A method, device, and electronic device for dividing a goods collection area

Abstract

The present invention discloses a method, apparatus and electronic device for dividing a goods collection area, which are applied to a warehouse management system. The method includes: obtaining the number of stores and the area required for each store to complete the current order task; determining the size and number of the smallest cells according to the size of the entire goods collection area and the size of each order container, and through the processing of the objective function according to the number of stores, the area required for each store, the size and number of the smallest cells, dividing the entire goods collection area into N goods collection areas, where N≥1; wherein the objective function defines that the storage rate of the entire goods collection area is optimal in terms of time. Each of the N goods collection areas contains one or more smallest cells, and each goods collection area corresponds to the order task of one store. This method realizes the dynamic division of the goods collection areas for each store in the goods collection area, and can avoid the waste of space resources caused by overly large initial division of the scope of each goods collection area.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular, to a method, device, and electronic device for dividing a goods consolidation area. Background Art

[0002] Cross-docking sorting of store orders means that at a cross-docking facility, full truckload shipments from various suppliers are received, immediately disassembled, sorted, and stacked according to the needs of each store and the delivery point, and then loaded onto prepared outbound conveyances and sent to the delivery points of each store. None of the shipments enter the storage space of the warehouse.

[0003] Generally, the entire production process of automated cross-docking sorting is divided into: receiving, palletizing, sorting, goods consolidation, and shipping. Among them, in order to improve the production efficiency of store orders, the order details are split into multiple groups of tasks, and different sorting task executors independently sort them. Each sorting task is bound to an order container (such as a pallet, a cage cart, a turnover box, etc.). All the goods required within each task are placed in the order container after sorting. Therefore, after picking is completed, it is necessary to gather multiple order containers covered by the same store, especially the order containers that need to be loaded onto the same vehicle according to the shipping vehicle schedule, for easy shipping. This process is called goods consolidation.

[0004] Since the order containers consolidated together are shipped and loaded onto the vehicle at the warehouse platform, in order to accurately manage the loaded containers, a physical storage area is generally marked separately in the shipping platform area to distinguish the goods loaded into different trucks. This area can be called the "goods consolidation identification area", or simply the "goods consolidation area" for short. Moreover, this area does not always belong to a certain store throughout the cycle, but dynamically changes the affiliated store according to the shipping requirements. Therefore, how to dynamically adjust the goods consolidation areas required by each store according to the upstream order tasks, avoid resource waste, and improve storage efficiency is an urgent problem for those skilled in the art. Summary of the Invention

[0005] The present invention provides a method, device, and electronic device for dividing a goods consolidation area, which are used to divide the entire goods consolidation area into multiple goods consolidation areas, so as to achieve the optimal allocation of resources and improve storage efficiency. Specifically, the embodiments of the present invention disclose the following technical solutions:

[0006] First aspect, an embodiment of the present invention provides a method for dividing a goods collection area. The method includes: obtaining the number of stores and the area required for each store to complete the current order task; determining the size and number of the smallest cells according to the size of the entire goods collection area and the size of each order container, where each order container is used to hold the goods corresponding to at least one order task; dividing the entire goods collection area into N goods collection areas through objective function processing according to the number of stores, the area required for each store, the size and number of the smallest cells, where N≥1; where the objective function defines the optimal storage rate of the entire goods collection area in terms of time, and each of the N goods collection areas contains one or more smallest cells, and each goods collection area corresponds to the order task of one store.

[0007] Combined with the first aspect, in a possible implementation manner of the first aspect, the obtaining the number of stores and the area required for each store to complete the current order task includes: performing the step of obtaining the number of stores and the area required for each store to complete the current order task based on any one of the following situations: every preset period; the order tasks of at least one store change, and the area required for the goods corresponding to the change reaches a certain threshold; the loading rate of the entire goods collection area reaches a certain threshold.

[0008] Combined with the first aspect, in another possible implementation manner of the first aspect, the area required for the goods and / or the loading rate of the entire goods collection area are obtained through the following methods: using a pressure sensor to detect the pressure in the entire goods collection area, or using the pictures in the entire goods collection area collected by a camera to obtain the area required for the goods and / or the loading rate of the entire goods collection area.

[0009] Combined with the first aspect, in yet another possible implementation manner of the first aspect, after the objective function processing, it further includes: outputting a first decision variable and a second decision variable; where the first decision variable is used to decide whether to enable production operations for each store at the current moment; the second decision variable is used to decide whether to enable storing order containers in each smallest cell, and the smallest cell belongs to one of the N goods collection areas.

[0010] Combined with the first aspect, in yet another possible implementation manner of the first aspect, when the objective function is processed, the objective function further constrains that: each store with a production operation decided to be started is assigned at least one smallest cell; and / or, each smallest cell decided to be enabled is uniquely assigned to one of the N goods collection areas; and / or, two or more smallest cells decided to be enabled and assigned to the same store are adjacent to each other.

[0011] In combination with the first aspect, in another possible implementation manner of the first aspect, when being processed by the objective function, the objective function further constrains that: the area of each goods collection area is greater than or equal to the area required for the current order task of the store corresponding to the goods collection area.

[0012] In combination with the first aspect, in another possible implementation manner of the first aspect, the method further includes: dividing the ranges of the N goods collection areas by using separation identification lines; wherein, the separation identification line is any one of a separation identification object, a light projection line or a luminous line.

[0013] In combination with the first aspect, in another possible implementation manner of the first aspect, the method further includes: when detecting that there are differences between the ranges of the currently determined N goods collection areas and the ranges of the previously divided goods collection areas, adjusting the positions of the separation identification lines corresponding to at least one goods collection area.

[0014] In a second aspect, an embodiment of the present invention further provides a device for dividing a goods collection area, and the device includes: an acquisition unit, configured to acquire the number of stores and the area required for each store to complete the current order task; a determination unit, configured to determine the size and number of the minimum cells according to the size of the entire goods collection area and the size of each order container, and each order container is used to hold the goods corresponding to at least one order task; a processing unit, configured to divide the entire goods collection area into N goods collection areas through objective function processing according to the number of stores, the area required for each store, the size and number of the minimum cells, where N≥1;

[0015] Wherein, the objective function defines that the storage rate of the entire goods collection area is optimal in time, each of the N goods collection areas includes one or more minimum cells, and each goods collection area corresponds to the order task of one store.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor and a memory, the memory is configured to store computer-executable instructions; the processor is configured to read the instructions from the memory and execute the instructions to implement the method described in the foregoing first aspect and any implementation manner of the first aspect.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, and computer instructions are stored in the computer-readable storage medium, and the computer instructions are used to cause the computer to execute the method in the foregoing first aspect and any implementation manner of the first aspect.

