Order production method and device, storage medium and electronic equipment

Abstract

The invention relates to the technical field of logistics, in particular to an order production method, an order production device, a storage medium and electronic equipment, and the order production method comprises the steps: responding to a production request of a multi-piece-flow sorting order, and when the multi-piece-flow sorting order comprises a non-on-machine multi-piece-flow sorting order, determining whether there is an idle non-on-machine re-checking table; when there is an idle non-on-machine re-checking table, splitting and combining the multi-piece stream sorting order according to an article stock unit SKU so as to create a sorting task of each SKU; determining a target execution mode according to the attribute information of the SKU corresponding to the order picking task, and executing the order picking task according to the target execution mode so as to pick the SKU and distribute the SKU to a rechecking station; wherein the target execution mode is manual execution or machine execution; and combining the SKUs distributed to the re-checking table according to the multi-piece-flow sorting order so as to complete production of the multi-piece-flow sorting order. According to the order production method provided by the invention, the problem of low productivity of multi-piece flow sorting orders can be solved.

Description

Technical Field

[0001] The present disclosure relates to the field of logistics technology, and in particular to an order production method, an order production device, a storage medium and an electronic device. Background Art

[0002] Flow picking is a production method that improves picking efficiency under the order-based production model. It requires that all SKUs in the order need to be located in the flow picking logic area, and subsequently need to be matched with the review table through the sorting machine.

[0003] For some full-category warehouses, fragile items, special-shaped items and other categories are not suitable for sorting on sorting machines, which makes the production of flow picking orders restricted by categories, thereby reducing the order productivity of flow picking.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to provide an order production method, an order production device, a storage medium and an electronic device, aiming to solve the problem of low productivity of multi-piece flow picking orders.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0007] According to one aspect of an embodiment of the present disclosure, there is provided an order production method, comprising:

[0008] In response to a production request for a multi-piece flow picking order, when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order, determining whether there is an idle non-machine-on review station;

[0009] When there is an idle non-online review station, the multi-piece flow picking order is split and merged according to the item inventory unit SKU to create a picking task for each SKU;

[0010] Determine a target execution mode according to the attribute information of the SKU corresponding to the picking task, and execute the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution;

[0011] The SKUs distributed to the review station are merged according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

[0012] According to some embodiments of the present disclosure, based on the aforementioned solution, the multi-piece flow picking order includes a non-machine multi-piece flow picking order based on the following steps:

[0013] Obtaining order identifiers corresponding to each of the multi-piece flow picking orders; wherein the order identifiers are configured according to attribute information of the SKU in the order and the quantity of the SKU;

[0014] When the order identifications include order identifications of multiple pieces that are not on the machine, it is determined that the flow picking order includes the multiple pieces flow picking order that is not on the machine.

[0015] According to some embodiments of the present disclosure, based on the above solution, determining whether there is an idle non-online review station includes:

[0016] Obtain the threshold for not going online for review; and

[0017] Based on the number of non-machine multi-piece flow picking orders that are being reviewed, the occupied number of non-machine review units is determined; based on the number of non-machine multi-piece flow picking orders that are ready for review, the occupied number of non-machine review units is determined;

[0018] It is determined whether there is an idle non-online review station according to the non-online review station threshold, the number of occupied non-online review stations and the number of pre-occupied non-online review stations.

[0019] According to some embodiments of the present disclosure, based on the above solution, the multi-piece flow picking order includes at least two SKUs, and the multi-piece flow picking order is split and merged according to the item stock keeping unit SKU to create a picking task for each SKU, including:

[0020] Splitting each of the multi-piece flow picking orders into respective SKUs;

[0021] Merge the same SKUs in different multi-piece flow picking orders to create picking tasks for each SKU based on the merged SKUs.

[0022] According to some embodiments of the present disclosure, based on the aforementioned solution, when the attribute information of the SKU is a non-machine-available attribute, the target execution mode is determined to be manual execution;

[0023] The step of executing the picking task according to the target execution mode to distribute the SKUs obtained by picking to the review station of the to-be-produced orders corresponding to the SKUs includes:

[0024] Generate a manual picking task based on the lane and storage location of the non-machine area where the SKU is located, and instruct the staff to perform the manual task to obtain the SKU;

[0025] Determine a target review station according to the target multi-piece flow picking order matched by the SKU; wherein the target multi-piece flow picking order is a non-machine-mounted multi-piece flow picking order, and the target review station is a non-machine-mounted review station;

[0026] A manual distribution task is generated based on the target review station to instruct the staff to perform the manual distribution task to distribute the SKU to the target review station.

[0027] According to some embodiments of the present disclosure, based on the aforementioned solution, determining a target review station according to the target flow picking order matched by the SKU includes:

[0028] When the target flow picking order is a flow picking order being reviewed, the non-machine review station occupied by the flow picking order is used as the target review station;

[0029] When the target flow picking order is a flow picking order to be reviewed, the non-on-machine review station occupied by the flow picking order is used as the target review station;

[0030] Otherwise, an idle review station is used as the target review station.

