Order processing

Abstract

The present application provides an order processing method and apparatus, an electronic device, and a machine-readable storage medium. The method comprises: acquiring information of one or more original orders; for each of the one or more original orders, performing correlation analysis on goods of the original order to determine the correlation between the goods; on the basis of the correlation between the goods, splitting the original order to obtain at least one sub-order, wherein in the same order, the higher the correlation between goods is, the higher the possibility of splitting the goods into the same sub-order is; and on the basis of the goods similarity between the sub-orders, performing wave picking on a plurality of sub-orders split from the one or more original orders, and assigning the grouped sub-orders to workstations for execution in waves, wherein the higher the goods similarity among the plurality of sub-orders is, the higher the possibility of grouping the plurality of sub-orders into a same wave is. The method can effectively improve the order processing efficiency.

Description

Order processing Technical Field

[0001] The present application relates to the field of logistics and distribution technology, and in particular to methods, devices, electronic devices, and machine-readable storage media for order processing. Background Art

[0002] In logistics and distribution, for some orders with high volumes and specific delivery timelines, traditional order-by-order processing can lead to excessively long execution times and inability to meet timeliness requirements. Therefore, orders need to be split and reorganized before execution to improve timeliness.

[0003] Traditional order splitting methods are designed for manual warehouse operations and are not suitable for robotic goods-to-person delivery scenarios. For these scenarios, how to properly split orders becomes a pressing technical challenge.

[0004] Summary of the Invention

[0005] In view of this, the present application provides an order processing method, device, electronic device and machine-readable storage medium.

[0006] According to a first aspect of an embodiment of the present application, an order processing method is provided, comprising: obtaining information of one or more original orders; for each of the one or more original orders, performing a correlation analysis on the goods in the original order to determine the correlation between the goods; splitting the original order based on the correlation between the goods to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the goods, the higher the possibility that the goods are split into the same sub-order; based on the similarity of the goods between the sub-orders, performing wave combination on the multiple sub-orders split from the one or more original orders, and assigning the combined sub-orders to workstations for execution by wave; wherein, the higher the similarity of the goods between the multiple sub-orders, the higher the possibility that the multiple sub-orders are combined into the same wave.

[0007] According to a second aspect of an embodiment of the present application, an order processing device is provided, including: an acquisition unit for acquiring information of one or more original orders; a determination unit for performing a correlation analysis on the goods of the original order for each of the one or more original orders, and determining the correlation between the goods; a splitting unit for splitting the original order according to the correlation between the goods to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the goods, the higher the possibility that the goods are split into the same sub-order; a combination unit for combining multiple sub-orders split from the one or more original orders in waves according to the similarity of the goods between the sub-orders; wherein, the higher the similarity of the goods between the multiple sub-orders, the higher the possibility that the multiple sub-orders are combined into the same wave; and an allocation unit for allocating the combined sub-orders to workstations for execution according to waves.

[0008] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising a processor and a machine-readable storage medium, wherein the machine-readable storage medium stores machine-executable instructions that can be executed by the processor, and the processor is used to execute the machine-executable instructions to implement the method provided in the first aspect.

[0009] According to a fourth aspect of an embodiment of the present application, a machine-readable storage medium is provided, wherein the machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a processor, the method provided in the first aspect is implemented.

[0010] The order processing method of the embodiment of the present application determines the correlation between the goods by performing a correlation analysis on the goods included in each order in one or more original orders, and splits each order in the original order based on the correlation between the goods to obtain the split sub-orders. Then, based on the similarity of the goods between the sub-orders, the sub-orders are combined in waves, and the combined sub-orders are assigned to workstations for execution by wave. Since, when splitting orders, goods with high correlation are preferentially split into the same sub-order, and when combining waves, sub-orders with high similarity of goods are preferentially combined into the same wave, the handling efficiency of the same goods in different orders can be improved, thereby effectively improving the order processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] FIG1 is a flow chart of an order processing method provided in an embodiment of the present application.

[0012] FIG2 is a schematic diagram of an order splitting process provided in an embodiment of the present application.

[0013] FIG3 is a schematic diagram of a specific flow of order processing provided in an embodiment of the present application.

[0014] FIG4 is a schematic structural diagram of an order processing device provided in an embodiment of the present application.

[0015] FIG5 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0017] The terms used in this application are for the purpose of describing particular embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0018] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, some of the terms involved in the embodiments of the present application are briefly explained below.