[0018] In addition, an embodiment of the present invention further provides a computer program product, which includes a computing program stored on a computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to execute the methods in the foregoing first aspect and any implementation manner of the first aspect.

[0019] The method, device, electronic device, and readable storage medium provided in this embodiment use the constructed objective function to constrain parameters such as the number of stores allocated to the goods consolidation area resources, the area required by the stores, the minimum cell size and quantity, etc., so as to obtain a configuration plan with the highest storage efficiency, that is, divide into N goods consolidation areas and the number of minimum cells included in each goods consolidation area, realizing the dynamic division of the goods consolidation areas of each store in the goods consolidation area, and avoiding the waste of space resources caused by overly large division of the scope of each goods consolidation area in the initial stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 It is a schematic diagram of a cross-docking system architecture provided by an embodiment of the present invention;

[0022] Figure 2 It is a flowchart of a method for dividing a goods consolidation area provided by an embodiment of the present invention;

[0023] Figure 3 It is a schematic diagram of the association between a store and a minimum cell provided by an embodiment of the present invention;

[0024] Figure 4 It is a flowchart of another method for dividing a goods consolidation area provided by an embodiment of the present invention;

[0025] Figure 5 It is a schematic diagram of establishing an association relationship between a store and each minimum cell provided by an embodiment of the present invention;

[0026] Figure 6 It is a schematic diagram of the goods consolidation area required by each store after division provided by an embodiment of the present invention;

[0027] Figure 7 It is a schematic diagram of the structure of a device for dividing a goods consolidation area provided by an embodiment of the present invention;

[0028] Figure 8 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Detailed implementation manners

[0029] In order to enable those skilled in the art of the present technology to better understand the technical solutions in the embodiments of the present invention, and to make the above-mentioned objects, features, and advantages of the embodiments of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0030] Before describing the technical solutions of the embodiments of the present invention, the application scenarios of the embodiments of the present invention will be described first with reference to the accompanying drawings.

[0031] See Figure 1 , Figure 1 , which is a schematic diagram of a goods cross-docking system architecture provided by an embodiment of the present invention. The system 100 includes: a server 10, a robot 20, a goods stacking shelf area 30, a sorting station 40, sorting personnel 41, a goods consolidation area 50, a store collection 60, and a truck area 70. In addition, the system 100 may further include other devices such as staff and an operation console.

[0032] Among them, the server 10 may be a single server, or may also be multiple servers, such as a server cluster, for controlling the work of other devices / robots in the cross-docking system. One or more robots 20 are used to place goods on the goods stacking shelf area 30. A plurality of shelves 31 are arranged in the goods stacking shelf area 30, and various items, goods, etc. are placed on each shelf 31. The placement of these goods is like the shelves with various commodities seen in a supermarket, and the plurality of shelves 31 are arranged in a shelf array form.

[0033] The staff makes the server 10 work through the operation console. The server 10 communicates with the robot 20 through a wireless network. Under the control of the server 10, each robot 20 executes corresponding item / goods handling tasks and executes the receiving and goods stacking processes in the cross-docking process. For example, the server 10 plans a moving path for the robot 20 according to the handling task, instructs the robot 20 to drive to the goods stacking shelf area 30 according to the indicated moving path, and then transports the goods in whole boxes to the pallet shelves. Further, in the goods stacking shelf area 30, different types of commodities are pre-divided into different areas, and then various commodities are placed in the corresponding areas. As Figure 1 shown, different goods areas in the shelf area are separated by gaps to facilitate the movement of the robot 20.

[0034] In addition, the sorting station 40 mainly completes the sorting tasks. The robot 20 or the staff member 41 will, according to the task instructions, transport the items / goods on a specific shelf 31 in the palletizing shelf area 30 along the direction of the picking shelf to the picking area (i.e., the sorting station 40). Then, the robot 20 or the staff member 41 in the sorting station 40 sorts the incoming items / goods, and after sorting, puts all the goods required within all tasks into the order container to generate a turnover box for turnover to the store orders or the supermarket. After the sorting task is completed, the robot returns to the palletizing shelf area 30 along the direction of the completed transportation, waiting for the transportation tasks of other goods.

[0035] After the picking is completed, it is necessary to gather multiple order containers covered by the same store, especially the order containers that need to be loaded into the same vehicle according to the shipping vehicle schedule, for easy shipping, which is the goods collection process. Place all the order containers in the goods collection area 50. The goods collection area 50 is pre-divided into multiple virtual cells 51, and the quantity and size of the cells 51 are related to the size of the entire goods collection area 50.

[0036] Different cells 51 form different goods collection areas, and each goods collection area corresponds to the order tasks of a store, which is used to complete the order tasks of each store in the store set 60. After establishing a unique relationship between the order containers in each goods collection area and the store, they are loaded and shipped according to the store numbers, such as the respective goods collection areas belonging to stores 1 to 5.

[0037] Among them, in the truck area 70, there can be at least one truck 71, which is used to load all the order containers of the associated stores and dispatch them to the corresponding stores and supermarkets.

[0038] Among them, each goods collection area divided according to the store in the entire goods collection area 50 can be in three states, namely:

[0039] 1. Idle state: indicating that the area is not assigned to a store;

[0040] 2. Production state: indicating that the area is assigned to a store but the goods collection is not completed;

[0041] 3. Completed state: indicating that the area is assigned to a store and the goods collection is completed.

[0042] Once the goods collection area is in a non-idle state, that is, the production state or the completed state, there is exclusivity for the containers in other areas. Along with the store production, it means that the quantity of the smallest commodity storage containers in this area will gradually increase from zero until it is full. In the time dimension, the temporarily unused space in this area is a waste of the cost of the storage space.

