Order processing method and device, equipment and readable medium

Abstract

The embodiment of the invention provides an order processing method and device, equipment and a medium. The method comprises: based on a plurality of orders, a first number of to-be-created order sets and a second number of orders included in the to-be-created order sets, allocating the plurality of orders to obtain a first number of first candidate order sets, each first candidate order set comprising a second number of orders; based on the first candidate order set and the orders not allocated to the first candidate order set in the plurality of orders, determining a second candidate order set, the second candidate order set comprising the orders not allocated to the first candidate order set and at least one order in the first order set; and obtaining a first number of target order sets based on the first candidate order set and the second candidate order set by using a planning model, each target order set comprising a second number of orders. Therefore, the order processing efficiency can be improved.

Description

Technical Field

[0001] Example embodiments of the present disclosure generally relate to the field of computer technology, and more specifically, to an order processing method, apparatus, device, and computer-readable storage medium. Background Art

[0002] With the rapid development of the e-commerce industry, the number of e-commerce orders has shown a high growth trend. For example, during the promotional activities held by major e-commerce platforms, the number of orders on e-commerce websites can reach tens of millions per day. Therefore, how to improve the efficiency of order processing is worthy of attention and urgent need. Summary of the invention

[0003] In a first aspect of the present disclosure, there is provided an order processing method, comprising: allocating the multiple orders to obtain a first number of first candidate order sets based on multiple orders, a first number of an order set to be created, and a second number of orders included in the order set to be created, each first candidate order set including a second number of orders; determining a second candidate order set based on the first candidate order set and orders in the multiple orders that are not allocated to the first candidate order set, the second candidate order set including orders that are not allocated to the first candidate order set and at least one order in the first order set; and obtaining a first number of target order sets based on the first candidate order set and the second candidate order set using a planning model, each target order set including the second number of orders.

[0004] In a second aspect of the present disclosure, an order processing device is provided, comprising: a first allocation module, configured to allocate multiple orders to obtain a first quantity of first candidate order sets based on multiple orders, a first quantity of an order set to be created, and a second quantity of orders included in the order set to be created, each first candidate order set including a second quantity of orders; a determination module, configured to determine a second candidate order set based on the first candidate order set and orders in the multiple orders that are not allocated to the first candidate order set, the second candidate order set including orders that are not allocated to the first candidate order set and at least one order in the first order set; and a second allocation module, configured to obtain a first quantity of target order sets based on the first candidate order set and the second candidate order set using a planning model, each target order set including a second quantity of orders.

[0005] In a third aspect of the present disclosure, an electronic device is provided. The electronic device includes at least one processing unit; and at least one memory, the at least one memory is coupled to the at least one processing unit and stores instructions for execution by the at least one processing unit, and when the instructions are executed by the at least one processing unit, the electronic device executes the method of the first aspect of the present disclosure.

[0006] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, wherein a computer program is stored on the computer-readable storage medium, and the computer program can be executed by a processor to perform the method according to the first aspect of the present disclosure.

[0007] In a fifth aspect of the present disclosure, a computer program product is provided, comprising computer executable instructions, wherein the computer executable instructions implement the method according to the first aspect of the present disclosure when executed by a processor.

[0008] It should be understood that the contents described in the summary of the invention are not intended to limit the key features or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In the following, in conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages and aspects of various implementations of the present disclosure will become more apparent. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0010] Figure 1 A schematic diagram showing an example environment in which embodiments of the present disclosure can be implemented;

[0011] Figure 2 A schematic diagram showing an interface for order screening according to some embodiments of the present disclosure is shown;

[0012] Figure 3 A flowchart showing a process of determining a target order set according to some embodiments of the present disclosure is shown;

[0013] Figure 4 A flowchart showing a process of order processing according to some embodiments of the present disclosure;

[0014] Figure 5 A block diagram showing an apparatus for order processing according to some embodiments of the present disclosure; and

[0015] Figure 6 A block diagram of an electronic device is shown in which one or more embodiments of the present disclosure may be implemented. DETAILED DESCRIPTION

[0016] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0017] In the description of the embodiments of the present disclosure, the term "including" and similar terms should be understood as open inclusion, that is, "including but not limited to". The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The term "some embodiments" should be understood as "at least some embodiments". Other explicit and implicit definitions may also be included below.

[0018] It should be noted that the acquisition, storage and application of user personal information involved in the technical solution of the present disclosure are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0019] It is understandable that before using the technical solutions disclosed in the embodiments of the present disclosure, the types, scope of use, usage scenarios, etc. of the personal information involved in the present disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0020] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested to be performed will require obtaining and using the user's personal information, so that the user can independently choose whether to provide personal information to software or hardware such as electronic devices, applications, servers or storage media that execute operations of the technical solution of the present disclosure based on the prompt message.

[0021] As an optional but non-limiting implementation, in response to receiving an active request from the user, the prompt information is sent to the user in a manner such as a pop-up window, in which the prompt information can be presented in text form. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0022] As briefly mentioned above, with the rapid development of the e-commerce industry, the volume of e-commerce orders has shown a high growth trend. For example, during promotional activities held by major e-commerce platforms, the daily order volume of e-commerce websites can reach tens of millions. In order to meet consumers' requirements for order timeliness, e-commerce companies adopt a warehousing and distribution system, establish multiple warehouses across the country, and select the nearest warehouse for shipment based on the distance from the consumer's location to achieve the goal of same-day or next-day arrival.

[0023] In the warehouse, goods are placed on shelves according to certain rules and / or order to facilitate warehouse management and picking according to orders. However, since e-commerce orders usually involve smaller quantities and pieces, if a single order is used as an independent picking task, it will lead to low picking efficiency and waste of human resources. To solve this problem, multiple orders can be combined into a picking task list (hereinafter referred to as "order set"), and picking is performed according to the order set, which significantly improves the picking efficiency.

[0024] In some scenarios, the orders included in the order set can be determined based on the maximum volume and maximum number of orders in the order set. For example, when the order set can still accommodate orders, the orders can be sorted from near to far according to the distance of the goods corresponding to the orders from the workbench, and the orders can be added to the order set in sequence. When the order set can no longer accommodate orders, a complete order set is generated.