[0019] (1) Goods to Personnel: refers to the process in which during warehousing and logistics operations, workers are at a fixed location and goods are transported to that location by robotic equipment for human operation.

[0020] (2) Original order: refers to the original order issued by the upstream system.

[0021] (3) Sub-order: refers to the new order generated after the original order is split.

[0022] (4) Stock Keeping Unit (SKU): refers to the only distinguishable category of goods in the warehouse. One SKU uniquely identifies a category of goods.

[0023] (5) Order line: refers to each item in the order. An order usually contains multiple order lines, and different order lines correspond to different items and their related information, such as SKU, product description, quantity, batch, etc.

[0024] In order to make the above-mentioned purposes, features and advantages of the embodiments of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application are further described in detail below with reference to the accompanying drawings.

[0025] It should be noted that the serial numbers of the steps in the embodiments of the present application do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0026] Please refer to Figure 1, which is a flow chart of an order processing method provided in an embodiment of the present application. As shown in Figure 1, the order processing method may include the following steps S100-S130.

[0027] Step S100: Obtain information of one or more original orders.

[0028] Step S110: For each of the one or more original orders, perform a correlation analysis on the goods in the original order to determine the correlation between the goods.

[0029] Step S120: Split the original order based on the correlation between the goods to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the goods, the higher the possibility that the goods will be split into the same sub-order.

[0030] In an embodiment of the present application, for any order, when splitting the order, the correlation between the goods in the order can be considered, and the order can be split based on the principle of splitting goods with high correlation into the same sub-order.

[0031] Accordingly, when the information of the original order is obtained, a correlation analysis can be performed on the goods included in each order in the original order to determine the correlation between the goods.

[0032] The higher the correlation between different products, the higher the probability that the different products appear in the same order.

[0033] The original order can be split according to the correlation between the goods to obtain the split sub-orders.

[0034] For example, the higher the correlation between goods, the higher the possibility that the goods will be split into the same sub-order. That is, when splitting an order, goods with high correlation will be split into the same sub-order first.

[0035] Step S130: Based on the similarity of goods between the sub-orders, the multiple sub-orders split from the one or more original orders are combined in waves, and the combined sub-orders are assigned to workstations for execution in waves; wherein, the higher the similarity of goods between the multiple sub-orders, the higher the possibility that the multiple sub-orders are combined into the same wave.

[0036] In an embodiment of the present application, in order to improve order processing efficiency, after the order splitting is completed in the above manner, the sub-orders can be combined in waves based on the similarity of goods between the sub-orders, that is, the sub-orders with high similarity in goods obtained by splitting different orders can be combined into the same wave.

[0037] For example, the similarity of items between sub-orders may be determined based on the proportion of identical items included in the sub-orders.

[0038] For example, assuming that sub-order 1 includes goods 1-2, and sub-order 2 also includes goods 1-2, the similarity between sub-order 1 and sub-order 2 may be 100%.

[0039] Assuming that sub-order 1 includes items 1 to 3, and sub-order 2 includes items 1 to 2 and 4, the similarity between sub-order 1 and sub-order 2 may be 67%.

[0040] For example, the higher the similarity of goods between sub-orders, the higher the priority of combining them into the same wave.

[0041] In the embodiment of the present application, when wave combination is performed in the manner described above, order allocation can be performed according to the wave, that is, the combined sub-orders are assigned to workstations for execution. Here, the assignment of sub-orders to workstations for execution can mean that the sub-orders are executed by a person or a robot (or both) at the workstation.

[0042] It can be seen that in the method flow shown in Figure 1, the correlation between the goods included in the original order is analyzed to determine the correlation between the goods, and the sub-orders in the original order are split according to the correlation between the goods to obtain the split sub-orders. Then, based on the similarity of the goods between the sub-orders, the sub-orders are combined in waves, and the combined sub-orders are assigned to workstations for execution according to the waves. Since when splitting the order, goods with high correlation are preferentially split into the same sub-order, and when combining the waves, sub-orders with high similarity of goods are preferentially combined into the same wave, the handling efficiency of the same goods in different orders can be improved, thereby effectively improving the order processing efficiency.

[0043] In some embodiments, performing the correlation analysis on the items in the original order to determine the correlation between the items may include:

[0044] Determine the correlation between the products based on the correlation parameters of the products;

[0045] The product relevance parameters include one or more of the following:

[0046] The name, category, and frequency of different products appearing in the same order.

[0047] For example, the correlation between the products may be determined based on one or more parameters such as product name, type, and the frequency of different products appearing in the same order.