[0043] In the actual production process, since upstream orders are issued in real time and there is a possibility of frequent modification, the production rhythm of orders in each store often shows dynamic changes. Considering production efficiency, more emphasis is placed on preferentially sorting the received goods rather than waiting until all the goods required by a certain store are received before centralized production. Therefore, when dividing the scope of each consolidation area, it is difficult to clearly define the space that will be used within a limited time at the beginning. Thus, if the scope of each independent consolidation area is divided too small, it may result in overly scattered distribution of order containers loaded onto the same truck during final delivery. Conversely, if the scope of each independent consolidation area is divided too large, it may lead to a large amount of remaining storage space, resulting in waste of storage resources and reduced storage efficiency.

[0044] The technical solution provided by the embodiments of the present invention is used to dynamically divide the consolidation area to improve storage efficiency and reduce waste.

[0045] The following details the technical solution provided by the embodiments of the present invention.

[0046] See Figure 2 , which is a method for dividing a consolidation area provided by an embodiment of the present invention. This method can be implemented by the above-mentioned server 10 or other devices in the cross-docking system. Specifically, the method includes:

[0047] Step 101: Obtain the number of stores and the area occupied by each store to complete the current order task.

[0048] Among them, the number of stores is the number of stores that need to allocate a consolidation area for the currently started production operation. The area occupied by each store to complete the current order task is the total area of all order containers required for the current store to complete all order tasks. Further, the server determines the number of stores and the occupied area based on the order numbers and delivery times of each store.

[0049] Step 102: Determine the size and quantity of the smallest cells according to the size of the entire consolidation area and the size of each order container, where each order container is used to hold the goods corresponding to at least one order task.

[0050] Among them, the size of the entire consolidation area includes the length and width of the consolidation area. For example Figure 1 as shown, the total length of the consolidation area 50 is 16 meters and the depth (width) is 10 meters, so the total area of the consolidation area 50 is 160 square meters. An order container refers to a container for holding order goods and commodities. Each order container corresponds to one or more order tasks, and these order tasks all belong to the same store.

[0051] For example, assuming that the size of each order container is 2×2 (both the length and width are 2), for the goods collection area 50 with a length of 16 meters, according to the length of each order container being two meters, it can be divided into 8 minimum cells, the size of each cell is 2×10, and the area of each minimum cell is 20 square meters.

[0052] It should be understood that the minimum cells can also be determined in other ways. For example, divided by the width of each minimum container. In this example, if divided by order containers with a width of 2 meters, the goods collection area with a depth of 10 meters can be divided into 5 minimum cells.

[0053] It should be noted that in this embodiment, the minimum cells are all virtual cells, and each goods collection area is formed by combining one or more cells.

[0054] Step 103: According to the number of stores, the area required by each store, the size and number of the minimum cells, through the processing of the objective function, divide the entire goods collection area into N goods collection areas, N≥1.

[0055] Among them, the objective function defines that the storage rate of the entire goods collection area is optimal in terms of time, that is, the storage efficiency of the goods collection area is the highest among different configuration schemes. The so-called optimal storage rate or highest storage efficiency means that at a certain time node, the area of the entire goods collection area is divided according to the order tasks of the stores, and the configuration scheme with the smallest remaining area when storing goods in each divided goods collection area.

[0056] Furthermore, this objective function can be expressed by the relational expression (1)

[0057]

[0058] Among them, A i represents the total area of the goods used by the order containers of store i, x i represents the decision variable, that is, it is interpreted as whether store i starts production operations at this moment. If it starts, then x i takes the value of 1; if it does not start, then x i takes the value of 0. A total is the area of the entire goods collection area, O is the set of stores, and i is used to indicate any store.

[0059] In an example, assuming there are 5 stores, i = {1, 2, 3, 4, 5}, then the above relational expression (1) can be written as expression (2):

[0060]

[0061] Among them, x iThe value (decision) can be 1 or 0. For example, one possible case is {x1, x2, x3, x4, x5}, with the value being {1, 1, 1, 0, 0}; or it can be other combinations, such as {1, 0, 1, 0, 0}, {1, 0, 0, 1, 1}, etc. Based on the above x i Due to different decisions, the corresponding total area is different. In addition, the total areas {A1, A2, A3, A4, A5} occupied by the order containers required by each store are also known in advance. Therefore, in this embodiment, the objective function is to find a solution in a possible decision situation where the total area is closest to or equal to the current entire goods collection area. In this case, the remaining area is the smallest and the output result is the best, that is, the storage rate of the goods collection area is the best.

[0062] In this embodiment, the parameters such as the number of stores, the area required by each store, the minimum cell size and quantity obtained in steps 101 and 102 are input into the constraint of this objective function to obtain a decision result, that is, N goods collection areas are obtained, where N is a positive integer greater than or equal to 1. And each of the N goods collection areas contains one or more minimum cells, and each goods collection area corresponds to the order task of one store.

[0063] The method provided in this embodiment uses the constructed objective function to constrain parameters such as the number of stores allocated to the goods collection area resources, the area required by the stores, the minimum cell size and quantity, etc., so as to obtain a configuration plan with the highest storage efficiency, that is, N goods collection areas are divided, and the number of minimum cells included in each goods collection area is obtained, realizing the dynamic division of the goods collection areas of each store in the goods collection area, and can avoid the space waste caused by the overly large range division of each goods collection area in the initial stage.

[0064] Optionally, in the above step 101, the steps of obtaining the number of stores and the area required by each store to complete the current order task can be executed based on any of the following situations. Specifically, it includes the following situations:

[0065] Situation 1: Every other preset period.

[0066] The system sets a preset period. Every time a preset period arrives, it starts to check the execution status of the current tasks of each store, the occupied area, and the number of stores that need to be scheduled currently. The preset period can be determined by the statistics of the historical goods collection processing completion situation. For example, if the historical goods collection processing period is 1 hour, then the preset period is set to 1 hour.

[0067] Situation 2: The order tasks of at least one store change, and the area required by the goods corresponding to the change reaches a certain threshold.

[0068] Specifically, when there are changes in the upstream orders of the store, the system calculates the change amount of the current orders. The change amount can be measured by the area occupied by the order containers that increase or / and decrease. When the change amount reaches a certain threshold, step 101 described above is started. Further, the threshold can be set according to the business scenario, or it can also be obtained through prediction or empirical inference. For example, in a certain area or store, the orders are very likely to be increased to 30% of the original quantity of goods or the total quantity of order containers.

[0069] Case 3: The loading rate of the entire goods collection area reaches a certain threshold.