[0025] In this way, since an order may contain multiple products at the same time and different products are in different locations in the warehouse, it is difficult to measure the distance between the products corresponding to the order and the workbench, resulting in poor results in the generated order set.

[0026] In some scenarios, a mathematical model (eg, a planning model) may also be established to calculate the order set. For example, if there are 240 orders to be processed, 6 order sets need to be generated, and the calculation takes more than 2 hours.

[0027] In this way, since it takes a long time to calculate the model, it cannot meet the needs of real-time scenarios.

[0028] The embodiments of the present disclosure provide an improved solution for order processing. In various embodiments of the present disclosure, based on multiple orders, a first number of order sets to be created, and a second number of orders contained in the order set to be created, multiple orders are allocated to obtain a first number of first candidate order sets, each of which includes a second number of orders; based on the first candidate order set and orders in the multiple orders that are not allocated to the first candidate order set, a second candidate order set is determined, the second candidate order set includes orders that are not allocated to the first candidate order set and at least one order in the first order set; and using a planning model, based on the first candidate order set and the second candidate order set, a first number of target order sets are obtained, each of which includes a second number of orders. Thus, the first candidate order set and the second candidate order set can be determined first to reduce the number of decision variables of the planning model, and then the planning model can be used to obtain the target order set based on the first candidate order set and the second candidate order set, thereby improving the efficiency of order processing.

[0029] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0030] Figure 1 1 is a schematic diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. Figure 1 As shown, the environment 100 may include terminal devices 110-1, 110-2, ..., 110-N, a server 120, pickers 130-1, 130-2, ..., 130-N, and shelves 140-1, 140-2, ..., 140-N.

[0031] For the convenience of description, the terminal devices 110-1, 110-2, ..., 110-N may be collectively or individually referred to as terminal devices 110, the pickers 130-1, 130-2, ..., 130-N may be collectively or individually referred to as pickers 130, and the shelves 140-1, 140-2, ..., 140-N may be collectively or individually referred to as shelves 140.

[0032] Various communication client applications may be installed on the terminal device 110 , such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0033] A communication connection may be established between the terminal device 110 and the server 120. The communication connection may be established in a wired manner or a wireless manner. The communication connection may include, but is not limited to, a Bluetooth connection, a mobile network connection, a Universal Serial Bus (USB) connection, a Wireless Fidelity (WiFi) connection, etc., and the embodiments of the present disclosure are not limited in this respect.

[0034] The user can use the terminal device 110 to interact with the server 120. For example, the user sends order information to the server 120 through a shopping application in the terminal device 110. The server 120 can instruct the picker 130 to pick goods from the shelf 140 based on the order information.

[0035] In some embodiments, the picker 130 may be a picker. In other embodiments, the picker 130 may be a picker device, such as a picker truck, a robot, etc.

[0036] In some embodiments, the terminal device 110 can be any type of device with computing capabilities, including a terminal device or a server device. The terminal device can be any type of mobile terminal, fixed terminal or portable terminal, including a mobile phone, a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a media computer, a multimedia tablet, a personal communication system (PCS) device, a personal navigation device, a personal digital assistant (PDA), an audio / video player, a digital camera / camcorder, a positioning device, a television receiver, a radio broadcast receiver, an e-book device, a gaming device, or any combination of the foregoing, including accessories and peripherals of these devices or any combination thereof. The server device may include, for example, a computing system / server, such as a mainframe, an edge computing node, an electronic device in a cloud environment, and the like.

[0037] In some embodiments, the server 120 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, and big data and artificial intelligence platforms. The server 120 may include, for example, a computing system / server, such as a mainframe, an edge computing node, a computing device in a cloud environment, and the like. The server 120 may provide background services for shopping applications in the terminal device 110.

[0038] It should be understood that Figure 1 The components and arrangements in the environment 100 shown are merely examples, and a computing system suitable for implementing the exemplary implementation described in the present disclosure may include one or more different components, other components, and / or different arrangements. The implementation of the present disclosure is not limited in this respect.

[0039] The following will continue to describe some exemplary embodiments of the present disclosure with reference to the accompanying drawings. Figure 1 For example, the order processing process can be implemented by the server 120.

[0040] Figure 2 FIG. 2 is a schematic diagram showing an example of an interface 200 for order screening according to some embodiments of the present disclosure. Figure 2As shown, the server 120 may present an interface 200, and the user may perform a filtering operation on the interface 200 to filter the desired orders. The user may configure the conditions for querying orders on the interface 200. For example, the order time range, delivery area, merchant name, etc. The user may also configure the number of order sets to be created (order set number) and the number of orders contained in each order set to be created (order set number) on the interface 200.

[0041] The server 120 may generate a corresponding number of order sets based on the orders screened by the user and in combination with the number of order sets to be created set by the user and the number of orders included in each order set to be created.

[0042] Next, the generation of an order set by the server 120 is described in detail with reference to a specific example.

[0043] In some embodiments, all shelves 140 in the warehouse may be numbered to facilitate picking by the picker 130. For example, the server 120 may set the picking path to be S-shaped or U-shaped (i.e., the driving route of the picker 130 in the shelf area is S-shaped or U-shaped), and the server 120 may number all shelves 140 in the warehouse according to the order of the picking path. In this way, the difference in the numerical values ​​of the shelf numbers corresponding to different shelves 140 may be used to represent the relative distance between the shelves 140.

[0044] Before generating an order set, the server 120 needs to establish a planning model. For example, the planning model may be a mixed integer programming model (Mixed Integer Programming, MIP). In some embodiments, the server 120 may construct an objective function based on the picking paths of multiple order sets, or may construct a constraint function based on at least one constraint condition for multiple order sets, and construct a planning model based on the objective function and the constraint function.

[0045] In an embodiment of the present disclosure, the picking path indicates the sum of shelf numbers corresponding to objects included in multiple order sets, and the objective function is configured to make the picking path shortest.