[0031] According to some embodiments of the present disclosure, based on the aforementioned solution, when the attribute information of the SKU is a machine attribute, it is determined that the target execution mode is machine execution;

[0032] The step of executing the picking task according to the target execution mode to distribute the SKUs obtained by picking to the review station of the to-be-produced orders corresponding to the SKUs includes:

[0033] Generate a machine picking task based on the lane and storage location of the machine area where the SKU is located, so as to use a sorting machine to automatically perform the machine picking task to obtain the SKU;

[0034] Determine the target review station according to the target flow picking order matched by the SKU; wherein the target flow picking order is a non-machine-mounted multi-piece flow picking order or a multi-piece machine-mounted flow picking order, and the target review station is a non-machine-mounted review station or a machine-mounted review station;

[0035] A machine distribution task is generated based on the target review station, so that a sorting machine is used to automatically execute the machine distribution task to distribute the SKU to the target review station.

[0036] According to a second aspect of an embodiment of the present disclosure, there is provided an order production device, comprising:

[0037] A response module, for responding to a production request of a multi-piece flow picking order, and determining whether there is an idle non-machine-on review station when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order;

[0038] A creation module, used for splitting and merging the multi-piece flow picking orders according to the item inventory unit SKU when there is an idle non-online review station, so as to create picking tasks for each SKU;

[0039] An execution module, used to determine a target execution mode according to the attribute information of the SKU corresponding to the picking task, and execute the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution;

[0040] The review module is used to merge the SKUs distributed to the review station according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

[0041] According to a third aspect of an embodiment of the present disclosure, there is provided a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the order production method as in the above embodiment is implemented.

[0042] According to the fourth aspect of an embodiment of the present disclosure, an electronic device is provided, characterized in that it 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 order production method as in the above-mentioned embodiment.

[0043] The exemplary embodiments of the present disclosure may have some or all of the following beneficial effects:

[0044] In the technical solutions provided by some embodiments of the present disclosure, when processing a multi-piece flow picking order that cannot be loaded onto the machine, special processing is performed during order production, that is, manual execution or machine execution is determined as the target execution method based on the attribute information of the SKU, and then the SKU picking task is executed according to the target execution method, and then the SKU is picked and distributed to the corresponding review station, and finally the order is uniformly produced at the review station. On the one hand, by determining different execution methods through the attribute information of the SKU, and then executing them separately by manual or machine methods, the production of multi-piece flow picking orders can be carried out without limiting the categories in the order, thereby broadening the application scenarios of order production; on the other hand, it can support the review and collection of SKUs distributed manually or by machine at the corresponding review station, further improving the production efficiency of flow picking orders.

[0045] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0047] Figure 1 A schematic diagram schematically illustrates a flow chart of an order production method in an exemplary embodiment of the present disclosure;

[0048] Figure 2 A schematic diagram showing a flow chart of a method for determining an idle non-online review station in an exemplary embodiment of the present disclosure;

[0049] Figure 3 A schematic diagram schematically illustrates a flow chart of a method for manually performing a picking task in an exemplary embodiment of the present disclosure;

[0050] Figure 4 A schematic diagram schematically illustrates a flow chart of a method for a machine to perform a picking task in an exemplary embodiment of the present disclosure;

[0051] Figure 5 Schematically showing a process diagram of a multi-piece order production without machine operation in an exemplary embodiment of the present disclosure;

[0052] Figure 6 A schematic diagram schematically shows the composition of an order production device in an exemplary embodiment of the present disclosure;

[0053] Figure 7 A schematic diagram schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure;

[0054] Figure 8 The structure diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0055] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0056] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0057] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0058] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0059] The implementation details of the technical solution of the embodiment of the present disclosure are elaborated in detail below.

[0060] The existing flow picking order production mode is different from the original collection order logic, and there is no need to assemble the collection order to split the task order. It is required that all SKUs in the order need to be located in the flow picking logic area, and the goods are picked by SKU, and then they need to be checked and matched by the sorting machine (hereinafter referred to as the machine). However, for some full-category warehouses, fragile goods, special-shaped goods and other categories are not suitable for sorting by the sorting machine, which reduces the order matching rate of flow picking.

[0061] In view of the shortcomings of the existing flow picking order production model, the present invention proposes a new order production model, which can split the picking tasks into on-machine tasks and off-machine tasks according to the attributes of the SKU in the flow picking order details. After picking for the on-machine tasks, the sorting machine will continue to sort them and match them with the review table. After the off-machine tasks are picked out separately, they will not go through the sorting machine, and the goods will be sent to the corresponding review table by manual sorting, so as to solve the problem of flow picking production being restricted by equipment categories, thereby improving the efficiency of the order production system.

[0062] Before introducing the technical solution of this application, a definition of "order" is given here.

[0063] Flow picking order: refers to the order that adopts the "flow picking" order production mode, that is, all SKUs in the order need to be located in the flow picking logic area, and then need to be matched by the sorting machine for verification;

[0064] Multiple-item order: refers to an order that includes multiple SKUs;

[0065] Single order: means the order includes one SKU;

[0066] On-machine order: refers to the order in which all SKUs are on-machine attributes;

[0067] Non-shipping order: This order includes at least one SKU with the non-shipping attribute.

[0068] The "multi-piece flow picking order" involved in this application is both a multi-piece order and a flow picking order.