[0070] Among them, the loading rate is equal to the ratio between the total area of all independent order containers and the area of the entire goods collection area. Expressed by the formula: Loading rate = Total area of all independent containers / Area of the entire goods collection area

[0071] For example, when loaded to a certain area and it is detected that the loading rate of the entire goods collection area reaches the threshold (such as 60%), start the step of dividing the goods collection area, that is, execute step 101.

[0072] In this embodiment, three methods are used to trigger the execution of obtaining the number of stores and the area required for each store to complete the current order task, thus realizing the dynamic adjustment and real-time detection of the division of the goods collection area.

[0073] Further, in the above-mentioned start detection step, it is necessary to detect "the area required for the goods" and / or "the loading rate of the entire goods collection area", which can be specifically obtained through the following methods:

[0074] Method 1: Use a pressure sensor to detect the pressure within the entire goods collection area.

[0075] Set a pressure sensing device on the entire bottom surface of the goods collection area. Through a storage unit with a preset area size, determine whether the storage unit is occupied, and then calculate the occupied area of the area placement in real time. When a pressure value can be detected on a certain storage unit, determine the area where the storage unit point is located as the occupied area; conversely, if no pressure value is detected, determine that the area of the current point is not placed with an order container.

[0076] Method 2: Use the pictures collected by the camera within the entire goods collection area to obtain "the area required for the goods" and / or "the loading rate of the entire goods collection area".

[0077] This implementation method mainly analyzes the pictures / photos taken by the camera to obtain the occupancy situation of the area on the current goods collection area. For example, analyze the area where the order containers are stacked from the pictures / photos taken, and then compare it with the area of the entire goods collection area to obtain the loading rate.

[0078] It should be understood that only two methods are enumerated in this embodiment, and the above-mentioned required occupied area or loading rate can also be obtained through other methods. For example, another possible implementation method is that when performing production operations, the system records that a certain goods consolidation area is designated for use by a store, and several order containers are placed in this goods consolidation area. Then, the occupied area of this goods consolidation area is counted as: the sum of the areas of all order containers in this goods consolidation area, and thus the "occupied area required for the goods" and / or the "loading rate of the entire goods consolidation area" are obtained.

[0079] For example, to complete the current order task in Store 1, a total of 10 order containers are required. These 10 order containers are allocated in Goods Consolidation Area No. 1, and these 10 order containers are all laid flat in the area of Goods Consolidation Area No. 1. The size of each order container is known. Based on this information, the total area of these 10 order containers in Goods Consolidation Area No. 1 can be obtained. In addition, using the known total area of Goods Consolidation Area No. 1, the ratio of the two is calculated to obtain the loading rate of the order of Store 1 in Goods Consolidation Area No. 1 at this time.

[0080] In another embodiment, in step 103, after being processed by the above-mentioned objective function, it further includes: outputting a first decision variable and a second decision variable.

[0081] Among them, the first decision variable is used to decide whether to start production operations for each store at the current moment. The second decision variable is used to decide whether to enable storing order containers for each minimum cell, and the minimum cell belongs to one of the N goods consolidation areas. Each of the N goods consolidation areas is used to store goods to be shipped for completing the order tasks of this store. Further, the goods to be shipped are stored in order containers.

[0082] Before outputting the decision variables, data modeling is performed on the above-mentioned objective function, and the following variables are defined:

[0083] Let a set composed of all stores be the store set. Assume that there is at least one store in this store set. Each store number is represented by "i", and the store set is represented by "O". For example, if the store set O consists of 5 stores, then this store set O = {Store 1, Store 2, Store 3, Store 4, Store 5}.

[0084] Each store has two states. One state is: enabling production operations at this moment; the other state is: not starting production operations at this moment. Among them, starting production operations means that a goods consolidation area is allocated for the goods required by this store. In other words, there is a goods consolidation area for stacking the order containers required by this store.

[0085] Optionally, the two states of the store are represented by "x", where x ∈ {0, 1}. When x = 1, it means that the store is activated for production operations at the current moment; when x = 0, it means that the store is not activated for production operations at the current moment.

[0086] In this embodiment, among the 5 stores, at the previous moment, some stores may have been assigned a goods collection area, and some stores may not have been assigned a goods collection area. Therefore, define the set of stores that have been assigned a goods collection area as "o". For example, if only store 3 has been assigned a goods collection area, then the set of stores o that have been assigned a goods collection area = {store 3}. At this time, the set of stores o that have been assigned a goods collection area is a subset of the store set O.

[0087] Optionally, the set of stores o that have been assigned a goods collection area can also be an empty set, indicating that none of stores 1 to 5 have been assigned a goods collection area at the current moment.

[0088] That is, summarizing the above, the first decision variable x is obtained i , and the value range of this first decision variable is 0 or 1, which can be expressed as:

[0089] x i ∈ {0, 1}, indicating the state of whether store i starts production operations at this moment.

[0090] In addition, after being processed by the objective function of the above relationship (1), a second decision variable is also output, which can be expressed as y ij , and for the second decision variable y ij the value range is 0 or 1.

[0091] y ij ∈ {0, 1}, indicating whether the i-th smallest cell is assigned to store j for use, and there is a one-to-one correspondence between this store j and a goods collection area. When y = 1, it means that the current smallest cell i is assigned to a store j for use; when y = 0, it means that the current smallest cell is not assigned to store j for use.

[0092] It should be noted that the letters "i" and "j" in this embodiment do not specifically refer to a certain store or a certain smallest cell, but are merely a data expression representing the meaning of variables. For example, in the expression of the first decision variable x i ∈ {0, 1}, "i" refers to the store number. In the expression of the second decision variable y ij ∈ {0, 1}, "i" refers to the smallest cell number, and "j" refers to the store number associated with the current smallest cell i. Of course, for the sake of distinction, other symbols or letters can also be used to represent them, and this embodiment does not limit this.

[0093] See Figure 3, which is a schematic diagram of the association between a store and the smallest cells provided in this embodiment. Among them, each smallest cell can be represented as S1, S2, S3, S4, etc. The set composed of these smallest cells, or the set of all smallest cells in the goods collection area, is "S", and S = {S1, S2, S3, S4,...}. Among them, the set composed of the smallest cells that have been assigned to stores (or goods collection areas) is marked as "s". For example, if the smallest cells S1, S2, and S3 have been assigned to stores, then s = {S1, S2, S3}. At this time, s is a subset of S.