[0046] First, define the following symbols and variables:

[0047] m, the number of orders filtered by the user;

[0048] q i , the number of items included in order i;

[0049] a i , the minimum shelf order corresponding to the products contained in order i;

[0050] bi , the maximum shelf order corresponding to the products contained in order i;

[0051] P, the maximum order quantity of the order set;

[0052] Q, the maximum number of items in the order set;

[0053] V, the maximum volume of the order set;

[0054] n, the number of order sets to be created;

[0055] x ij , when order i belongs to order set j, it is equal to 1, otherwise it is equal to 0;

[0056] y j , if there is an order in order set j, it is equal to 1, otherwise it is equal to 0; if there is y j =0, the number of order sets actually generated is less than the number of order sets that need to be created;

[0057] λ j , the minimum shelf order among the shelf orders corresponding to all the products corresponding to all the orders contained in the order set j;

[0058] μ j , the maximum shelf order among the shelf orders corresponding to all the products corresponding to all the orders contained in the order set j.

[0059] Thus, the server 120 can construct the following objective function based on the above symbols and variables:

[0060] In some embodiments, the constraints may include at least one of the following: the number of order sets to be created; giving priority to generating order sets with smaller shelf numbers; when an order set contains orders, the order set is enabled; the number of orders contained in the order set to be created; the number of objects contained in all order sets; the volume of objects contained in all order sets; the size relationship between the maximum shelf sequence and the minimum shelf sequence corresponding to the order set; the maximum shelf sequence corresponding to the picking path; and the minimum shelf sequence corresponding to the order set.

[0061] The server 120 may construct a constraint function based on the above constraint conditions, symbols and variables as follows:

[0062] The number of order sets to be created:

[0063] Prioritize the generation of order sets with smaller shelf numbers: y i ≥y i+1i=1,2,…,n-1

[0064] An order collection is enabled when it contains orders:

[0065] Maximum order quantity limit for order collection:

[0066] The limit on the number of objects contained in the order collection:

[0067] The volume limit of objects contained in the order collection:

[0068] The relationship between the maximum shelf order and the minimum shelf order corresponding to the order set: μ j ≤λ j j=1,2,…,m

[0069] The maximum shelf sequence corresponding to the picking path: b i ·x ij ≤λ i i=1,2,…,m

[0070] The minimum shelf order corresponding to the order set: a i ·x ij ≥μ i i=1,2,…,m

[0071] in addition, x ij ∈{0,1}i=1,2,…,mj=1,2,…,n y j ∈{0,1}j=1,2,…,n

[0072] For the above-mentioned planning model, the server 120 can obtain an initial solution for allocating orders to form an order set by gradually expanding the heuristic algorithm of the channel, and then use a mixed method of the large-scale neighborhood search algorithm and the planning model to perform iterative calculations to obtain the best solution for allocating orders to form an order set.

[0073] In some embodiments, server 120 may allocate multiple orders based on multiple orders, a first quantity of an order set to be created, and a second quantity of orders included in the order set to be created to obtain a first quantity of first candidate order sets, each first candidate order set including a second quantity of orders, to obtain an initial plan for allocating orders to form an order set.

[0074] Specifically, the server 120 can number the shelves based on a predetermined picking path and determine the shelf numbers as a shelf order; then obtain order information of each of the multiple orders, the order information including the codes and number of objects included in the orders; finally, based on the order information, the shelf order, the first quantity and the second quantity, allocate the multiple orders to obtain a first candidate order set of the first quantity, and make each first candidate order set include the second quantity of orders.

[0075] Each order may include one or more objects, such as commodities, and each commodity may have a corresponding commodity code. For example, an order may include one or more commodities, and the corresponding codes and quantity of the one or more commodities.

[0076] The server 120 can determine the number of pieces corresponding to all the commodities included in the multiple orders through the order information, and determine the shelf order of the shelf 140 to which the commodity corresponding to the largest number belongs based on the serial numbers of the commodities stored on the shelf 140 .

[0077] For example, all the shelves in the warehouse are U = {U 1 ,U 2 ,…,U s}, the set of multiple orders to be generated is Q = {Q 1 ,Q 2 ,…,Q t}, order i∈Q. The number of items on shelf j corresponding to order i is w ij , we can determine the number of items in the order set placed on each shelf j as: Place shelf 140 according to the number of items V j Sorting, for example, can be sorted in descending order. Assume that the shelf with the largest number of pieces is U k , and the picking order corresponding to this shelf is L.

[0078] The server 120 may determine the minimum order and the maximum order of the selected order based on the shelf order of the shelf 140 to which the goods corresponding to the largest number of pieces belong. For example, if the shelf order of the shelf 140 to which the goods corresponding to the largest number of pieces belong is L, then the minimum order L of the selected order is determined. x =L, maximum order L y =L.

[0079] The server 120 allocates the multiple orders based on the minimum order and the maximum order to obtain a first candidate order set. For example, if the shelf order corresponding to one or more orders among the multiple orders is greater than or equal to the minimum order and less than or equal to the maximum order, the one or more orders are used as the first candidate order set.

[0080] If the number of the first candidate order set is less than or equal to the first number, and / or the number of orders included in the first candidate order set is less than or equal to the second number, adjust the minimum order and the maximum order by a preset step size; based on the adjusted minimum order and the maximum order, continue to allocate unallocated orders in the multiple orders; and repeat the above steps until the number of the first candidate order set is equal to the first number, and the number of orders included in the first candidate order set is equal to the second number.

[0081] For example, suppose there are 6 products, and their corresponding shelves and shelf order are shown in Table 1: Table 1

[0082] Assume there are 7 orders, and the order information is shown in Table 2 below: Table 2 Order Number Product code and quantity Q1 A*1 Q2 A*1+B*1 Q3 B*1+E*1 Q4 C*1+D*2 Q5 C*2+E*2 Q6 D*2 Q7 F*2

[0083] In Table 2, A*1+B*1 means that order Q2 contains 1 item A and 1 item B.