[0069] Figure 1 The following is a schematic diagram showing a process flow of an order production method in an exemplary embodiment of the present disclosure. Figure 1 As shown, the order production method includes steps S101 to S104:

[0070] Step S101, in response to a production request of a multi-piece flow picking order, when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order, determining whether there is an idle non-machine-on review station;

[0071] Step S102, when there is an idle non-online review station, the multi-piece flow picking order is split and merged according to the item inventory unit SKU to create a picking task for each SKU;

[0072] Step S103, determining a target execution mode according to the attribute information of the SKU corresponding to the picking task, and executing the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution;

[0073] Step S104, merging the SKUs distributed to the review station according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

[0074] In the technical solutions provided by some embodiments of the present disclosure, when processing a multi-piece flow picking order that cannot be loaded onto the machine, special processing is performed during order production, that is, manual execution or machine execution is determined as the target execution method based on the attribute information of the SKU, and then the SKU picking task is executed according to the target execution method, and then the SKU is picked and distributed to the corresponding review station, and finally the order is uniformly produced at the review station. On the one hand, by determining different execution methods through the attribute information of the SKU, and then executing them separately by manual or machine methods, the production of multi-piece flow picking orders can be carried out without limiting the categories in the order, thereby broadening the application scenarios of order production; on the other hand, it can support the review and collection of SKUs distributed manually or by machine at the corresponding review station, further improving the production efficiency of flow picking orders.

[0075] Below, each step of the order production method in this example implementation will be described in more detail with reference to the accompanying drawings and embodiments.

[0076] In step S101, in response to a production request of a multi-piece flow picking order, when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order, it is determined whether there is an idle non-machine-on review station.

[0077] Specifically, after the order production system receives the order issued by the OFC (Order Fulfillment Center), it extracts a multi-piece flow picking order from it. Among them, the multi-piece flow picking order is both a multi-piece order and a flow picking order. Multi-piece means that the order includes SKUs, and flow picking means that the order supports the flow picking mode.

[0078] For non-flow picking orders, other production methods such as collective order production can be adopted. For single-piece orders, there is no need to conduct multiple-piece collection and review, so these parts are not within the scope of this disclosure.

[0079] After responding to the production request of the multi-piece flow picking order, it can be first determined whether the multi-piece flow picking order includes the non-machine-mounted multi-piece flow picking order. This is because if the flow picking order does not include the non-machine-mounted multi-piece flow picking order, then the picking logic is no different from the existing flow picking order production mode, and no special processing is required. However, if the non-machine-mounted multi-piece flow picking order is included, a different execution method needs to be adopted.

[0080] In one embodiment of the present disclosure, the inclusion of non-machine-mounted multi-piece flow picking orders in the flow picking order is determined based on the following steps: obtaining order identifiers corresponding to each of the multi-piece flow picking orders; wherein the order identifiers are configured based on the attribute information of the SKU and the quantity of the SKU in the order; and when an order identifier of non-machine-mounted multi-pieces is included in each of the order identifiers, it is determined that the flow picking order includes the non-machine-mounted multi-piece flow picking order.

[0081] Among them, each flow picking order corresponds to its own order identifier, so the order identifier corresponding to the flow picking order can be configured in advance according to the attribute information of the SKU and the quantity of the SKU in the flow picking order.

[0082] The order identification of flow picking can be divided into the following three categories:

[0083] ① Multiple pieces that are not on the machine, that is, the flow picking order includes multiple SKUs, and at least one of the multiple SKUs has the attribute of not being on the machine. Orders with this order identifier are multi-piece flow picking orders that are not on the machine, for example, Order 1: 1 cup, 1 toothbrush, where the cup is fragile and cannot be used in the sorting machine, and has the attribute of not being on the machine;

[0084] ② Multiple pieces on the machine, that is, the flow picking order includes multiple SKUs, and the multiple SKUs are all SKUs with the attribute of being on the machine. Orders with this order identifier are multi-piece flow picking orders on the machine, for example, Order 2: Toothpaste*1, toothbrush*1, toothpaste and toothbrush can be used in the sorting machine, and both have the attribute of being on the machine;

[0085] ③ Single piece, that is, the flow picking order includes one SKU. Orders with this order identifier are single piece flow picking orders, such as Order 3: Cup*1. Single piece flow picking orders do not require review by the review station, so the production logic of single piece flow picking orders is different and is not considered in this application.

[0086] The process of configuring the order identification can be completed in the OFC, or after receiving the order issued by the OFC, the orders can be labeled in sequence, such as marking a preset number on a fixed position of the order type tag, for example, businesstag142 bit = 4, and then querying the order identification through the order type tag.

[0087] After obtaining the order identifiers corresponding to the multi-piece flow picking orders, you can check whether there is an order identifier for multiple pieces that are not on the machine. If so, it means that the multi-piece flow picking orders include multi-piece flow picking orders that are not on the machine. If not, it will be processed according to the existing flow picking process.

[0088] It should be noted that the volume of multi-piece flow picking orders can also be checked. Since multi-piece flow picking orders need to be checked using a review table, the volume of all goods corresponding to the multi-piece flow picking order must meet the volume limit of the review table grid, otherwise the review table cannot be used.

[0089] Therefore, a non-machine-mounted multi-piece flow picking order can have the following characteristics: ① The order is a flow picking order; ② The order includes multiple SKUs; ③ There is at least one SKU with the non-machine-mounted attribute in the order; ④ Business tag142 bit = 4; ⑤ The volume meets the review grid restriction.