[0094] Figure 3 In this case, for each store, such as store 1 or store 2, there may be a corresponding relationship with any one of the smallest cells, and one smallest cell can only be assigned to one store for use. Figure 3 Each dotted line shown indicates a possible corresponding relationship between the two, such as y ij = y 11 Indicates that a corresponding relationship is established between the smallest cell S1 and store 1. At this time, the dotted line between store 1 and the smallest cell S1 becomes a solid line, and the dotted line connection between S1 and store 2 is cancelled (the dotted line indicates that the two have not established a binding relationship). At this time, the first decision variable x1 of decision-making store 1 is 1, and store 1 is started to be put into production at this moment.

[0095] In this embodiment, through the first decision variable and the second decision variable output by the objective function, that is, it represents whether each store is put into production use, and after the store is put into production, the number and size of the smallest cells assigned to the store, so as to determine the range of the goods collection area required by different stores, and thus obtain the configuration result with the highest storage efficiency.

[0096] Furthermore, in the above embodiment, in step 103, when processed by the objective function, the objective function also makes at least one or more of the following constraints:

[0097] Constraint 1: Each store where the production operation is enabled by decision-making is assigned at least one smallest cell.

[0098] Among them, this constraint 1 is used to ensure that the store where the production operation is started will be assigned one or more smallest cells, so as to avoid the situation that the store is not assigned a smallest cell and cannot carry out production operations. Furthermore, this constraint 1 can be represented by relational expressions (3) and (4)

[0099] x i ≥ y ij j∈O,i∈S (3)

[0100]

[0101] In relations (3) and, x i represents whether the i-th store is engaged in production operations, and y ij represents whether a minimum cell i is assigned to store j or collection area j. Here, the store j mentioned is the same store as the store i referred to by x i The only difference is that the mathematical symbols "i" and "j" have different meanings. j is any one in the store set O, and i is any one in the set S of unassigned minimum cells.

[0102] In relation (4), x j represents whether the j-th store is engaged in production operations, represents the sum of all minimum cells assigned to store j, mathematically represents any value, represents any store j in the store set O.

[0103] In this example, only when x i = 1 and y ij = 0 or 1 does the above relation (3) hold. However, when x i = 0 and y ij = 1, relation (3) does not hold. At this time, it means that store i is not enabled for production operations, but y = 1 has already assigned the minimum cell i to this store. At this time, formula (3) does not hold, so the optimal solution cannot be obtained in the above objective function formula (1). Therefore, the case where x i = 0 and y ij = 1 is excluded in "Constraint 1". Similarly, in relation (4), for the formula to hold, the sum of the quantities of all the minimum cells assigned on the left side is greater than or equal to 1, and x j takes the value of 1, indicating that the current store j is enabled for production operations.

[0104] In this embodiment, through Constraint 1, it can be ensured that in the decision output by the objective function, each store where the production operation is started is assigned at least one minimum cell, that is, a correspondence relationship is established between the store where the enabled production operation is located and at least one minimum cell.

[0105] Constraint 2: Each minimum cell enabled by a decision is uniquely assigned to one of the N collection areas.

[0106] This constraint avoids the same minimum cell being assigned to two or more collection areas. The specific constraint is represented by the following relations (4) and (5).

[0107]

[0108] Among them, the relational expression (5) can be interpreted as: if a minimum cell i is assigned to store j, and the first decision weight value of this store j is 1, then the sum of all values of this minimum cell i cannot exceed 1, that is, the maximum value is 1, and it only corresponds to one store. Therefore, it is restricted that a minimum cell can only be assigned to one store and used as part of the goods collection area range of this store, so as to prevent the order containers for goods collection from being confused due to different stores sharing the same minimum cell.

[0109] Constraint 3: Two or more minimum cells that are decision-enabled and assigned to the same store are adjacent to each other.

[0110] This Constraint 3 is used to ensure that there are no minimum cells of other stores between two minimum cells assigned to the same store. For example, for minimum cells 1, 2, and 3, when assigning stores, avoid assigning minimum cell 1 and 3 to store 1 and minimum cell 2 to store 2. In this example, in order to assign minimum cells 1 and 2 to store 1 and cell 3 to store 2 through Constraint 3, the efficiency of loading and shipping can be improved, and the divergence of the order containers of the same store during shipping can be avoided. Specifically, this Constraint 3 can be represented by the following relational expressions (6) and (7):

[0111]

[0112]

[0113] Among them, i represents the currently to-be-assigned minimum cell, (i - 1) and (i + 1) respectively represent the two adjacent cells of this minimum cell i, y (i-1)j and y (i+1)j indicate that the minimum cells (i - 1) and (i + 1) are both assigned to store j, and if they are both used to store the order containers of store j, the corresponding values are both 1. In this case, the relational expression (6) is expressed as: y (i-1)j +y (i+1)j -1 = 1 + 1 - 1 = 1. At this time, the constraint y ij = 1 is required to satisfy the expression (6), ensuring that the two adjacent minimum cells (i - 1) and (i + 1) of the minimum cell i are both assigned to store j, and further ensuring the continuity of the minimum cell assignment.

[0114] Similarly, in the relational expression (7), for the two cells (i - 1) and (i + 1) adjacent to the minimum cell i that are both assigned to store j, when taking values, ensure that: y (i-1)j +y (i+1)j = 1 + 1 = 2 > y ij , y ij = 1, thus ensuring the continuous assignment of two adjacent minimum cells.

[0115] In addition, in another example, the above objective function further includes Constraint 4. Specifically,

[0116] Constraint 4: The area of each consolidation area is greater than or equal to the area required for the current order task of the store corresponding to the consolidation area.

[0117] In this example, Constraint 4 is used to ensure that the area of the consolidation area allocated to each store is greater than or equal to the total area A required by the store. j , which is mathematically represented by the following relational expression (8):

[0118]

[0119] where a represents the area of each smallest cell, and y ij represents whether a smallest cell i is allocated to store j, represents the sum of the areas of all the smallest cells allocated to store j, and x i represents whether store j is put into production operation. If so, the value of x i is 1, and the value of y ij is also 1, that is the sum of the areas of all the smallest cells allocated to store j is greater than or equal to the area required for the current order task of store j.

