit 601 may be further configured to determine the order quantity of the order structure in a future period according to the order quantity of the order structure in a historical period.

[0067] In a specific application, the order structure includes: a single-item order structure including one or more items of one item, and a multi-item order structure including multiple items, and including one or more items of any one item.

[0068] In an optional technical solution, the centralized picking conditions preset for any target order structure include: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset second time length.

[0069] In addition, in the embodiment of the present invention, the centralized picking condition preset for any target order structure includes: the number of pending orders currently having the target order structure in the order pool reaches a preset second number.

[0070] According to the technical solution of the embodiments of the present invention, the server determines a target order structure with a larger order quantity in the future period based on the historical production data of the warehouse, or first determines the target items with a larger production quantity in the future period and then determines the target order structure containing the target items. After that, the server does not generate picking tasks in real time and issue them to execute picking for the pending orders with the target order structure in the order pool, but accumulates the order quantity until the centralized picking conditions are met, such as the order quantity reaches a threshold or the current time reaches a preset time, and then generates and issues centralized picking tasks based on the pending orders with the same target order structure in the order pool to achieve centralized production of the same target order structure, thereby utilizing the advantages of big data prediction and centralized production corresponding to a single order structure to significantly improve warehouse production efficiency.

[0071] Figure 7 An exemplary system architecture 700 to which the method or device for issuing picking tasks according to the embodiments of the present invention may be applied is shown.

[0072] like Figure 7As shown, the system architecture 700 may include terminal devices 701, 702, 703, a network 704 and a server 705 (this architecture is only an example, and the components included in the specific architecture may be adjusted according to the specific application). The network 704 is used to provide a medium for a communication link between the terminal devices 701, 702, 703 and the server 705. The network 704 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0073] The user can use the terminal devices 701 , 702 , and 703 to interact with the server 705 through the network 704 to receive or send messages, etc. Various client applications, such as a picking application (only an example), can be installed on the terminal devices 701 , 702 , and 703 .

[0074] The terminal devices 701 , 702 , and 703 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, PDAs, laptop computers, desktop computers, and the like.

[0075] The server 705 may be a server that provides various services, such as a WMS server (only an example) that supports the picking application operated by the user using the terminal devices 701, 702, 703. The WMS server may send instructions (such as a generated picking request - only an example) to the terminal devices 701, 702, 703.

[0076] It should be noted that the picking task issuing method provided in the embodiment of the present invention is generally executed by the server 705 , and accordingly, the picking task issuing device is generally arranged in the server 705 .

[0077] It should be understood that Figure 7 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0078] The present invention also provides an electronic device. The electronic device of an embodiment of the present invention includes: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method for sending picking tasks provided by the present invention.

[0079] Reference below Figure 8 , which shows a schematic diagram of the structure of a computer system 800 of an electronic device suitable for implementing an embodiment of the present invention. Figure 8 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0080] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 802 or a program loaded from a storage part 808 to a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the computer system 800 are also stored. The CPU 801, the ROM 802, and the RAM 803 are connected to each other through a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0081] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0082] In particular, according to the embodiments disclosed in the present invention, the process described in the main step diagram above can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the main step diagram. In the above embodiment, the computer program can be downloaded and installed from the network through the communication part 809, and / or installed from the removable medium 811. When the computer program is executed by the central processing unit 801, the above functions defined in the system of the present invention are executed.

[0083] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than computer readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0084] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0085] The units involved in the embodiments of the present invention may be implemented by software or hardware. The units described may also be set in a processor, for example, it may be described as: a processor includes: a prediction unit, an order structure determination unit and a centralized picking decision unit. The names of these units do not constitute a limitation on the units themselves in some cases, for example, the prediction unit may also be described as "a unit that provides target items to the order structure determination unit".