[0090] When it is determined according to the order identifier that the multi-piece flow picking order includes the non-machine-on multi-piece flow picking order, it is determined whether there is an idle non-machine-on review station.

[0091] Figure 2 The following is a schematic diagram showing a flow chart of a method for determining an idle non-online review station in an exemplary embodiment of the present disclosure. Figure 2 As shown in the figure, the specific steps to determine the idle and non-online review station are as follows:

[0092] Step S201, obtaining a threshold value for not going online for review; and

[0093] Step S202, determining the occupied number of non-machine review units based on the number of non-machine multi-piece flow picking orders being reviewed, and determining the reserved number of non-machine review units based on the number of non-machine multi-piece flow picking orders to be reviewed;

[0094] Step S203, determining whether there is an idle non-online review station according to the non-online review station threshold, the number of occupied non-online review stations and the number of pre-occupied non-online review stations.

[0095] Specifically, one flow picking order occupies one review station, which can be divided into non-machine review stations and machine review stations. Non-machine review stations correspond to non-machine multiple-piece orders; machine review stations correspond to machine multiple-piece orders.

[0096] In step 201, although there are SKUs in the non-machine orders that are not allowed to be distributed through the sorting machine, they need to be reviewed through electronic labels or automatic rebin, so a review station with electronic labels or automatic rebin is required. Therefore, the producible quantity of the non-machine orders is affected by the review station threshold.

[0097] During operation, some electronic label review walls will be allocated to review the non-machine orders. According to the actual situation, the non-machine review station ratio coefficient can be maintained, and then the maximum number of non-machine orders that can be produced can be calculated based on the review station threshold * non-machine review station ratio coefficient = non-machine review station threshold.

[0098] In step S202, the number of occupied non-machine review stations, that is, the non-machine review stations that are currently reviewing flow picking orders, corresponds to the number of non-machine multi-piece flow picking orders being reviewed, that is, the backlog number of non-machine orders.

[0099] The pre-occupied non-machine review station means that the review station has been assigned a non-machine flow picking order, but is currently in the picking or distribution stage and has not yet reached the review stage. The number of pre-occupied non-machine review stations corresponds to the number of non-machine multi-piece flow picking orders prepared for review.

[0100] In step S203, the result of calculating the threshold of non-online verification stations - the number of occupied non-online verification stations - the number of pre-occupied non-online verification stations is greater than 0. If the result is greater than 0, it means that there are currently idle non-online verification stations. If the result is ≤0, it means that there are currently no idle non-online verification stations.

[0101] It should be noted that if there is an idle non-online review table, the task allocation worker can be executed, that is, the picking task is created according to the method provided in the present invention; if there is no idle non-online review table, the task allocation worker will not be executed, and the flow picking order will be displayed on the front-end task allocation interface. Task allocation will be carried out after there are sufficient non-online review tables, or manual operation can be performed to switch to the collection order production mode.

[0102] In step S102, when there is an idle non-online review station, the multi-piece flow picking order is split and merged according to the item inventory unit SKU to create a picking task for each SKU.

[0103] In one embodiment of the present disclosure, a multi-piece flow picking order includes at least two SKUs, and the multi-piece flow picking order is split and merged according to the item inventory unit SKU to create a picking task for each SKU, including: splitting each of the multi-piece flow picking orders into each SKU respectively; merging the same SKU in different multi-piece flow picking orders to create a picking task for each SKU according to the merged SKU.

[0104] Specifically, in the production mode of multi-piece flow picking orders, orders can be split and merged according to SKU, and then the orders can be converted into SKU picking tasks. For example, if the multi-piece flow picking order includes the aforementioned order 1: cup*1, toothbrush*1; order 2: toothpaste*1, toothbrush*1, three picking tasks of cup*1, toothbrush*2, and toothpaste*1 are created.

[0105] It should be noted that although the production logic of single-piece orders is different from that of multiple-piece orders, they can also be combined during the picking process.

[0106] In step S103, the target execution mode is determined according to the attribute information of the SKU corresponding to the picking task, and the picking task is executed according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution.

[0107] There are two main steps in executing the picking task. First, the goods can be picked up from the storage area and taken off the shelves. Then the picked up goods are distributed to the review table for order review.

[0108] There are two main ways to perform picking tasks. One is manual execution, that is, workers pick and distribute the goods. The other is machine execution, that is, using a sorting machine to pick and distribute the goods.

[0109] Specifically, after the task allocation and the picking task is issued, it is determined whether the SKU in the picking task is a non-machine-connected SKU, that is, whether the attribute information of the SKU is a non-machine-connected attribute. If it is a non-machine-connected SKU, the corresponding picking task is a non-machine-connected picking task, which is executed manually. If it is a machine-connected SKU, the corresponding picking task is an machine-connected picking task, which is executed by machine.