[0120] It should be understood that, given the area of the consolidation area allocated to store j and the area currently required by store j, subtracting the two can obtain whether there is any remaining area in the consolidation area corresponding to store j. If the result of the subtraction is 0, it is determined that there is no remaining space in the currently allocated consolidation area; if the result is not 0, it means that there is still remaining space.

[0121] In this embodiment, according to the above Constraint 4, the resources of the consolidation area are allocated to each store to be allocated, so as to ensure that the area of the allocated consolidation area is sufficient to stack the total area of the order containers required for the store to complete the current task.

[0122] Optionally, in the above objective function, other more or fewer constraints may also be included. For example, to ensure that at the previous moment, the consolidation area in the production state can continue to maintain production at the next moment, and there will be no situation where the original production stops or is abnormal due to the re - division of the consolidation area.

[0123] Specifically, it can be constrained by expressions (9) and (10),

[0124]

[0125]

[0126] where sij represents the set of the smallest cell j that has been assigned to store i. That is, if the smallest cell i is assigned to store j, then store j must be a store in the assigned collection area and belongs to one of the assigned store sets s. Then, through the relationship (9) y ij =s ij To constrain. At this time, store i or store j must be the store that is decided to be put into production operation, that is, constraint x i =1, thus ensuring that at the next moment, stores in production operation can continue production.

[0127] It should be noted that the above equations (2) to (10) are only one or more mathematical expressions used to express the various constraints mentioned above in this embodiment. Of course, other formulas or equations can also be used to express them, and this embodiment does not limit this.

[0128] In addition, in another embodiment, if Figure 4 As shown, the above method also includes:

[0129] Step 104: Divide the ranges of the N cargo collection areas by using separation marking lines.

[0130] In this embodiment, any one of a separation marker, a light projection line or a luminous line can be used as a separation marker line. Furthermore, the separation marker can be an object with a separation function, such as a partition, a baffle, etc. The light projection line can be projected to envelop each collection area by optical illumination, thereby achieving the function of separating and distinguishing different collection areas. In addition, modular luminous lines can be pre-set on the ground (the entire collection area), and the dynamic combination of the boundaries of different areas can be achieved by switching the luminous lines.

[0131] In this embodiment, dividing identification lines are used to color-code each of the N collection areas. When any truck arrives, a color correspondence can be established between its platform and the collection area covered by the truck's shipment. Each color corresponds to a collection area, making it easier for loaders to find the area and reducing the probability of identification errors.

[0132] Optionally, in another embodiment, the method further includes: when it is detected that the ranges of the currently determined N cargo collection areas are different from the ranges of the previously divided cargo collection areas, adjusting the position of the dividing identification line corresponding to at least one cargo collection area.

[0133] Since the N divided collection areas do not belong to a fixed store, the scope of the collection areas of each store is adjusted according to the demand for order containers of each store in the collection area, the change in upstream orders, or system decisions, so as to maximize the resource utilization efficiency of the collection area.

[0134] For example, based on the above objective function and various constraints, allocate and store the order containers in the corresponding goods consolidation areas with remaining storage space; or, create a new goods consolidation area in the remaining space and allocate the order containers to the newly created goods consolidation area; or, it is also possible to expand the scope of the currently full goods consolidation area so as to allocate the order containers to the expanded goods consolidation area. Among them, when creating a new goods consolidation area and expanding the scope of the existing goods consolidation area, it is necessary to adjust the position of the separation identification lines of the existing goods consolidation areas, that is, re-divide the positions of the separation identification lines for each goods consolidation area.

[0135] Combined with the above objective function and constraints, in a specific embodiment, assume that the store set O contains 5 stores, namely O = {Store 1, Store 2, Store 3, Store 4, Store 5}, and the area of the order containers required for each store to complete the current goods consolidation task is A = [60, 80, 20, 30, 200], with the unit being "square meters". In addition, the area of the goods consolidation area that has been allocated to Store 3 at the previous moment is 20 square meters, and o is the set of stores that have been allocated, o = {Store 3}.

[0136] In addition, the input parameters also include the size of the goods consolidation area and the size of the smallest cell. For example, assume that the total length of the goods consolidation area is 16 meters, the depth (width) is 10 meters, the length of the smallest container is 2 meters, and the size is 2×2, then it is determined that it can be divided into 8 smallest units, and the area of each smallest unit is 20 square meters. As Figure 5 shown, before the division of the scope of the goods consolidation area, there are a total of 8 smallest cells, namely S1 to S8, that is, the set of the smallest goods consolidation units S = {S1, S2, S3, S4, S5, S6, S7, S8}. In this embodiment, assume that the area of each smallest cell is equal, that is, the area of each smallest cell is area = 20 square meters.

[0137] Input the parameters defined above into the objective function of the foregoing relational expression (1), and at the same time use the foregoing relational expressions (2) to (10) to constrain each variable, and after processing, output the first decision value and the second decision value. As Figure 5 shown, the two ends connected by the solid line represent the corresponding relationship between the store and the smallest cell. In this example, the smallest cells S1, S2, and S3 are allocated to Store 1, and Store 1 starts production operations, that is, the value of the first decision variable x1 is 1; the corresponding smallest cells y 11 , y 21 and y 31 values (the second decision variable) are all 1, and the 3 cells are adjacent.

[0138] Similarly, the smallest cells S4, S5, S6, and S7 are assigned to store 2, and store 2 starts production operations, that is, the value of the first decision variable x2 is 1; the corresponding values of the smallest cells y 42 , y 52 , y 62 , and y 72 (the second decision variable) are all 1, and these 4 cells are adjacent. The smallest cell S8 is assigned to store 3, and store 3 starts production operations, that is, the value of the first decision variable x3 is 1; the corresponding value of the smallest cell y 83 (the second decision variable) is 1, where y 83 indicates that the smallest cell 8 is assigned for use by store 3. Additionally, the values of the first decision variables corresponding to stores 4 and 5 are both 0, indicating that production operations are not started at the current moment. At this time, after the allocation, the entire area of the consolidation area is used for storing goods, with no remaining area, and the storage rate is optimal.