[0048] For example, "toothbrush" and "toothpaste" have similar names and are both commonly used daily necessities, so their correlation is relatively high.

[0049] For another example, assuming there are 100 orders in total, and Item 1 and Item 2 appear in 90 orders at the same time, the correlation between Item 1 and Item 2 will be relatively high.

[0050] In one example, the correlation score between the items may be determined based on the frequency with which different items appear in the same order; wherein, the higher the correlation score, the higher the correlation between the items.

[0051] For example, assuming there are 100 orders in total, and Item 1 and Item 2 appear in 90 orders at the same time, their relevance score may be 0.9.

[0052] In some embodiments, the above-mentioned splitting of the original order based on the correlation between the goods may include:

[0053] Split the original order based on the relevance between products and order splitting restrictions;

[0054] The order splitting restriction conditions include at least one of the following: an upper limit on the volume of a single sub-order, or an upper limit on the number of sub-orders that can be split from a single original order.

[0055] In one example, in order to avoid the volume of a single sub-order (ie, the volume of goods included in the sub-order) being too large, the volume of the sub-order may also be considered when splitting the order.

[0056] Accordingly, the original order can be split according to the correlation between the goods and the upper limit of the volume of a single sub-order to prevent the volume of the sub-orders from exceeding the preset upper limit of the volume of a single sub-order.

[0057] In another example, in order to avoid an excessive number of sub-orders resulting from splitting a single order, the number of sub-orders may be considered when splitting the order.

[0058] Accordingly, the original order can be split based on the correlation between the goods and the upper limit of the number of sub-orders that can be split from a single original order, so as to avoid the number of sub-orders split from a single order exceeding the preset upper limit of the number of sub-orders that can be split from a single original order.

[0059] In another example, the original order may be split based on the correlation between goods, the upper limit of the volume of a single sub-order, and the upper limit of the number of sub-orders that can be split from a single original order.

[0060] In some embodiments, the wave combination of the multiple sub-orders split from the one or more original orders based on the similarity of goods between the sub-orders may include:

[0061] The multiple sub-orders are combined in waves according to the order priority from high to low and the similarity of the goods between the sub-orders.

[0062] For example, to ensure that high-priority orders are processed first, when wave grouping sub-orders, the sub-orders split from one or more original orders can be grouped in descending order of order priority and based on the similarity of the products between the sub-orders. Specifically, the sub-orders split from high-priority orders are prioritized for wave grouping. Consequently, high-priority orders can be preferentially assigned to workstations for execution.

[0063] For example, suppose order 1 includes items 1 to 4, order 2 includes items 1 to 2 and 5 to 6, and order 3 includes items 3 to 4 and 7 to 8. Order 1 is split into sub-order 11 (including items 1 to 2) and sub-order 12 (including items 3 to 4), order 2 is split into sub-order 21 (including items 1 to 2) and sub-order 22 (including items 5 to 6), and order 3 is split into sub-order 31 (including items 3 to 4) and sub-order 32 (including items 7 to 8). The similarity between sub-order 11 and sub-order 21 is 100%, the similarity between sub-order 12 and sub-order 31 is also 100%. When the priorities of orders 1 to 3 are the same, sub-order 11 and sub-order 21 can be grouped together (order 1 and order 2 can be grouped together), or sub-order 12 and sub-order 31 can be grouped together (order 1 and order 3 can be grouped together). When the priorities of order 1 and order 2 are higher than that of order 3, sub-order 11 and sub-order 21 can be grouped together, and accordingly, sub-order 12 and sub-order 22 can be grouped together.

[0064] In some embodiments, the wave combination of the multiple sub-orders split from the one or more original orders based on the similarity of goods between the sub-orders includes:

[0065] The multiple sub-orders are combined in waves based on the similarity of goods between the sub-orders and the number of order lines of each sub-order; wherein the difference between the number of order lines in different waves is less than a preset threshold.

[0066] For example, in the process of combining sub-orders in waves, in order to avoid excessive differences in the number of order lines in different waves, thereby leading to unbalanced allocation of resources and causing some work areas to be busy while other areas are idle, in the process of combining multiple sub-orders split from the one or more original orders in waves based on the similarity of goods between the sub-orders, it is also necessary to ensure that the number of order lines between waves is balanced as much as possible.