[0110] In one embodiment of the present disclosure, when the attribute information of the SKU is a non-machine-connected attribute, the target execution mode is determined to be manual execution. Figure 3 The following is a schematic diagram of a process flow of a method for manually performing a picking task in an exemplary embodiment of the present disclosure. Figure 3 As shown, the steps of executing the picking task in step S103 are as follows:

[0111] Step S301, generating a manual picking task based on the lane and storage location of the non-machine area where the SKU is located, to instruct the staff to perform the manual task to obtain the SKU;

[0112] Step S302, determining a target review station according to the target multi-piece flow picking order matched by the SKU; wherein the target multi-piece flow picking order is a non-machine-mounted multi-piece flow picking order, and the target review station is a non-machine-mounted review station;

[0113] Step S303: Generate a manual distribution task based on the target review station to instruct the staff to perform the manual distribution task to distribute the SKU to the target review station.

[0114] Specifically, non-machine SKUs are usually located in storage locations in non-machine areas, and non-machine picking tasks are displayed with separate buttons on the RF end to distinguish picking task types. When the picking task is a non-machine picking task, first add the operation buttons "non-machine task automatic collection" (including the lane where the SKU is located) and "non-machine task collection by storage location" (including the storage location where the SKU is located) as manual picking tasks.

[0115] Among them, the staff clicks the "Automatically pick up tasks without going to the machine" button, and the judgment logic is consistent with the automatic collection of flow picking tasks. The aisle with the highest density is judged for the first collection, and the collection starts from the aisle with the highest density, and subsequent tasks are pulled according to storage locations; then the staff clicks the "Picking up tasks by storage location without going to the machine" button, which allows the collection of tasks at the current storage location by scanning the storage location; other delisting processes and logic remain unchanged.

[0116] In step S302, the SKUs obtained by picking are matched with corresponding target review stations. It should be noted that the multi-piece flow picking orders corresponding to the non-machine SKUs are non-machine multi-piece flow picking orders, because these non-machine multi-piece flow picking orders include non-machine SKUs. The review stations corresponding to the non-machine SKUs are all non-machine review stations, because only non-machine review stations will review non-machine SKUs.

[0117] In one embodiment of the present disclosure, the target review station is determined according to the target multi-piece flow picking order matched with the SKU, including: when the target multi-piece flow picking order is a multi-piece flow picking order under review, the non-machine review station occupied by the multi-piece flow picking order is used as the target review station; when the target multi-piece flow picking order is a multi-piece flow picking order prepared for review, the non-machine review station occupied by the multi-piece flow picking order is used as the target review station; otherwise, an idle review station is used as the target review station.

[0118] Specifically, if the target multi-piece flow picking order corresponding to the SKU has occupied or pre-occupied the verification station, the SKU will be distributed to the corresponding verification station. If not, it will be matched with an idle verification station. In addition, if there is no idle verification station at this time, the result of matching failure can be generated and distributed to the re-delivery port, and the abnormal reason will be recorded for subsequent re-distribution.

[0119] In one embodiment of the present disclosure, the process of matching a SKU to a target review station is as follows:

[0120] (1) Determine the matching target multi-piece flow picking order based on SKU;

[0121] (2) Determine whether the target multi-piece flow picking order is a multi-piece flow picking order that is being reviewed; if it is a multi-piece flow picking order that is being reviewed, that is, the review station has been occupied, then the SKU is distributed to the occupied review station, and the distribution is completed; if it is not a multi-piece flow picking order that is being reviewed, that is, the review station is not occupied, then execute (3);

[0122] (3) Determine whether there is a non-online verification station; if there is no non-online verification station, broadcast the SKU to the re-delivery port, and the abnormal reason is "no verification station matched"; if there is a non-online verification station, execute (4);

[0123] (4) Continue to determine whether the review station is configured with a multi-piece flow picking order; if the review station is not configured with a multi-piece flow picking order, the SKU is distributed to the re-delivery port, and the abnormal reason is "not matched to the review station"; if the review station is configured with a multi-piece flow picking order, execute (5);

[0124] (5) Determine whether there is an idle review station; if there is an idle review station, the SKU is broadcast to the idle review station and the broadcast is completed; if there is no idle review station, the SKU is broadcast to the re-delivery port, and the abnormal reason is "no idle review station".

[0125] In step S303, a manual distribution task is generated based on the target review station to instruct the staff to perform the manual distribution task to distribute the SKU to the target review station.

[0126] After the target review station is determined, a manual distribution task is generated according to the target review station, and the staff manually delivers the SKU to the target review station according to the target review station.

[0127] It should be noted that when the SKU is obtained after picking up the goods from the shelves, it can be determined whether the current warehouse is configured with a flow picking line. If the current warehouse is configured with a flow picking line, WCS is called to create a conveyor line task to transport the SKU to the target review station; if the current warehouse is not configured with a flow picking line, there is no need to call the conveyor line.

[0128] In one embodiment of the present disclosure, when the attribute information of the SKU is a non-machine-connected attribute, the target execution mode is determined to be manual execution.

[0129] In one embodiment of the present disclosure, the attribute information of the SKU is a machine attribute, and the target execution mode is determined to be machine execution. Figure 4 The following is a schematic diagram showing a flow chart of a method for a machine to perform a picking task in an exemplary embodiment of the present disclosure. Figure 4 As shown, the steps of executing the picking task in step S103 are as follows:

[0130] Step S401, generating a machine picking task based on the lane and storage location of the machine area where the SKU is located, so as to use a sorting machine to automatically execute the machine picking task to obtain the SKU;

[0131] Step S402, determining a target review station according to the target multi-piece flow picking order matched by the SKU; wherein the target multi-piece flow picking order is a non-machine multi-piece flow picking order or a multi-piece machine flow picking order, and the target review station is a non-machine review station or a machine review station;

[0132] Step S403: generating a machine distribution task based on the target review station, so as to use a sorting machine to automatically execute the machine distribution task and distribute the SKU to the target review station.