[0084] Suppose you need to generate an order set, and the maximum number of orders in the order set is 4. Among these 7 orders, the number of items on shelf U4 where item D is located is the largest, so the shelf order is 4. Select the minimum order L of the order x =4, select the maximum order L of the order y =4, at this time, only the shelf order of the goods in order Q6 satisfies the shelf order corresponding to the order, which is greater than or equal to the minimum order and less than or equal to the maximum order. At this time, the number of orders in the order set does not reach 4, so the minimum order of the selected order is reduced and the maximum order of the selected order is increased. For example, the minimum order of the selected order L x =3, select the maximum order L of the order y =5, at this time, the goods C, D, and E on the shelves U3, U4, and U5 meet the requirements. At this time, orders Q4, Q5, and Q6 can be added to the order set.

[0085] Continue to make the minimum order L of the selected order x =2, select the maximum order L of the order y =6, at this time, goods B, C, D, E, F on shelves U2, U3, U4, and U5 meet the requirements. At this time, orders Q3, Q4, Q5, Q6, and Q7 meet the requirements. We can select 4 orders in the order of shelves that meet the minimum requirements, and finally select orders Q3, Q4, Q5, and Q6 to generate an order set.

[0086] The server 120 may determine a second candidate order set based on the first candidate order set and orders in the plurality of orders not allocated to the first candidate order set. The second candidate order set includes orders not allocated to the first candidate order set and at least one order in the first order set.

[0087] The server 120 may also utilize the planning model to obtain a first number of target order sets based on the first candidate order set and the second candidate order set, each target order set including a second number of orders.

[0088] Figure 3 A flowchart of a process 300 of determining a target order set according to some embodiments of the present disclosure is shown. The process 300 may be implemented at the server 120.

[0089] At block 310 , the server 120 determines a number of orders in the plurality of orders that are not allocated to the first candidate order set (unallocated orders).

[0090] In block 320 , when the number of orders to be allocated is not 0, the server 120 selects some orders from the orders to be allocated to form group A orders, and the remaining orders are group B orders.

[0091] In some embodiments, the server 120 removes at most a predetermined number (eg, K) of orders from the first candidate order set, and determines a second candidate order set based on the orders removed from the first candidate order set and the orders to be allocated.

[0092] In some embodiments, the server 120 can determine the shelf distance of each order in the first candidate order set to the first candidate order set, sort all orders in the first candidate order set in order of shelf distance from small to large, and remove up to a predetermined number of orders from the first candidate order set according to the sorting result of the first candidate order set.

[0093] In some embodiments, the server 120 determines a first shelf distance between the minimum shelf sequence corresponding to the first candidate order set and the minimum shelf sequence corresponding to the order, determines a second shelf distance between the maximum shelf sequence corresponding to the first candidate order set and the maximum shelf sequence corresponding to the order, and determines the minimum distance between the first shelf distance and the second shelf distance as the shelf distance from the order to the first candidate order set.

[0094] For example, suppose the minimum shelf order corresponding to the first candidate order set is L min , the minimum shelf order corresponding to the order is l x , the maximum shelf order corresponding to the first candidate order set is L max , the minimum shelf order corresponding to the order is l y, then the distance from each order in the first candidate order set to the shelf of the first candidate order set is: d=min(|L min -l x |,|L max -l y |)

[0095] The server 120 may sort all orders in the first candidate order set in ascending order based on the shelf distance d, and remove at most a predetermined number of orders from the first candidate order set according to the sorting result of the first candidate order set.

[0096] In some embodiments, the server 120 obtains a random number within a predetermined range, determines the number of orders to be removed based on the number of orders included in the first candidate order set and the random number, and removes a corresponding number of orders from the first candidate order set based on the number of orders to be removed according to the sorting result of the first candidate order set. The number of orders to be removed is less than or equal to the predetermined number.

[0097] For example, the predetermined range may be [0.12, 0.2], and the server 120 may determine the number of orders that need to be removed based on the product of a random number within the range and the number of orders included in the first candidate order set.

[0098] The server 120 may combine the orders removed from the first candidate order set and the orders to be allocated to form a second candidate order set.

[0099] In some embodiments, server 120 can determine the first order concentration of the first candidate order set, determine the second order concentration of the first candidate order set after adding each order in the second candidate order set to the first candidate order set, determine the order concentration change value corresponding to each order in the second candidate order set based on the first order concentration and the second order concentration, sort the orders in the second candidate order set in order of the order concentration change values ​​from large to small, and select a predetermined number of orders from the sorted orders.

[0100] In some embodiments, the first order concentration of the first candidate order set can be determined in the following manner: the server 120 determines the sum of the first shelf distances of each order based on the minimum shelf order and the maximum shelf order of each order in the first candidate order set, and then determines the sum of the second shelf distances between the minimum shelf order and the maximum shelf order corresponding to the first candidate order set, and finally determines the first order concentration based on the sum of the first shelf distances, the sum of the second shelf distances and the first quantity.

[0101] For example, the order concentration of an order set can be calculated by the following formula:

[0102] Among them, l ix represents the minimum shelf order of orders in the order set, l iy represents the maximum shelf order of orders in the order set, L min Represents the minimum shelf order of the order set, L max represents the maximum shelf order of the order set, and n represents the number of order sets to be created.

[0103] The order concentration change value of an order is the change value of the concentration before and after the order is added to a certain order set. The server 120 can determine the order concentration change value corresponding to each order in the second candidate order set based on the above formula, and sort the orders in the second candidate order set in descending order of the order concentration change value, and select a predetermined number of orders from the sorted orders as group A orders, and the remaining orders as group B orders.

[0104] In block 330 , the server 120 allocates the orders in group A of orders to a first set of candidate orders for removal in an optimal solution.

[0105] In some embodiments, the server 120 may select a predetermined number of orders from the second candidate order set, and utilize the planning model to obtain a first number of target order sets based on the orders selected from the second candidate order set and the first candidate order set.

[0106] For example, server 120 takes the first candidate order set after removing the order as the decided items, and the orders in group A as the items to be decided. Server 120 uses the planning model to assign the order values ​​in group A to the first candidate order set after removing the order as the target order set.