[0086] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently and not be assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the device, the steps executed by the device include: obtaining the production quantity of items in the warehouse in the future period, determining at least one item with the largest production quantity in the future period as the target item of the warehouse, and determining at least one target order structure containing the target item; or obtaining the order quantity of each order structure in the warehouse in the future period, and determining at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse; for pending orders with the same target order structure in the order pool of the warehouse, if it is determined that the preset centralized picking conditions of the target order structure are met, generating and issuing centralized picking tasks according to the current pending orders in the order pool.

[0087] In the technical solution of the embodiment of the present invention, the server determines a target order structure with a larger order quantity in the future period based on the warehouse's historical production data, or first determines the target items with a larger production quantity in the future period and then determines the target order structure containing the target items. After that, the server does not generate picking tasks in real time for the pending orders with the target order structure in the order pool and issue them to execute picking. Instead, the order quantity is accumulated until the centralized picking conditions are met. For example, the order quantity reaches a threshold or the current time reaches a preset time. Only then is the centralized picking task generated and issued based on the pending orders with the same target order structure in the order pool, so as to realize the centralized production of the same target order structure, thereby utilizing the advantages of big data prediction and centralized production corresponding to a single order structure to significantly improve the warehouse production efficiency.

[0088] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for issuing picking tasks, characterized in that: include: Obtaining the production quantity of items in the warehouse in a future period, and determining at least one item with the largest production quantity in the future period as the target item of the warehouse; determining at least one target order structure including the target item; For pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met, a centralized picking task is generated and issued based on the current pending orders in the order pool.

2. The method according to claim 1, characterized in that The method of obtaining the production quantity of items in the warehouse in the future period includes: Determine the production quantity of the item in the future period based on the production quantity of the item in the historical period.

3. The method according to claim 1, characterized in that The determining of at least one target order structure containing the target item includes: A target order structure containing only the target item is determined for each target item, and the number of items contained in each target order structure is 1.

4. The method according to any one of claims 1 to 3, characterized in that: The centralized picking conditions preset for any target order structure include: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset first time length.

5. The method according to any one of claims 1 to 3, characterized in that: The centralized picking conditions preset for any target order structure include: the number of pending orders currently having the target order structure in the order pool reaches a preset first number.

6. A method for issuing picking tasks, characterized in that: include: Obtaining order quantities of each order structure in the warehouse in a future period, and determining at least one order structure with the largest order quantity in the future period as a target order structure for the warehouse; For pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met, a centralized picking task is generated and issued based on the current pending orders in the order pool.

7. The method according to claim 6, characterized in that The obtaining of the order quantity of each order structure in the warehouse in the future period includes: The order quantity of the order structure in a future period is determined according to the order quantity of the order structure in a historical period.

8. The method according to claim 6, characterized in that The order structure includes: a single-item order structure including one or more items of one item, and a multi-item order structure including multiple items, and including one or more items of any one item.

9. The method according to any one of claims 6 to 8, characterized in that: The centralized picking conditions preset for any target order structure include: the time interval between the current moment and the nearest wave cut-off moment in the future reaches a preset second time length.

10. The method according to any one of claims 6 to 8, characterized in that: The centralized picking conditions preset for any target order structure include: the number of pending orders currently having the target order structure in the order pool reaches a preset second number.

11. A picking task issuing device, characterized in that: include: A prediction unit, used to obtain the production quantity of items in the warehouse in a future period, and determine at least one item with the largest production quantity in the future period as the target item of the warehouse; An order structure determining unit, configured to determine at least one target order structure including the target item; The centralized picking decision unit is used to generate and issue centralized picking tasks for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met, according to the current pending orders in the order pool.

12. A picking task issuing device, characterized in that: include: An order quantity prediction unit, used to obtain the order quantity of each order structure in the warehouse in a future period, and determine at least one order structure with the largest order quantity in the future period as the target order structure of the warehouse; The picking decision unit is used to generate and issue centralized picking tasks for pending orders with the same target order structure in the order pool of the warehouse, when it is determined that the centralized picking conditions preset for the target order structure are met, according to the current pending orders in the order pool.

13. An electronic device, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 10.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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