[0067] Accordingly, in the process of combining sub-orders into waves based on the similarity of goods between each sub-order, for any optional wave combination scheme, the difference between the number of order lines in each wave in the scheme can be determined. When the difference between the number of order lines in different waves is greater than or equal to a preset threshold, the wave combination scheme can be excluded, and then, a wave combination scheme in which the difference between the number of order lines in different waves is less than the preset threshold can be selected.

[0068] Still taking the above example, assuming that the similarity between sub-order 11 and sub-order 21 is 100%, and the similarity between sub-order 12 and sub-order 31 is also 100%, when the priorities of orders 1 to 3 are the same, the difference between the order line quantities of different waves when sub-order 11 and sub-order 21 are grouped (order 1 and order 2 are grouped), and the difference between the order line quantities of different waves when sub-order 12 and sub-order 31 are grouped (order 1 and order 3 are grouped), can be determined respectively; if the difference between the order line quantities of different waves of the former is greater than or equal to the preset threshold, and the difference between the order line quantities of different waves of the latter is less than or equal to the preset threshold, the latter grouping scheme can be selected.

[0069] In some embodiments, allocating the combined sub-orders by wave to the workstations for execution may include:

[0070] For different waves, the allocation order of each wave is determined based on the order priority within the wave and / or the order creation time within the wave;

[0071] For any wave to be allocated, the distance between each product and the workstation is determined based on the inventory of each product in the wave and the distribution of workstations. Based on the distance between each product and the workstation, orders are allocated according to the principle of proximity.

[0072] For example, in order to ensure that orders with high priority can be processed as much as possible, and / or that orders with early creation time can be processed as much as possible, in the process of allocating the combined sub-orders to workstations for execution by wave, for different waves, the allocation order of each wave can be determined based on the order priority within the wave and / or the order creation time within the wave.

[0073] For example, the priority of an order within a wave refers to the priority of a sub-order within the wave. The priority of a sub-order can be determined based on the priority of the order to which the sub-order belongs. For example, the priority of the order to which a sub-order belongs can be determined as the priority of the sub-order.

[0074] In one example, the priority of the sub-order with the highest priority in a wave may be determined as the priority of the wave.

[0075] In another example, a weighted average may be performed on the priorities of the sub-orders within a wave, and the obtained average value may be determined as the priority of the wave.

[0076] Taking the example of determining the priority of the highest-priority sub-order within a wave as the priority of the wave, for different waves, the priorities of these waves can be determined according to the priorities of the highest-priority sub-orders within these waves, and the allocation order of these waves can be determined in the order from highest to lowest priority.

[0077] For example, the implementation of order creation time is similar to that of priority.

[0078] For example, taking the example of determining the creation time of the earliest sub-order within a wave as the creation time of the wave, for different waves, the creation time of these waves can be determined according to the creation time of the earliest sub-order within these waves, and the order of the creation time of these waves from early to late can be determined as the allocation order of these waves.

[0079] When the allocation sequence of each wave is determined, the combined sub-orders can be allocated to workstations for execution in that order and by wave.

[0080] For any wave, the distance between each product and the workstation is determined based on the inventory of each product in the wave and the distribution of workstations. Orders are then allocated based on the proximity principle based on the distance between each product and the workstation.

[0081] In one example, the distance (such as average distance or total distance) between each product and different workstations can be determined based on the inventory of each product in the same wave, and the workstation with the shorter distance can be selected for order allocation to improve order processing efficiency.

[0082] For example, assuming that sub-orders 11 and 21 are combined into the same wave, both sub-orders 11 and 21 include item 1 and item 2, and the workstations include workstation 1 and workstation 2, then the distance from the inventory location of item 1 to workstation 1 (assuming D11) and the distance from the inventory location of item 1 to workstation 2 (assuming D12) can be determined separately, as well as the distance from the inventory location of item 2 to workstation 1 (assuming D21) and the distance from the inventory location of item 2 to workstation 2 (assuming D22). Then:

[0083] If D11+D21>D12+D22, sub-orders 11 and 21 can be assigned to workstation 2. If D11+D21<D12+D22, sub-orders 11 and 21 can be assigned to workstation 1. If D11+D21=D12+D22, other strategies can be used to determine whether to assign sub-orders 11 and 21 to workstation 1 or workstation 2. For example, based on the current workload of workstations 1 and 2, the workstation with the smaller workload can be assigned.

[0084] Illustratively, in an embodiment of the present application, for sub-orders in any wave assigned to workstations in the manner described above, during wave picking (i.e., after assigning sub-orders to workstations by wave, sub-orders are selected for processing), the picking order of each sub-order can be determined based on the order priority within the wave and / or the order creation time within the wave.