[0107] In box 340, the server 120 determines whether all the orders in group A have been allocated. If not, the orders in group B are treated as orders to be allocated, and the above process is repeated until all the orders in group A are allocated.

[0108] If too many orders are selected, the MIP model will take more time to solve, and the number of final iterations will be small. If few orders are selected, the improvement of a single optimization is small, but the number of iterative solutions will be large. In the embodiment of the present disclosure, for the predetermined number K, the value of K can be verified by calculation.

[0109] For example, 240 orders generate 6 order sets, each order set has a maximum of 40 orders, and the maximum calculation time is 5 seconds. When the initial objective function is 1278 and K is 4 or 8, the calculation results are shown in Table 3 below: Table 3

[0110] As shown in Table 3, when K is 4 or 8, the best effect is achieved, and the objective function drops to 733, a decrease of 42.64%.

[0111] For example, 240 orders generate 5 order sets, each order set has a maximum of 40 orders, the maximum calculation time is 5 seconds, the initial objective function is 1278, and the calculation results are shown in Table 4 below: Table 4

[0112] As shown in Table 4, when K is 6, the best effect is achieved and the objective function drops to 690.

[0113] For example, 486 orders generate 6 order sets, the maximum calculation time is 5 seconds, and the calculation results are shown in Table 5 below: Table 5

[0114] As shown in Table 5, when K is 10, the objective function 632 is obtained.

[0115] Through the above three examples, the overall effect is best when K is 6. For different K values, and after iterative optimization, the overall objective function can be greatly optimized.

[0116] According to an embodiment of the present disclosure, based on multiple orders, a first number of order sets to be created, and a second number of orders included in the order set to be created, multiple orders are allocated to obtain a first number of first candidate order sets, each of which includes a second number of orders; based on the first candidate order set and orders in the multiple orders that are not allocated to the first candidate order set, a second candidate order set is determined, the second candidate order set includes orders that are not allocated to the first candidate order set and at least one order in the first order set; and based on the first candidate order set and the second candidate order set, a first number of target order sets are obtained using a planning model, each of which includes a second number of orders. Thus, the first candidate order set and the second candidate order set can be determined first to reduce the number of decision variables of the planning model, and then the planning model can be used to obtain the target order set based on the first candidate order set and the second candidate order set, thereby improving the efficiency of order processing.

[0117] Figure 4 A flow chart of a process 400 of order processing according to some embodiments of the present disclosure is shown. The process 400 may be implemented at the server 120.

[0118] In box 410, the server 120 allocates the multiple orders based on the multiple orders, the first number of the order set to be created, and the second number of orders included in the order set to be created to obtain a first number of first candidate order sets, each of which includes the second number of orders.

[0119] In block 420 , the server 120 determines a second candidate order set based on the first candidate order set and orders in the plurality of orders that are not assigned to the first candidate order set, the second candidate order set including orders that are not assigned to the first candidate order set and at least one order in the first order set.

[0120] In block 430 , the server 120 uses the planning model to obtain a first number of target order sets based on the first candidate order set and the second candidate order set, each target order set including a second number of orders.

[0121] In some embodiments, based on multiple orders, a first quantity of an order set to be created, and a second quantity of orders included in the order set to be created, allocating multiple orders to obtain a first quantity of a first candidate order set includes: numbering the shelves based on a predetermined picking path, and determining the shelf numbers as a shelf sequence; obtaining order information of each of the multiple orders, the order information including the code and number of objects included in the order; and allocating multiple orders to obtain a first quantity of a first candidate order set based on the order information, the shelf sequence, the first quantity, and the second quantity.

[0122] In some embodiments, based on order information, shelf order, first quantity and second quantity, allocating multiple orders to obtain a first candidate order set of the first quantity includes: determining the number of pieces corresponding to all objects contained in the multiple orders; determining the shelf order of the shelves to which the objects corresponding to the largest number of pieces belong; based on the shelf order of the shelves to which the objects corresponding to the largest number of pieces belong, determining the minimum order and maximum order of selecting orders; based on the minimum order and the maximum order, allocating multiple orders to obtain a first candidate order set; if the number of the first candidate order set is less than or equal to the first quantity, and / or the number of orders contained in the first candidate order set is less than or equal to the second quantity, adjusting the minimum order and the maximum order by a preset step size; based on the adjusted minimum order and maximum order, continuing to allocate unallocated orders in the multiple orders; and repeating the above steps until the number of the first candidate order set is equal to the first quantity, and the number of orders contained in the first candidate order set is equal to the second quantity.

[0123] In some embodiments, determining a second candidate order set based on the first candidate order set and orders from a plurality of orders that are not assigned to the first candidate order set includes: removing up to a predetermined number of orders from the first candidate order set; and determining the second candidate order set based on the orders removed from the first candidate order set and orders from a plurality of orders that are not assigned to the first candidate order set.

[0124] In some embodiments, removing up to a predetermined number of orders from the first candidate order set includes: determining the shelf distance of each order in the first candidate order set to the first candidate order set; sorting all orders in the first candidate order set in order of shelf distance from small to large; and removing up to a predetermined number of orders from the first candidate order set according to the sorting result of the first candidate order set.

[0125] In some embodiments, determining the shelf distance from each order in the first candidate order set to the first candidate order set includes: determining, for each order, a first shelf distance between the minimum shelf sequence corresponding to the first candidate order set and the minimum shelf sequence corresponding to the order; determining a second shelf distance between the maximum shelf sequence corresponding to the first candidate order set and the maximum shelf sequence corresponding to the order; and determining the minimum distance between the first shelf distance and the second shelf distance as the shelf distance from the order to the first candidate order set.

[0126] In some embodiments, removing up to a predetermined number of orders from the first candidate order set according to the sorting result of the first candidate order set includes: obtaining a random number within a predetermined range; determining the number of orders to be removed based on the number of orders included in the first candidate order set and the random number, the number of orders to be removed being less than or equal to a predetermined number; and removing a corresponding number of orders from the first candidate order set based on the number of orders to be removed according to the sorting result of the first candidate order set.

