Method and device for combining picking tasks

Abstract

The present invention discloses a method and device for combining picking tasks, and relates to the field of warehousing and logistics technology. A specific implementation of the method includes: determining the similarity between any two picking tasks based on the positions of each storage point in each picking task; determining the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, and each point on the closed loop represents a picking task; and arbitrarily selecting a preset number of picking tasks from the closed loop for combination. This implementation can flexibly select the number of combinations of picking tasks, and make the combination reach the overall optimal, thereby improving the overall picking efficiency.

Description

Technical Field

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

[0002] In the prior art, an intelligent handling robot can only complete two picking tasks in one handling task, that is, one intelligent handling robot can place two picking boxes. When combining picking tasks, first select a seed picking task, and then select a picking task with the same lane as the seed picking task for combination; or, first calculate the similarity between each picking task and the picking storage location set of other picking tasks, and select the two picking tasks with the greatest similarity for combination each time. The handling tasks combined by the former are not the optimal picking path, and the latter only achieves the local optimum, but not the overall optimum. Summary of the invention

[0003] In view of this, an embodiment of the present invention provides a method and device for combining picking tasks, which can flexibly select the number of combinations of picking tasks and make the combination reach the overall optimum, thereby improving the overall picking efficiency.

[0004] According to one aspect of an embodiment of the present invention, a method for combining picking tasks is provided, comprising:

[0005] Determine the similarity between any two picking tasks based on the location of each storage point in each picking task;

[0006] According to the similarity between any two picking tasks, a closed loop corresponding to all picking tasks is determined based on the traveling salesman algorithm, where each point on the closed loop represents a picking task;

[0007] A preset number of picking tasks are selected continuously from the closed loop for combination.

[0008] Optionally, the any two picking tasks include a first picking task and a second picking task, and determining the similarity between the any two picking tasks includes:

[0009] For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance;

[0010] For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance;

[0011] The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

[0012] Optionally, according to the similarity between any two picking tasks, the closed loops corresponding to all picking tasks are determined based on the traveling salesman algorithm, including:

[0013] According to the similarity between any two picking tasks, the path cost after any two picking tasks are combined is determined;

[0014] Based on the traveling salesman algorithm, a combination order that minimizes the overall path cost of all picking tasks is determined to obtain closed loops corresponding to all picking tasks.

[0015] Optionally, determining the path cost of the combination of any two picking tasks according to the similarity between any two picking tasks includes: taking the similarity between the any two picking tasks as the path cost of the combination of the any two picking tasks.

[0016] According to a second aspect of an embodiment of the present invention, there is provided a device for combining picking tasks, comprising:

[0017] A similarity determination module determines the similarity between any two picking tasks based on the locations of each storage point in each picking task;

[0018] A task sorting module determines the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, where each point on the closed loop represents a picking task;

[0019] The task combination module selects a preset number of picking tasks from the closed loop for combination.

[0020] Optionally, the any two picking tasks include a first picking task and a second picking task, and the similarity determination module determines the similarity between the any two picking tasks, including:

[0021] For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance;

[0022] For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance;

[0023] The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

[0024] Optionally, the task sorting module determines the closed loops corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, including:

[0025] According to the similarity between any two picking tasks, the path cost after any two picking tasks are combined is determined;

[0026] Based on the traveling salesman algorithm, a combination order that minimizes the overall path cost of all picking tasks is determined to obtain closed loops corresponding to all picking tasks.

[0027] Optionally, the task sorting module determines the path cost of any two picking tasks combined according to the similarity between any two picking tasks, including: taking the similarity between the any two picking tasks as the path cost of any two picking tasks combined.

[0028] According to a third aspect of an embodiment of the present invention, there is provided an electronic device for picking task combination, comprising:

[0029] one or more processors;

[0030] a storage device for storing one or more programs,

[0031] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the embodiment of the present invention.

[0032] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0033] An embodiment of the above invention has the following advantages or beneficial effects: by determining the similarity between any two picking tasks and using the traveling salesman algorithm to determine the closed loop corresponding to all picking tasks, the number of combinations of picking tasks can be flexibly selected, and the combination can be made to reach the overall optimal, thereby improving the overall picking efficiency.