[0085] Exemplarily, the creation time of a sub-order may be determined based on the creation time of the order to which it belongs. For example, the creation time of the order to which a sub-order belongs may be determined as the creation time of the sub-order.

[0086] For example, within the same wave, sub-orders with higher priorities can be picked first; for sub-orders with the same priority, sub-orders created earlier can be picked first.

[0087] For example, within the same wave, sub-orders with earlier creation time can be picked first; for sub-orders with the same creation time, sub-orders with higher priority can be picked first.

[0088] It should be noted that in the implementation of this application, considering that there are other sub-orders being processed in the workstation, there may be idle task vehicles (such as material boxes) that cannot meet the needs of the sub-orders currently to be processed. In order to improve the efficiency of sub-order processing, sub-orders whose needs can be met by idle task vehicles can be given priority.

[0089] For example, for two sub-orders in the same wave (assuming they are sub-order 11 and sub-order 21), the picking order determined according to the above method is sub-order 11 first and sub-order 21 later. However, since there are other sub-orders being processed, the currently idle task vehicles cannot meet the needs of sub-order 11. In this case, if the currently idle task vehicles can meet the needs of sub-order 21, sub-order 21 can be processed first.

[0090] In addition, when picking sub-orders, the number of times the task vehicle is carried out, the transportation distance, etc. can also be considered, and sub-orders with fewer transportation times and shorter transportation distances can be processed first.

[0091] Furthermore, considering that there may be a certain overlap in the routes of different sub-orders during processing, if there are narrow areas such as alleys on the routes, congestion may occur, and then, abnormal sub-order processing may occur. Therefore, in the case of sub-order picking, this situation needs to be avoided as much as possible. For example, for any sub-order to be picked, the processing route of the sub-order overlaps with the processing route of a sub-order currently being processed, and there are narrow areas such as alleys on the route, then the sub-order to be picked can be processed after the processing of the currently processed sub-order is completed.

[0092] In order to enable those skilled in the art to better understand the technical solutions provided by the embodiments of the present application, the technical solutions provided by the embodiments of the present application are described below with reference to specific examples.

[0093] This embodiment provides a long order splitting method suitable for robot goods-to-person scenarios, which realizes the reorganization of multiple long orders after splitting, taking into account the sub-order overlap (i.e., the similarity of goods between sub-orders), packing volume (i.e., sub-order volume), inventory distribution, and task balance, to achieve optimized processing of the original long orders.

[0094] To facilitate understanding, a specific example is given below and the description is given in conjunction with the specific example.

[0095] Assume there are four original orders (as shown in Figure 2). Each order takes a long time to process (a single station takes 2 hours to process two orders). These orders are handled by two people. In the conventional goods-to-person model, each person is responsible for a picking station. Assuming each station can handle two orders simultaneously, and it takes two people 2 hours to complete this batch of long orders, orders O1 to O4 will each take 2 hours to complete.

[0096] Since each order has an execution time limit, for example, two orders (O1, O2) need to be processed within 1 hour, the orders need to be split and reorganized, and two people need to process orders O1 and O2 in 1 hour to ensure that they can be issued on time.

[0097] Among them, in order to ensure that orders O1 and O2 can be processed in a timely manner, a higher priority can be set for orders O1 and O2 (such as setting the highest priority supported by the system). Then, the priority of the wave obtained by splitting and combining orders O1 and O2 will also be the highest. When performing order allocation and wave picking, the sub-orders in orders O1 and O2 can be processed first.

[0098] Splitting an original order can result in a variety of outcomes. If the splitting is not controlled, it can lead to an increase in the number of robot handling operations during the actual operation. The splitting method provided in the embodiments of this application optimizes the splitting process, minimizing the total number of handling operations after the sub-orders are reassembled. For example, in Figure 2, compared to the optimized splitting, the robot handling times are higher with the standard split.

[0099] As shown in Figure 2, without order splitting, orders O1 and O2 include a, b, d, e, f, and g, a total of 6 different goods. Assigning orders O1 and O2 to workstation w1 for processing requires 6 moves; similarly, orders O3 and O4 also require 6 moves.