[0127] In some embodiments, using a planning model to obtain a first quantity of target order sets based on a first candidate order set and a second candidate order set includes: selecting a predetermined number of orders from the second candidate order set; and using a planning model to obtain a first quantity of target order sets based on the orders selected from the second candidate order set and the first candidate order set.

[0128] In some embodiments, selecting a predetermined number of orders from the second candidate order set includes: determining a first order concentration of the first candidate order set; determining a second order concentration of the first candidate order set after adding each order in the second candidate order set to the first candidate order set; determining an order concentration change value corresponding to each order in the second candidate order set based on the first order concentration and the second order concentration; sorting the orders in the second candidate order set in order of order concentration change values ​​from large to small; and selecting a predetermined number of orders from the sorted orders.

[0129] In some embodiments, determining the first order concentration of the first candidate order set includes: determining the sum of the first shelf distances of each order based on the minimum shelf order and the maximum shelf order of each order in the first candidate order set; determining the sum of the second shelf distances between the minimum shelf order and the maximum shelf order corresponding to the first candidate order set; and determining the first order concentration based on the sum of the first shelf distances, the sum of the second shelf distances and the first quantity.

[0130] In some embodiments, process 400 also includes: constructing an objective function based on picking paths of multiple order sets, the picking path indicates the sum of shelf numbers corresponding to objects included in the multiple order sets, and the objective function is configured to make the picking path shortest; constructing a constraint function based on at least one constraint condition for the multiple order sets; and constructing a planning model based on the objective function and the constraint function.

[0131] In some embodiments, the constraints include at least one of the following: the number of order sets to be created; giving priority to generating order sets with smaller shelf numbers; when an order set contains orders, the order set is enabled; the number of orders contained in the order set to be created; the number of objects contained in all order sets; the volume of objects contained in all order sets; the size relationship between the maximum shelf sequence and the minimum shelf sequence corresponding to the order set; the maximum shelf sequence corresponding to the picking path; and the minimum shelf sequence corresponding to the order set.

[0132] Figure 5 A block diagram of an apparatus 500 for order processing according to some embodiments of the present disclosure is shown. The apparatus 500 may be implemented as or include the server 120.

[0133] The device 500 includes a first allocation module 510, which is configured to allocate multiple orders to obtain a first number of first candidate order sets based on multiple orders, a first number of order sets to be created, and a second number of orders included in the order set to be created, each of which includes a second number of orders; a determination module 520, which is configured to determine a second candidate order set based on the first candidate order set and orders in the multiple orders that are not allocated to the first candidate order set, the second candidate order set including orders that are not allocated to the first candidate order set and at least one order in the first order set; and a second allocation module 530, which is configured to use a planning model to obtain a first number of target order sets based on the first candidate order set and the second candidate order set, each of which includes a second number of orders.

[0134] In some embodiments, the first allocation module 510 is further configured to: determine the shelf numbers as a shelf order by numbering the shelves based on a predetermined picking path; obtain order information of each of a plurality of orders, the order information including the codes and number of objects included in the order; and allocate the plurality of orders based on the order information, the shelf order, the first quantity and the second quantity to obtain a first candidate order set of the first quantity.

[0135] In some embodiments, the first allocation module 510 is further configured to: determine the number of pieces corresponding to all objects contained in multiple orders; determine the shelf order of the shelves to which the objects corresponding to the largest number of pieces belong; determine the minimum order and maximum order of selecting orders based on the shelf order of the shelves to which the objects corresponding to the largest number of pieces belong; allocate multiple orders based on the minimum order and the maximum order to obtain a first candidate order set; if the number of the first candidate order set is less than or equal to the first number, and / or the number of orders contained in the first candidate order set is less than or equal to the second number, adjust the minimum order and the maximum order by a preset step size; based on the adjusted minimum order and the maximum order, continue to allocate unallocated orders in the multiple orders; and repeat the above steps until the number of the first candidate order set is equal to the first number, and the number of orders contained in the first candidate order set is equal to the second number.

[0136] In some embodiments, the determination module 520 is further configured to: remove up to a predetermined number of orders from the first candidate order set; and determine a second candidate order set based on the orders removed from the first candidate order set and orders from the plurality of orders that are not assigned to the first candidate order set.

[0137] In some embodiments, the determination module 520 is further configured to: determine the shelf distance from each order in the first candidate order set to the first candidate order set; sort all orders in the first candidate order set in order of shelf distance from small to large; and remove up to a predetermined number of orders from the first candidate order set according to the sorting result of the first candidate order set.

[0138] In some embodiments, the determination module 520 is also configured to: for each order, determine a first shelf distance between the minimum shelf sequence corresponding to the first candidate order set and the minimum shelf sequence corresponding to the order; determine a second shelf distance between the maximum shelf sequence corresponding to the first candidate order set and the maximum shelf sequence corresponding to the order; and determine the minimum distance between the first shelf distance and the second shelf distance as the shelf distance from the order to the first candidate order set.

[0139] In some embodiments, the determination module 520 is further configured to: obtain a random number within a predetermined range; determine the number of orders that need to be removed based on the number of orders included in the first candidate order set and the random number, and the number of orders that need to be removed is less than or equal to a predetermined number; and according to the sorting result of the first candidate order set, based on the number of orders that need to be removed, remove a corresponding number of orders from the first candidate order set.

[0140] In some embodiments, the second allocation module 530 is further configured to: select a predetermined number of orders from the second candidate order set; and utilize a planning model to obtain a first number of target order sets based on the orders selected from the second candidate order set and the first candidate order set.

[0141] In some embodiments, the second allocation module 530 is further configured to: determine a first order concentration of the first candidate order set; determine a second order concentration of the first candidate order set after adding each order in the second candidate order set to the first candidate order set; determine an order concentration change value corresponding to each order in the second candidate order set based on the first order concentration and the second order concentration; sort the orders in the second candidate order set in order of order concentration change values ​​from large to small; and select a predetermined number of orders from the sorted orders.