[0139] See Figure 6 . The part pointed by the arrow is the divided consolidation area, which contains a total of 3 consolidation areas, and each consolidation area is represented by a different filling for easy distinction. Further, the separation identification lines shown in the entire consolidation area are:

[0140] When store 1 starts production operations, the area of the assigned consolidation area 1 is 3 * 20 = 60 square meters. The range of consolidation area 1 in the entire consolidation area 50 is: one luminous line is marked at the starting position 0, and another luminous line is marked at the second position 6 meters away from the starting position 0, corresponding to an area of 3 * 20 = 60 square meters. Similarly, the consolidation area assigned to store 2 is consolidation area 2, and the assigned area is 4 * 20 = 80 square meters. The range of this consolidation area 2 starts from the second position 6 meters and ends at the third position of 14 (14 = 6 + 4×2) meters, so the luminous line is marked at 14 meters at the third position. Finally, the range of consolidation area 3 assigned to store 3 is the area covered from the third position of 14 meters to the fourth position of 16 meters, corresponding to 20 square meters.

[0141] In this specific embodiment, information such as the current demand, the area of the consolidation area, the size and quantity of the smallest cells, etc. are input into the established objective function model. After processing by this objective function model and related constraint conditions, decision values are output. For example, it includes indicating the stores that start consolidation at this moment, and starting to allocate consolidation areas for these stores, as well as the area sizes of the consolidation areas allocated to these stores. Additionally, it also includes the positions of the separation identification lines for marking the boundaries of each consolidation area.

[0142] It should be understood that in this embodiment, only one configuration result is output after one input parameter is constrained by the objective function and the constraint conditions. Other parameters can also be input as needed and another configuration result can be output after processing. When the two output results are different, the position of the separation identification line of each goods consolidation area is adjusted, so as to realize the dynamic adjustment of the scope of each goods consolidation area.

[0143] On the one hand, the method provided in this embodiment reduces the space requirements for cross-docking production. By dynamically adjusting the scope of each goods consolidation area, the space requirements for production can be effectively reduced, and the storage rate of the goods consolidation area can be improved. Since the area of each goods consolidation area is no longer fixed, the waste of space caused by over-large initial division of a certain goods consolidation area is avoided.

[0144] On the other hand, this method also improves the efficiency of shipping and loading. By dynamically adjusting and managing the scope of the goods consolidation area, when shipping, the speed of manually searching for goods in the required goods consolidation area is faster, the error rate is lower, and the required transportation distance is shorter, thus improving the efficiency of shipping and loading.

[0145] The following introduces the device embodiment corresponding to the foregoing method embodiment.

[0146] Based on the foregoing Figure 2 shown method, this embodiment further provides a device for dividing a goods consolidation area, which is used to execute the method for dividing the goods consolidation area in the foregoing embodiment.

[0147] Specifically, as Figure 7 shown, the device includes: an acquisition unit 701, a determination unit 702, and a processing unit 703. In addition, the device may further include other more or fewer units / modules, such as a storage unit, a sending unit, and so on.

[0148] Among them, the acquisition unit 701 is used to acquire the number of stores and the area occupied by each store to complete the current order task.

[0149] The determination unit 702 is used to determine the size and quantity of the smallest cells according to the size of the entire goods consolidation area and the size of each order container, and each of the order containers is used to hold the goods corresponding to at least one order task.

[0150] The processing unit 703 is used to divide the entire goods consolidation area into N goods consolidation areas through objective function processing according to the number of stores, the area occupied by each store, the size and quantity of the smallest cells, where N≥1.

[0151] Among them, the objective function defines that the storage rate of the entire goods consolidation area is optimal in terms of time. Each of the N goods consolidation areas contains one or more smallest cells, and each of the goods consolidation areas corresponds to the order task of one store.

[0152] Optionally, in a specific implementation manner of this embodiment, the obtaining unit 701 is configured to perform the steps of obtaining the number of stores and the area required for each store to complete the current order task based on any one of the following situations. Any one of the following situations includes: at every other preset period, the order tasks of at least one store change, and the area required for the goods corresponding to the change reaches a certain threshold; or the loading rate of the entire goods collection area reaches a certain threshold.

[0153] Optionally, in another specific implementation manner of this embodiment, the obtaining unit 701 is further configured to obtain the area required for the goods and / or the loading rate of the entire goods collection area in the following manner. The following manner includes, but is not limited to: detecting the pressure in the entire goods collection area by using a pressure sensor, or collecting pictures of the entire goods collection area by using a camera.

[0154] Optionally, the processing unit 703 is further configured to output a first decision variable and a second decision variable after processing input parameters such as an objective function; wherein, the first decision variable is used to decide whether to enable production operations for each store at the current moment; the second decision variable is used to decide whether to enable storing order containers for each minimum cell, and the minimum cell belongs to one of the N goods collection areas.

[0155] Optionally, when the processing unit 703 processes the input parameters with an objective function, the objective function further constrains that: each store with a decision to start production operations is assigned at least one minimum cell; and / or, each minimum cell with a decision to be enabled is uniquely assigned to one of the N goods collection areas; and / or, two or more minimum cells that are decided to be enabled and assigned to the same store are adjacent to each other.

[0156] In addition, the processing unit 703 is further configured to constrain that the area of each of the goods collection areas is greater than or equal to the area required for the current order task of the store corresponding to the goods collection area.

[0157] Optionally, in another specific implementation manner of this embodiment, the processing unit 703 is further configured to divide the ranges of the N goods collection areas by using a separation identification line; wherein, the separation identification line is any one of a separation identification object, a light projection line, or a luminous line.

[0158] Furthermore, the device provided in this embodiment may further include a detection unit, which is configured to detect whether the ranges of the currently determined N goods collection areas are the same as those of the previously divided goods collection areas. When a difference is detected between the two, the processing unit 703 adjusts the positions of the separation identification lines corresponding to at least one goods collection area. For example, re-divide the positions of the separation identification lines of each goods collection area to meet the dynamic allocation requirements at the current moment.

[0159] In a specific implementation, an embodiment of the present invention further provides an electronic device, which may be the server in the foregoing embodiment and is used to implement all or part of the steps of the method for dividing the goods collection area.

[0160] As Figure 8 shown, it is a schematic structural diagram of an electronic device provided in this embodiment. It includes: at least one processor 110, a memory 120, and at least one interface 130. In addition, a communication bus 140 may be further included for connecting these components.