[0100] In the ordinary splitting scheme, the similarity of goods is not considered when splitting and grouping orders. The wave obtained by grouping sub-orders o11 and o21 (assuming it is wave 1) includes 3 different goods a, b, and e, and the wave obtained by grouping o12 and o22 (assuming it is wave 2) includes 3 different goods d, f, and g; the wave obtained by grouping o31 and o41 (assuming it is wave 3) includes 3 different goods a, b, and c; the wave obtained by grouping o32 and o42 (assuming it is wave 4) includes 4 different goods c, d, e, and g; Waves 1 and 3 are assigned to site w1 for processing. Site w1 needs to move goods 3 times when processing wave 1 and 3 times when processing wave 3, for a total of 6 times; Waves 2 and 4 are assigned to site w2 for processing. Site w2 needs to move goods 3 times when processing wave 2 and 7 times when processing wave 4, for a total of 13 times, and the number of moves increases.

[0101] In the optimized splitting scheme, the similarity of goods is taken into account when splitting and grouping orders. For orders O1 and O2, a and f are split into the same sub-order (o11 and o21), and the remaining goods are split into another sub-order (o12 and o22). O11 and o21 are grouped into waves (wave 1), and o12 and o22 are grouped into waves (wave 2). Similarly, for orders O3 and O4, b and c are split into the same sub-order (o32 and o42), and the remaining goods are split into two sub-orders (o32 and o42). Divide it into another sub-order (o31 and o41), and group the waves to obtain wave 3 and wave 4; in order to ensure that orders O1 and O2 can be completed within 1 hour, wave 1 and wave 2 are assigned to sites w1 and w2 for processing respectively. For sites w1 and w2 to process wave 1 and wave 2, 6 moves are required; wave 3 and wave 4 are assigned to sites w1 and w2 for processing respectively. For sites w1 and w2 to process wave 3 and wave 4, 6 moves are required, that is, a total of 12 moves.

[0102] It can be seen that compared with the ordinary splitting scheme, the number of transfers is reduced from 13 to 12. In addition, in the scheme without splitting, orders O1 and O2 are processed by site w1 on a single line, which takes 2 hours to complete. In the optimized splitting scheme, by splitting and combining orders O1 and O2 in waves, the waves corresponding to orders O1 and O2 are respectively assigned to sites w1 and w2 for dual-line processing, which can be completed in 1 hour.

[0103] As shown in Figure 2, by adopting the order splitting solution provided in the embodiment of the present application, orders O1 and O2 can be processed within 1 hour while the total number of transfers is increased as little as possible (compared to before the split), that is, the total number of transfers can be increased as little as possible while ensuring that O1 and O2 are processed on time.

[0104] In actual order splitting, the original order has more items, the original order pool is larger, and the corresponding warehouse area and the number of workstations are also larger. Therefore, the specific order splitting and the allocation of sub-orders after splitting need to consider, but are not limited to, the following factors:

[0105] 1. Sub-order size limit and / or sub-order number limit;

[0106] 2. Sub-order inventory distribution;

[0107] 3. Original order task priority.

[0108] In this embodiment, the specific process of order processing may be as shown in FIG3 , including the following steps S310 - S360 .

[0109] S310. Obtain n original orders through the original orders issued by the upstream. A single order is represented as: Oi (i=1, 2, 3, ..., n).

[0110] S320. Based on the sub-order volume limit Vm and / or the maximum number of sub-orders that can be split from a single order S, the number of split orders Si for each original order can be obtained.

[0111] Where Vi is the total volume of the i-th original order.

[0112] S330: Perform a correlation analysis on all SKUs (one SKU identifies one category of goods) involved in the original order obtained to obtain a correlation index of the SKU.

[0113] S340: Based on the SKU correlation index, prioritize the classification of highly correlated SKUs within the original order into a sub-order. Based on this principle, split each original order. The splitting process considers multiple constraints (including but not limited to the following):

[0114] Wherein, Vij is the jth (j=1, 2, 3, ...) sub-order into which the i-th original order is split.

[0115] S350. Group the sub-orders into waves (wave combination) based on the similarity between the sub-orders, and group the sub-orders into the same wave on the principle of giving priority to grouping sub-orders with similar contents into the same wave.

[0116] Among them, sub-orders with high priority (sub-orders obtained by splitting orders with high priority) are given priority in grouping.

[0117] S6. Assign the combined sub-orders to workstations for execution according to the wave.

[0118] Exemplarily, for different waves, the allocation order of each wave is determined based on the order priority within the wave and / or the order creation time within the wave.

[0119] For example, for any wave to be allocated, order allocation takes into account the spatial relationship between the SKU inventory and workstation distribution within the wave order, and gives priority to allocation based on proximity, that is, order allocation is performed based on the principle of proximity according to the distance of each product to different workstations.