[0142] In some embodiments, the second allocation module 530 is further configured to: determine the sum of the first shelf distances of each order based on the minimum shelf order and the maximum shelf order of each order in the first candidate order set; determine the sum of the second shelf distances between the minimum shelf order and the maximum shelf order corresponding to the first candidate order set; and determine the first order concentration based on the sum of the first shelf distances, the sum of the second shelf distances and the first quantity.

[0143] In some embodiments, the device 500 also includes a construction module, which is configured to: construct an objective function based on the picking paths of multiple order sets, where the picking paths indicate the sum of the shelf numbers corresponding to the objects included in the multiple order sets, and the objective function is configured to make the picking path as short as possible; construct a constraint function based on at least one constraint condition for the multiple order sets; and construct a planning model based on the objective function and the constraint function.

[0144] In some embodiments, the constraints include at least one of the following: the number of order sets to be created; giving priority to generating order sets with smaller shelf numbers; when an order set contains orders, the order set is enabled; the number of orders contained in the order set to be created; the number of objects contained in all order sets; the volume of objects contained in all order sets; the size relationship between the maximum shelf sequence and the minimum shelf sequence corresponding to the order set; the maximum shelf sequence corresponding to the picking path; and the minimum shelf sequence corresponding to the order set.

[0145] The modules included in the device 500 can be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units can be implemented using software and / or firmware, such as machine executable instructions stored on a storage medium. In addition to or as an alternative to machine executable instructions, some or all of the units in the device 500 can be implemented at least in part by one or more hardware logic components. As an example and not limitation, exemplary types of hardware logic components that can be used include field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0146] Figure 6 1 shows a block diagram of an electronic device 600 in which one or more embodiments of the present disclosure may be implemented. It should be understood that Figure 6 The electronic device 600 shown is merely exemplary and should not constitute any limitation on the functionality and scope of the embodiments described herein.

[0147] like Figure 6 As shown, the electronic device 600 is in the form of a general electronic device. The components of the electronic device 600 may include, but are not limited to, one or more processors or processing units 610, a memory 620, a storage device 630, one or more communication units 640, one or more input devices 650, and one or more output devices 660. The processing unit 610 may be an actual or virtual processor and is capable of performing various processes according to a program stored in the memory 620. In a multi-processor system, multiple processing units execute computer executable instructions in parallel to improve the parallel processing capability of the electronic device 600.

[0148] The electronic device 600 typically includes a plurality of computer storage media. Such media can be any available media accessible to the electronic device 600, including but not limited to volatile and non-volatile media, removable and non-removable media. The memory 620 can be a volatile memory (e.g., registers, caches, random access memory (RAM)), a non-volatile memory (e.g., a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), flash memory) or some combination thereof. The storage device 630 can be a removable or non-removable medium, and can include a machine-readable medium, such as a flash drive, a disk, or any other medium, which can be used to store information and / or data and can be accessed within the electronic device 600.

[0149] The electronic device 600 may further include additional removable / non-removable, volatile / non-volatile storage media. Figure 6 As shown in , a disk drive for reading or writing from a removable, non-volatile disk (e.g., a "floppy disk") and an optical drive for reading or writing from a removable, non-volatile optical disk may be provided. In these cases, each drive may be connected to the bus (not shown) by one or more data media interfaces. The memory 620 may include a computer program product 625 having one or more program modules that are configured to perform various methods or actions of various embodiments of the present disclosure.

[0150] The communication unit 640 implements communication with other electronic devices through a communication medium. Additionally, the functions of the components of the electronic device 600 can be implemented with a single computing cluster or multiple computing machines that can communicate through a communication connection. Therefore, the electronic device 600 can operate in a networked environment using a logical connection with one or more other servers, a network personal computer (PC), or another network node.

[0151] The input device 650 may be one or more input devices, such as a mouse, a keyboard, a tracking ball, etc. The output device 660 may be one or more output devices, such as a display, a speaker, a printer, etc. The electronic device 600 may also communicate with one or more external devices (not shown) through the communication unit 640 as needed, such as a storage device, a display device, etc., communicate with one or more devices that allow a user to interact with the electronic device 600, or communicate with any device that allows the electronic device 600 to communicate with one or more other electronic devices (e.g., a network card, a modem, etc.). Such communication may be performed via an input / output (I / O) interface (not shown).

[0152] According to an exemplary implementation of the present disclosure, a computer-readable storage medium is provided, on which one or more computer instructions are stored, wherein the one or more computer instructions are executed by a processor to implement the method described above. According to an exemplary implementation of the present disclosure, a computer program product is also provided, which is tangibly stored on a non-transitory computer-readable medium and includes computer-executable instructions, and the computer-executable instructions are executed by a processor to implement the method described above.

[0153] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products implemented according to the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0154] These computer-readable program instructions can be provided to a processing unit of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processing unit of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0155] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0156] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple implementations of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of a module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some implementations as replacements, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be realized by a special hardware-based system that performs the function or action of the specification, or can be realized by a combination of special hardware and computer instructions.

[0157] The above descriptions of various implementations of the present disclosure are exemplary, non-exhaustive, and not limited to the disclosed implementations. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described implementations. The selection of terms used herein is intended to best explain the principles of the implementations, practical applications, or improvements to the technology in the marketplace, or to enable other persons of ordinary skill in the art to understand the implementations disclosed herein.

Claims

1. An order processing method, comprising: Based on a plurality of orders, a first quantity of the order set to be created, and a second quantity of orders included in the order set to be created, allocating the plurality of orders to obtain the first quantity of first candidate order sets, each of which includes the second quantity of orders; Determine a second candidate order set based on the first candidate order set and orders in the plurality of orders that are not assigned to the first candidate order set, wherein the second candidate order set includes orders that are not assigned to the first candidate order set and at least one order in the first order set; as well as The planning model is used to obtain the first quantity of target order sets based on the first candidate order set and the second candidate order set, each target order set including the second quantity of orders.

2. The method according to claim 1, wherein based on a plurality of orders, a first quantity of the order set to be created, and a second quantity of orders included in the order set to be created, allocating the plurality of orders to obtain the first quantity of the first candidate order set comprises: By numbering the shelves based on a predetermined picking path, the numbers of the shelves are determined as a shelf sequence; Obtaining order information of each of the multiple orders, wherein the order information includes a code and a quantity of objects included in the order; as well as Based on the order information, the shelf sequence, the first quantity, and the second quantity, the multiple orders are allocated to obtain the first candidate order set of the first quantity.