[0161] Among them, at least one processor 110 may be a CPU or a processing chip, which is configured to read and execute computer program instructions stored in the memory 120, so that at least one processor 110 can execute the method processes in the foregoing various embodiments.

[0162] The memory 120 may be a non-transitory memory, which may include a volatile memory, such as a high-speed random access memory (Random Access Memory, RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0163] At least one interface 130 includes an input / output interface and a communication interface. The communication interface may be a wired or wireless interface, so as to realize the communication connection between the electronic device and other devices. The input / output interface may be used to connect external devices, such as a display screen, a keyboard, etc.

[0164] In some embodiments, the memory 120 stores computer-readable program instructions. When the processor 110 reads and executes the program instructions in the memory 120, a method for dividing a goods collection area in the foregoing embodiments can be implemented.

[0165] In addition, this embodiment further provides a computer program product for storing computer-readable program instructions. When the instructions are executed by the processor 110, a method for dividing a goods collection area in the foregoing embodiments can be implemented.

[0166] In addition, this embodiment further provides a cross-docking management system, which includes a server and at least one terminal device. Among them, the server may be the electronic device shown in the foregoing Figure 8 and at least one terminal device may be a robot or a terminal device, such as a mobile phone, a PDA, and so on.

[0167] It should be noted that in the application, relational terms such as first and second are only used 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 term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0168] Each embodiment in this specification is described in a related manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized. In particular, for the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the description of the method embodiments.

[0169] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus or device), or used in combination with these instruction execution systems, apparatus or devices.

[0170] For this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus or device.

[0171] More specific examples (a non-exhaustive list) of computer-readable media include the following: electrical connections (electronic devices) having one or more wirings, portable computer diskettes (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber devices, and portable compact disc read-only memory (CDROM).

[0172] Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory. It should be understood that the various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof.

[0173] In the above-described embodiments, the multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well-known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), and the like.

[0174] The above-described embodiments of the present invention do not constitute a limitation on the scope of protection of the present invention.

Claims

1. A method for dividing a goods collection area, characterized in that The method includes: obtaining the number of stores and the area required for each store to complete the current order task; determining the size and number of the smallest cells according to the size of the entire goods collection area and the size of each order container, where each order container is used to hold the goods corresponding to at least one order task; dividing the entire goods collection area into N goods collection areas, N≥1, through objective function processing according to the number of stores, the area required for each store, the size and number of the smallest cells; wherein, the objective function defines the optimal storage rate of the entire goods collection area in terms of time based on the total area of the goods required by each store using the order containers, decision variables, and the area of the entire goods collection area, the decision variables are determined based on whether each store starts production operations at this moment, each of the N goods collection areas contains one or more smallest cells, and each goods collection area corresponds to the order task of one store; wherein, the obtaining the number of stores and the area required for each store to complete the current order task includes: performing the steps of obtaining the number of stores and the area required for each store to complete the current order task based on the pressure or picture collected by the sensor.

2. The method according to claim 1, wherein The performing the steps of obtaining the number of stores and the area required for each store to complete the current order task based on the pressure or picture collected by the sensor includes: performing the steps of obtaining the number of stores and the area required for each store to complete the current order task when the order task of at least one store changes and based on the pressure or the picture collected by the sensor, it is determined that the area required for the goods corresponding to the change reaches a certain threshold; performing the steps of obtaining the number of stores and the area required for each store to complete the current order task when it is determined based on the pressure or the picture collected by the sensor that the loading rate of the entire goods collection area reaches a certain threshold.

3. The method according to claim 2, wherein The area required for the goods, and / or, the loading rate of the entire goods collection area is obtained by the following method: detecting the pressure in the entire goods collection area using a pressure sensor, or collecting the picture in the entire goods collection area using a camera, to obtain the area required for the goods and / or the loading rate of the entire goods collection area.

4. The method according to claim 1, characterized in that After the objective function processing, it further includes: outputting a first decision variable and a second decision variable; the first decision variable is used to decide whether each store starts production operations at the current moment; the second decision variable is used to decide whether each smallest cell starts storing order containers, and the smallest cell belongs to one of the N goods collection areas.

5. The method according to claim 4, characterized in that, When the objective function is processed, the objective function also constrains: each store with a production operation started by decision is at least assigned one smallest cell; and / or, each smallest cell enabled by decision is uniquely assigned to one of the N goods collection areas; and / or, two or more smallest cells enabled by decision and assigned to the same store are adjacent to each other.

6. The method according to claim 4, characterized in that When being processed by the objective function, the objective function further constrains that: The area of each goods-gathering area is greater than or equal to the area required for the current order tasks of the store corresponding to the goods-gathering area.

7. The method according to any one of claims 1-6, characterized in that, The method further includes: Dividing the ranges of the N goods-gathering areas by using partition identification lines; Wherein, the partition identification line is any one of a partition identifier, a light projection ray, or a luminous line.

8. The method according to claim 7, wherein The method further includes: When it is detected that there are differences between the ranges of the currently determined N goods-gathering areas and the ranges of the previously partitioned goods-gathering areas, adjusting the positions of the partition identification lines corresponding to at least one goods-gathering area.

9. A device for dividing a goods collection area, characterized in that The device includes: An acquisition unit, configured to acquire the number of stores and the area required for each store to complete the current order tasks; A determination unit, configured to determine the size and quantity of the minimum cells according to the size of the entire goods-gathering area and the size of each order container, where each order container is used to hold the goods corresponding to at least one order task; A processing unit, configured to partition the entire goods-gathering area into N goods-gathering areas, N≥1, through objective function processing according to the number of stores, the area required for each store, the size and quantity of the minimum cells; Wherein, the objective function defines the optimal storage rate of the entire goods-gathering area in terms of time based on the total area of the goods used by each store using the order containers, decision variables, and the area of the entire goods-gathering area. The decision variables are determined based on whether each store starts production operations at this moment. Each of the N goods-gathering areas includes one or more minimum cells, and each goods-gathering area corresponds to the order tasks of one store; The acquisition unit is specifically configured to perform the steps of acquiring the number of stores and the area required for each store to complete the current order tasks based on the pressure or pictures collected by the sensor.

10. An electronic device, comprising: A processor and a memory, characterized in that The memory is used to store computer-executable instructions; The processor is configured to read the instructions from the memory and execute the instructions to implement the method according to any one of claims 1 to 8.

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