[0120] The method provided by this application is described above. The device provided by this application is described below.

[0121] Please refer to FIG4 , which is a schematic diagram of the structure of an order processing device provided in an embodiment of the present application. As shown in FIG4 , the order processing device may include:

[0122] An acquisition unit 410 is configured to acquire information of one or more original orders;

[0123] The determining unit 420 is configured to perform a correlation analysis on the items in each of the one or more original orders to determine the correlation between the items;

[0124] The splitting unit 430 is configured to split the original order based on the correlation between the items to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the items, the higher the probability that the items will be split into the same sub-order;

[0125] A combining unit 440 is configured to combine the plurality of sub-orders split from the one or more original orders into waves based on the similarity of goods between the sub-orders; wherein the higher the similarity of goods between the plurality of sub-orders, the higher the probability that the plurality of sub-orders are combined into the same wave;

[0126] The allocation unit 450 is used to allocate the combined sub-orders to workstations for execution according to waves.

[0127] In some embodiments, the determining unit 420 performs a correlation analysis on the items in the original order to determine the correlation between the items, including:

[0128] Determine the correlation between the products based on the correlation parameters of the products;

[0129] The product relevance parameters include one or more of the following:

[0130] The name, category, and frequency of different products appearing in the same order.

[0131] In some embodiments, the splitting unit 430 splits the original order based on the correlation between the goods, including:

[0132] Split the original order based on the relevance between products and order splitting restrictions;

[0133] The order splitting restriction condition includes at least one of the following: an upper limit on the volume of a single sub-order, or an upper limit on the number of sub-orders that can be split from a single original order.

[0134] In some embodiments, the combining unit 440 performs wave combining on the multiple sub-orders split from the one or more original orders based on the similarity of goods between the sub-orders, including:

[0135] The multiple sub-orders are combined in waves according to the order priority from high to low and the similarity of the goods between the sub-orders.

[0136] In some embodiments, the combining unit 440 performs wave combining on the multiple sub-orders split from the one or more original orders based on the similarity of goods between the sub-orders, including:

[0137] The multiple sub-orders are combined in waves based on the similarity of goods between the sub-orders and the number of order lines of each sub-order; wherein the difference between the number of order lines in different waves is less than a preset threshold.

[0138] In some embodiments, the allocating unit 450 allocates the combined sub-orders to workstations for execution in waves, including:

[0139] For different waves, the allocation order of each wave is determined based on the order priority within the wave and / or the order creation time within the wave;

[0140] For any wave to be allocated, the distance between each product and the workstation is determined based on the inventory of each product in the wave and the distribution of workstations. Based on the distance between each product and the workstation, the combined sub-orders are allocated to the workstation for execution according to the principle of proximity.

[0141] An embodiment of the present application provides an electronic device, including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor is used to execute the machine-executable instructions to implement the order processing method described above.

[0142] Please refer to Figure 5, which is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. The electronic device may include a processor 501 and a memory 502 storing machine-executable instructions. The processor 501 and the memory 502 may communicate via a system bus 503. Furthermore, by reading and executing the machine-executable instructions corresponding to the order processing logic in the memory 502, the processor 501 may execute the order processing method described above.

[0143] The memory 502 mentioned herein may be any electronic, magnetic, optical, or other physical storage device that may contain or store information, such as executable instructions, data, and the like. For example, the machine-readable storage medium may be: RAM (Random Access Memory), volatile memory, non-volatile memory, flash memory, a storage drive (such as a hard disk drive), a solid-state drive, any type of storage disk (such as a CD, DVD, etc.), or similar storage media, or a combination thereof.

[0144] In some embodiments, a machine-readable storage medium is further provided, such as memory 502 in FIG. 5 . The machine-readable storage medium stores machine-executable instructions that, when executed by a processor, implement the order processing method described above. For example, the storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical data storage device.

[0145] It should be noted that, in this document, relational terms such as "first", "second" and target are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0146] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. An order processing method, comprising: Get information about one or more original orders; For each of the one or more original orders, Performing correlation analysis on the goods of the original order to determine the correlation between the goods; The original order is split according to the correlation between the goods to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the goods, the higher the possibility that the goods are split into the same sub-order; Combining the multiple sub-orders split from the one or more original orders in waves according to the similarity of goods between the sub-orders; and The combined sub-orders are assigned to workstations for execution in waves; wherein, the higher the similarity of goods between the multiple sub-orders, the higher the possibility that the multiple sub-orders are combined into the same wave.