3. The method according to claim 2, wherein based on the order information, the shelf sequence, the first quantity and the second quantity, allocating the plurality of orders to obtain the first quantity of the first candidate order set comprises: Determine the number of items corresponding to each of the objects included in the multiple orders; Determine the shelf order of the shelf to which the objects corresponding to the largest number of pieces belong; Determine the minimum order and the maximum order of selecting the order based on the shelf order of the shelf to which the objects corresponding to the largest number of pieces belong; Based on the minimum order and the maximum order, the multiple orders are allocated to obtain the first candidate order set; If the number of the first candidate order set is less than or equal to the first number, and / or the number of orders included in the first candidate order set is less than or equal to the second number, adjusting the minimum order and the maximum order by a preset step size; Based on the adjusted minimum order and maximum order, continue to allocate the unallocated orders among the multiple orders; as well as The above steps are repeated until the number of the first candidate order set is equal to the first number, and the number of orders included in the first candidate order set is equal to the second number.

4. The method according to claim 1, wherein determining a second candidate order set based on the first candidate order set and orders in the plurality of orders that are not assigned to the first candidate order set comprises: removing at most a predetermined number of orders from the first candidate order set; as well as A second candidate order set is determined based on the orders removed from the first candidate order set and the orders in the plurality of orders that are not allocated to the first candidate order set.

5. The method of claim 4, wherein removing at most a predetermined number of orders from the first candidate order set comprises: Determine a shelf distance from each order in the first candidate order set to the first candidate order set; Sort all orders in the first candidate order set in ascending order of the shelf distance; as well as According to the sorting result of the first candidate order set, at most the predetermined number of orders are removed from the first candidate order set.

6. The method according to claim 5, wherein determining the shelf distance of each order in the first candidate order set to the first candidate order set comprises: For each order Determine a first shelf distance between a minimum shelf sequence corresponding to the first candidate order set and a minimum shelf sequence corresponding to the order; Determine a second shelf distance between a maximum shelf sequence corresponding to the first candidate order set and a maximum shelf sequence corresponding to the order; as well as The minimum distance between the first shelf distance and the second shelf distance is determined as the shelf distance from the order to the first candidate order set.

7. The method according to claim 5, wherein removing at most the predetermined number of orders from the first candidate order set according to the ranking result of the first candidate order set comprises: Get a random number within a predetermined range; Based on the number of orders included in the first candidate order set and the random number, determining the number of orders to be removed, the number of orders to be removed being less than or equal to the predetermined number; as well as According to the sorting result of the first candidate order set, based on the number of orders that need to be removed, a corresponding number of orders are removed from the first candidate order set.

8. The method according to claim 1, wherein using a planning model to obtain the first quantity of target order set based on the first candidate order set and the second candidate order set comprises: Selecting a predetermined number of orders from the second candidate order set; as well as The planning model is used to obtain the first number of the target order set based on the orders selected from the second candidate order set and the first candidate order set.

9. The method according to claim 8, wherein selecting the predetermined number of orders from the second candidate order set comprises: Determining a first order concentration of the first candidate order set; determining a second order concentration of the first candidate order set after each order in the second candidate order set is added to the first candidate order set; Determine, based on the first order concentration and the second order concentration, an order concentration change value corresponding to each order in the second candidate order set; Sorting the orders in the second candidate order set in descending order of the order concentration change values; as well as The predetermined number of orders is selected from the sorted orders.

10. The method according to claim 9, wherein determining the first order concentration of the first candidate order set comprises: Determine the sum of the first shelf distances of each order based on the minimum shelf sequence and the maximum shelf sequence of each order in the first candidate order set; Determine the sum of the second shelf distances between the minimum shelf sequence and the maximum shelf sequence corresponding to the first candidate order set; as well as The first order concentration is determined based on the sum of the first shelf distances, the sum of the second shelf distances, and the first quantity.

11. The method according to claim 1, further comprising: constructing an objective function based on the picking paths of the multiple order sets, the picking paths indicating the sum of the shelf numbers corresponding to the objects included in the multiple order sets, the objective function being configured to make the picking paths the shortest; constructing a constraint function based on at least one constraint condition for the plurality of order sets; as well as The planning model is constructed based on the objective function and the constraint function.

12. The method according to claim 11, wherein the constraint condition comprises at least one of the following: The number of order sets to be created; Prioritize the generation of order sets with smaller shelf numbers; When an order set contains orders, the order set is enabled; The number of orders contained in the order set to be created; The number of objects contained in all order collections; The volume of all objects contained in the order set; The size relationship between the maximum shelf order and the minimum shelf order corresponding to the order set; The maximum shelf sequence corresponding to the picking path; The minimum shelf order corresponding to the order set.

13. An order processing device, comprising: A first allocation module is configured to allocate the multiple orders based on the multiple orders, the first number of the order set to be created, and the second number of orders included in the order set to be created to obtain the first number of first candidate order sets, each of which includes the second number of orders; a determining module configured to determine a second candidate order set based on the first candidate order set and orders in the plurality of orders that are not allocated to the first candidate order set, wherein the second candidate order set includes orders that are not allocated to the first candidate order set and at least one order in the first order set; as well as The second allocation module is configured to obtain the first quantity of target order sets based on the first candidate order set and the second candidate order set by using a planning model, each target order set including the second quantity of orders.

14. An electronic device comprising: at least one processing unit; as well as At least one memory, the at least one memory being coupled to the at least one processing unit and storing instructions for execution by the at least one processing unit, the instructions causing the electronic device to perform the method according to any one of claims 1 to 12 when executed by the at least one processing unit.

15. A computer-readable storage medium having a computer program stored thereon, wherein the computer program can be executed by a processor to implement the method according to any one of claims 1 to 12.

16. A computer program product comprising computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the method according to any one of claims 1 to 12.

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