[0133] Specifically, in step S401, the SKUs on the machine are usually located in the storage area of ​​the machine area, and the sorting machine is used to automatically perform machine picking tasks according to the lanes and storage locations to pick the SKUs off the shelves.

[0134] It should be noted that the machine area usually does not support non-machine picking tasks. If the SKU is located in the machine area and the non-machine picking attribute is maintained, the order has been marked as a flow picking order, then production needs to be carried out according to the logic of the machine flow picking order.

[0135] In step S402, the logic of determining the target review station is similar to that of manual picking in S302, except that the target multi-piece flow picking order matched by the machine SKU can be all types of multi-piece flow picking orders, and the matching review station can be a non-machine review station or an machine review station, because some machine SKUs can also exist in the non-machine order.

[0136] In step S403, the sorting machine is still used for automatic distribution, thereby achieving the process of matching the SKU to the target review station.

[0137] In step S104, the SKUs distributed to the review station are merged according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

[0138] Specifically, a multi-piece flow picking order needs to be reviewed to determine whether the SKUs in the multi-piece flow picking order are complete. When all the SKUs in the multi-piece flow picking order are collected, they can be packaged to complete the production of the multi-piece flow picking order for subsequent order delivery; if not collected, continue to distribute.

[0139] Based on the above method, it can be known that the on-machine SKUs in the non-on-machine multiple-piece orders are allowed to be distributed to the non-on-machine review station, that is, the non-on-machine SKUs are only allowed to be distributed to the non-on-machine review station, but the on-machine SKUs can be distributed to both the non-on-machine review station and the on-machine review station at the same time; orders in which all SKUs are on-machine SKUs cannot be distributed to the non-on-machine review station.

[0140] Figure 5 A schematic diagram of a process flow of producing multiple orders without using a machine in an exemplary embodiment of the present disclosure is shown. Figure 5 As shown:

[0141] Steps S501 to S508 are the process of receiving orders. Step S501 is to receive OFC order information. Step S502 is to check whether the order supports flow picking. If so, execute step S503. If not, execute step S506 to include it in the collection order process. Step S503 is to determine whether it is a multi-piece flow picking order. For the convenience of description, all orders after step S503 are flow picking orders. If it is multi-piece, execute step S504. If not, execute step S507 to mark the order as a single-piece flow picking order. Step S504 is to check whether there is a non-machined sku in the order details. If so, execute step S505 to mark the order as a non-machined multi-piece flow picking order. If not, execute step S508 to execute the original multi-piece flow picking order process.

[0142] Steps S509 to S511 are a positioning process. Step S509 is to locate the order, i.e., determine where the items in the order are stored. Step S510 is to determine whether the items are located in the machine area. If they are in the machine area, step S511 is executed to execute the original multi-piece flow picking order process. If not, step S512 is executed.

[0143] Steps S512 to S514 are the process of task allocation. Step S12 determines whether the threshold of non-online review stations - backlog of orders - pre-occupied non-online review stations is greater than 0. If it is greater than 0, then execute step S513 and temporarily do not allocate tasks. If it is not greater than 0, then execute step S514 to allocate tasks.

[0144] Steps S515 to S521 are the process of picking allocation. Step S515, the picking task is issued; Step S516, determine whether the picking task detail sku is a non-machine picking sku, if so, execute step S517, that is, produce multiple non-machine picking tasks, if not, execute step S518, that is, produce ordinary flow picking on the machine picking tasks; Step S519, complete the picking according to the picking tasks; Step S520, determine whether to generate a transmission and selection line task, if generated, execute step S521, that is, call WCS to generate multiple non-machine transmission and selection line tasks, if not generated, execute step S522.

[0145] Steps S522 to S536 are the process of distribution. Step S522 generates a distribution backlog. Step S5223 determines whether there is a non-online task. If so, execute step S524, that is, use the import station for distribution. If not, execute step S525 to manually distribute the matching order. When manually distributing the matching order, step S526 determines whether the matched order has occupied a review station. If so, execute step S533 to allocate it to the review station of the corresponding order. If not, execute step S527 to determine whether there are multiple non-online review stations and perform a test. If so, execute step S528 to determine whether the multiple non-online review stations are configured with order review types. If the non-online review stations are If the order review type is not configured, or there are multiple non-machine-based review stations, step S531 will be executed, and the reason for the exception will be recorded as the failure to match the review station. If the non-machine-based review station is configured with the order review type, step S529 will be executed to determine whether there is an available review station. If so, step S530 will be executed to assign the order to an available review station. If not, step S532 will be executed, and the reason for the exception will be recorded as no available review station. After completing the above process, step S534 will be executed, and sku distribution will be completed. Step S535 will determine whether the sku in the order are complete. If so, step S536 will be executed to complete the order distribution. If not, wait for completion.

[0146] Steps S527 to S538 are the review process. Step S537 is the review backlog left over from before. After the review backlog is completed, step S538 is reviewed and packaged, and the production of the order is completed.