2. The method according to claim 1, characterized in that The performing correlation analysis on the goods included in the original order to determine the correlation between the goods includes: Determining the correlation between the goods according to the correlation parameters of the goods; The product relevance parameters include one or more of the following: name, category, and frequency of occurrence of different products in the same order.

3. The method according to claim 1, characterized in that The splitting of the original order according to the correlation between the goods includes: Split the original order according to the correlation between the goods and the order splitting restriction conditions; The order splitting restriction condition includes at least one of the following: an upper limit on the volume of a single sub-order, or an upper limit on the number of sub-orders that can be split from a single original order.

4. The method according to claim 1, characterized in that The wave combining of the multiple sub-orders split from the one or more original orders according to the similarity of goods between the sub-orders includes: In order of order priority from high to low, and based on the similarity of goods between the sub-orders, the multiple sub-orders are combined in waves.

5. The method according to claim 1 or 4, characterized in that: The wave combining of the multiple sub-orders split from the one or more original orders according to the similarity of goods between the sub-orders includes: Combining the multiple sub-orders in waves according to the similarity of goods between the sub-orders and the number of order lines of each sub-order; Among them, the difference between the quantity of order lines in different waves is less than the preset threshold.

6. The method according to claim 1, characterized in that The method of allocating the combined sub-orders to workstations for execution according to waves includes: For different waves, the allocation order of each wave is determined based on the order priority within the wave and / or the order creation time within the wave; For any wave to be allocated, the distances from each product to different workstations are determined based on the inventory of each product in the wave and the distribution of workstations. Based on the distances from each product to different workstations, the combined sub-orders are allocated to the workstations for execution according to the proximity principle.

7. An order processing device, characterized in that: include: An acquisition unit, used for acquiring information of one or more original orders; A determination unit, configured to perform a correlation analysis on the goods of each of the one or more original orders to determine the correlation between the goods; A splitting unit, used to split the original order according to the correlation between the goods to obtain at least one sub-order; wherein, in the same order, the higher the correlation between the goods, the higher the possibility that the goods are split into the same sub-order; A combining unit, configured to combine the multiple sub-orders split from the one or more original orders into waves according to the similarity of goods between the sub-orders; wherein the higher the similarity of goods between the multiple sub-orders, the higher the possibility that the multiple sub-orders are combined into the same wave; Allocation unit, used to allocate the combined sub-orders to workstations for execution in waves.

8. The device according to claim 7, characterized in that The determining unit performs a correlation analysis on the goods of the original order to determine the correlation between the goods, including: Determining the correlation between the goods according to the correlation parameters of the goods; The product relevance parameters include one or more of the following: The name, category, and frequency of different items appearing in the same order.

9. The device according to claim 7, characterized in that The splitting unit splits the original order according to the correlation between the goods, including: Split the original order according to the correlation between the goods and the order splitting restriction conditions; The order splitting restriction condition includes at least one of the following: an upper limit on the volume of a single sub-order, or an upper limit on the number of sub-orders that can be split from a single original order.

10. The device according to claim 7, characterized in that The combining unit performs wave combining on the multiple sub-orders split from the one or more original orders according to the similarity of goods between the sub-orders, including: In order of order priority from high to low, according to the similarity of goods between the sub-orders, the multiple Sub-orders are combined in waves.

11. The device according to claim 7 or 10, characterized in that The combining unit performs wave combining on the multiple sub-orders split from the one or more original orders according to the similarity of goods between the sub-orders, including: The multiple sub-orders are combined in waves according to the similarity of goods between the sub-orders and the number of order lines of each sub-order; wherein the difference between the number of order lines of different waves is less than a preset threshold.

12. The device according to claim 7, characterized in that The allocation unit allocates the combined sub-orders to the workstations for execution according to the wave, including: For different waves, the allocation order of each wave is determined based on the order priority within the wave and / or the order creation time within the wave; For any wave to be allocated, the distances from each product to different workstations are determined based on the inventory of each product in the wave and the distribution of workstations. Based on the distances from each product to different workstations, the combined sub-orders are allocated to the workstations for execution according to the proximity principle.

13. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores machine executable instructions that can be executed by the processor, and the processor is used to execute the machine executable instructions to implement the method according to any one of claims 1 to 6.

14. A machine-readable storage medium, characterized in that: The machine-readable storage medium stores machine-executable instructions, and when the machine-executable instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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