[0147] Based on the above method, the picking task is divided into on-machine tasks and off-machine tasks according to the attributes of the SKU in the order details. After picking on the machine task, the sorting machine will continue to sort and match the review table. After picking off the off-machine task, the goods will be sent to the corresponding review table manually without going through the sorting machine, so as to solve the problem of equipment-restricted categories in flow picking production. At the same time, manual sorting and sorting machine sorting can be combined to collect orders, and the on-machine goods and manual goods in the order can be gathered on the same review table.

[0148] Figure 6 The following is a schematic diagram showing the composition of an order production device in an exemplary embodiment of the present disclosure. Figure 6 As shown, the order production device 600 may include a response module 601, a creation module 602, an execution module 603 and a review module 604. Among them:

[0149] A response module 601 is used to respond to a production request of a multi-piece flow picking order, and when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order, determine whether there is an idle non-machine-on review station;

[0150] A creation module 602 is used to split and merge the multi-piece flow picking orders according to the item inventory unit SKU to create picking tasks for each SKU when there is an idle non-online review station;

[0151] An execution module 603 is used to determine a target execution mode according to the attribute information of the SKU corresponding to the picking task, and execute the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution;

[0152] The review module 604 is used to merge the SKUs distributed to the review station according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

[0153] According to an exemplary embodiment of the present disclosure, the response module 601 is also used to obtain the order identification corresponding to each of the multi-piece flow picking orders; wherein the order identification is configured according to the attribute information of the SKU and the quantity of the SKU in the order; when the order identification of multiple pieces that are not shipped on the machine is included in each of the order identifications, it is determined that the flow picking order includes the multiple-piece flow picking order that is not shipped on the machine.

[0154] According to an exemplary embodiment of the present disclosure, the response module 601 is also used to obtain a threshold value for non-online review stations; and determine the number of occupied non-online review stations based on the number of multi-piece flow picking orders that are being reviewed, and determine the number of pre-occupied non-online review stations based on the number of multi-piece flow picking orders that are ready for review; and determine whether there is an idle non-online review station based on the threshold value for non-online review stations, the number of occupied non-online review stations, and the pre-occupied number of non-online review stations.

[0155] According to an exemplary embodiment of the present disclosure, the multi-piece flow picking order includes at least two SKUs, and the creation module 602 is also used to split each of the multi-piece flow picking orders into respective SKUs; merge the same SKUs in different multi-piece flow picking orders to create picking tasks for each SKU according to the merged SKU.

[0156] According to an exemplary embodiment of the present disclosure, when the attribute information of the SKU is a non-machine-connected attribute, the target execution mode is determined to be manual execution; the execution module 603 includes a first execution unit, which is used to generate a manual picking task based on the lanes and storage locations in the non-machine area where the SKU is located, to instruct the staff to perform the manual execution task to obtain the SKU; determine the target review station according to the target multi-piece flow picking order matched by the SKU; wherein the target multi-piece flow picking order is a non-machine-connected multi-piece flow picking order, and the target review station is a non-machine-connected review station; generate a manual distribution task based on the target review station, to instruct the staff to perform the manual distribution task to distribute the SKU to the target review station.

[0157] According to an exemplary embodiment of the present disclosure, the first execution unit is also used to, when the target flow picking order is a flow picking order under review, use the non-machine review station occupied by the flow picking order as the target review station; when the target flow picking order is a flow picking order to be prepared for review, use the non-machine review station occupied by the flow picking order as the target review station; otherwise, use an idle review station as the target review station.

[0158] According to an exemplary embodiment of the present disclosure, when the attribute information of the SKU is a machine attribute, it is determined that the target execution mode is machine execution; the execution module 603 also includes a second execution unit, and the second execution unit is also used to generate a machine picking task based on the lanes and storage locations in the machine area where the SKU is located, so as to adopt a sorting machine to automatically execute the machine picking task to obtain the SKU; determine the target review station according to the target flow picking order matched by the SKU; wherein the target flow picking order is a non-machine multi-piece flow picking order or a multi-piece machine flow picking order, and the target review station is a non-machine review station or a machine review station; generate a machine distribution task based on the target review station, so as to adopt a sorting machine to automatically execute the machine distribution task to distribute the SKU to the target review station.

[0159] The specific details of each module in the above-mentioned order production device 600 have been described in detail in the corresponding order production method, so they will not be repeated here.

[0160] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0161] In an exemplary embodiment of the present disclosure, a storage medium capable of implementing the above method is also provided. Figure 7 A schematic diagram schematically illustrates a computer-readable storage medium in an exemplary embodiment of the present disclosure, such as Figure 7 As shown, a program product 700 for implementing the above method according to an embodiment of the present disclosure is described, which can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a mobile phone. However, the program product of the present disclosure is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.

[0162] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided. Figure 8 The structure diagram of a computer system of an electronic device in an exemplary embodiment of the present disclosure is schematically shown.

[0163] It should be noted that Figure 8 The computer system 800 of 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 disclosure.

[0164] 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 the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803. In the RAM 803, various programs and data required for system operation are also stored. The CPU 801, the ROM 802 and the RAM 803 are connected to each other through a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804.

[0165] 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 (Local Area Network) 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.

[0166] In particular, according to an embodiment of the present disclosure, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure 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 flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 809, and / or installed from a removable medium 811. When the computer program is executed by a central processing unit (CPU) 801, various functions defined in the system of the present disclosure are executed.

[0167] It should be noted that the computer-readable medium shown in the embodiment of the present disclosure 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, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, 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), a 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 disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals 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 programs 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, wired, etc., or any suitable combination of the above.

[0168] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. 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.

[0169] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, and the units described may also be arranged in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0170] As another aspect, the present disclosure further provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the above embodiment.

[0171] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0172] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0173] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. The present disclosure is intended to cover any variations, uses or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary technical means in the art that are not disclosed in the present disclosure.

[0174] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An order production method, characterized in that: include: In response to a production request for a multi-piece flow picking order, when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order, determining whether there is an idle non-machine-on review station; When there is an idle non-online review station, the multi-piece flow picking order is split and merged according to the item inventory unit SKU to create a picking task for each SKU; Determine a target execution mode according to the attribute information of the SKU corresponding to the picking task, and execute the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution; The SKUs distributed to the review station are merged according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

2. The order production method according to claim 1, characterized in that: The multi-piece flow picking order includes the non-machine multi-piece flow picking order based on the following steps: Obtaining order identifiers corresponding to each of the multi-piece flow picking orders; wherein the order identifiers are configured according to attribute information of the SKU in the order and the quantity of the SKU; When the order identifications include order identifications of multiple pieces that are not on the machine, it is determined that the flow picking order includes the multiple pieces flow picking order that is not on the machine.

3. The order production method according to claim 1, characterized in that: The determining whether there is an idle non-online verification station includes: Obtain the threshold for not going online for review; and Based on the number of non-machine multi-piece flow picking orders that are being reviewed, the occupied number of non-machine review units is determined; based on the number of non-machine multi-piece flow picking orders that are ready for review, the occupied number of non-machine review units is determined; It is determined whether there is an idle non-online review station according to the non-online review station threshold, the number of occupied non-online review stations and the number of pre-occupied non-online review stations.

4. The order production method according to claim 1, characterized in that: The multi-piece flow picking order includes at least two SKUs, and the multi-piece flow picking order is split and merged according to the item stock keeping unit SKU to create a picking task for each SKU, including: Splitting each of the multi-piece flow picking orders into respective SKUs; Merge the same SKUs in different multi-piece flow picking orders to create picking tasks for each SKU based on the merged SKUs.

5. The order production method according to claim 1, characterized in that: When the attribute information of the SKU is a non-online attribute, determining that the target execution mode is manual execution; The step of executing the picking task according to the target execution mode to distribute the SKUs obtained by picking to the review station of the to-be-produced orders corresponding to the SKUs includes: Generate a manual picking task based on the lane and storage location of the non-machine area where the SKU is located, and instruct the staff to perform the manual task to obtain the SKU; Determine a target review station according to the target multi-piece flow picking order matched by the SKU; wherein the target multi-piece flow picking order is a non-machine-mounted multi-piece flow picking order, and the target review station is a non-machine-mounted review station; A manual distribution task is generated based on the target review station to instruct the staff to perform the manual distribution task to distribute the SKU to the target review station.

6. The order production method according to claim 5, characterized in that: The step of determining a target review station according to the target flow picking order matched by the SKU comprises: When the target flow picking order is a flow picking order being reviewed, the non-machine review station occupied by the flow picking order is used as the target review station; When the target flow picking order is a flow picking order to be reviewed, the non-on-machine review station occupied by the flow picking order is used as the target review station; Otherwise, an idle review station is used as the target review station.

7. The order production method according to claim 1, characterized in that: When the attribute information of the SKU is a machine attribute, determining that the target execution mode is machine execution; The step of executing the picking task according to the target execution mode to distribute the SKUs obtained by picking to the review station of the to-be-produced orders corresponding to the SKUs includes: Generate a machine picking task based on the lane and storage location of the machine area where the SKU is located, so as to use a sorting machine to automatically perform the machine picking task to obtain the SKU; Determine the target review station according to the target flow picking order matched by the SKU; wherein the target flow picking order is a non-machine-mounted multi-piece flow picking order or a multi-piece machine-mounted flow picking order, and the target review station is a non-machine-mounted review station or a machine-mounted review station; A machine distribution task is generated based on the target review station, so that a sorting machine is used to automatically execute the machine distribution task to distribute the SKU to the target review station.

8. An order production device, characterized in that: include: A response module, for responding to a production request of a multi-piece flow picking order, and determining whether there is an idle non-machine-on review station when the multi-piece flow picking order includes a non-machine-on multi-piece flow picking order; A creation module, used for splitting and merging the multi-piece flow picking orders according to the item inventory unit SKU when there is an idle non-online review station, so as to create picking tasks for each SKU; An execution module, used to determine a target execution mode according to the attribute information of the SKU corresponding to the picking task, and execute the picking task according to the target execution mode to pick the SKU and distribute the SKU to the review station; wherein the target execution mode is manual execution or machine execution; The review module is used to merge the SKUs distributed to the review station according to the multi-piece flow picking order to complete the production of the multi-piece flow picking order.

9. A computer-readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the order production method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: one or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, enables the one or more processors to implement the order production method as described in any one of claims 1 to 7.

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