[0034] The further effects of the above-mentioned non-conventional optional manner will be described below in conjunction with specific implementation examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to better understand the present invention and do not constitute an improper limitation of the present invention.

[0036] Figure 1 is a schematic diagram of the main process of the method for combining picking tasks according to an embodiment of the present invention;

[0037] Figure 2 is a closed loop schematic diagram of a picking task combination in an optional embodiment of the present invention;

[0038] Figure 3 is a schematic diagram of a topological map of an intelligent transport robot in an optional embodiment of the present invention;

[0039] Figure 4 Schematic diagram of main modules of a device for combining picking tasks according to an embodiment of the present invention;

[0040] Figure 5 is an exemplary system architecture diagram to which embodiments of the present invention may be applied;

[0041] Figure 6 It is a schematic diagram of the structure of a computer system of a terminal device or a server suitable for implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The following is a description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0043] According to one aspect of an embodiment of the present invention, a method for combining picking tasks is provided.

[0044] Figure 1 FIG. 1 is a schematic diagram of the main process of the method for combining picking tasks according to an embodiment of the present invention. Figure 1 As shown, the method for combining picking tasks includes: step S101, step S102 and step S103.

[0045] In step S101, the similarity between any two picking tasks is determined based on the positions of each storage point in each picking task. This step traverses the storage positions of all picking tasks, and all storage positions of each picking task form a point set.

[0046] The method for determining the similarity can be selectively set according to actual conditions, such as a cosine similarity algorithm, an Euclidean distance algorithm, etc. Optionally, the arbitrary two picking tasks include a first picking task and a second picking task, and determining the similarity between the arbitrary two picking tasks includes:

[0047] For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance;

[0048] For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance;

[0049] The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

[0050] The smaller the similarity value, the greater the similarity between the two picking tasks.

[0051] If the storage point sets of the two picking tasks are:

[0052] M={m1,m2,…,m a}, N={n1,n2,…,n b}, then the similarity d between picking task M and picking task N H (M,N) can be defined as:

[0053]

[0054] In the formula, m represents the storage location in picking task M, a represents the number of storage locations in picking task M, n represents the storage location in picking task N, and b represents the number of storage locations in picking task N; d(m,n) represents the shortest driving distance between storage location m and storage location n.

[0055] For example, the similarity between any two picking tasks is shown in the following table:

[0056] Task 1 Task 2 Task 3 Task 4 Task 5 Task 6 … Task 1 50 30 100 70 60 … Task 2 60 10 55 70 … Task 3 80 30 40 … Task 4 60 70 … Task 5 60 … Task 6 … … …

[0057] In step S102, according to the similarity between any two picking tasks, a closed loop corresponding to all picking tasks is determined based on the Traveling Salesman Problem (TSP), where each point on the closed loop represents a picking task.

[0058] The present invention takes minimizing the overall distance as the goal, regards each picking task as a city, and the distance value between every two picking tasks as the distance between the two cities. Traversing all cities with the shortest route distance can be converted into an overall optimal solution that combines all picking tasks in sequence.

[0059] Optionally, according to the similarity between any two picking tasks, the closed loop corresponding to all picking tasks is determined based on the traveling salesman algorithm, including: according to the similarity between any two picking tasks, determining the path cost after any two picking tasks are combined; determining the combination sorting that minimizes the overall path cost of all picking tasks based on the traveling salesman algorithm, and obtaining the closed loop corresponding to all picking tasks. Since the path cost is usually positively correlated with the distance, and the distance is usually positively correlated with the distance between the two. H (M, N) are positively correlated, so the similarity between any two picking tasks can be used as the path cost after the combination of the two picking tasks, that is, using the d H (M, N) represents the path cost between the two.

[0060] set up:

[0061]

[0062] Find the shortest path cost, so the objective function can be set as:

[0063]

[0064] The constraints need to be met:

[0065] A vertex has only one incoming edge:

[0066] A vertex has only one outgoing edge:

[0067] The above constraints may result in multiple sub-loops, so each sub-loop generated needs to be added to the constraint conditions. The sub-loop vertex set is S, which is a non-empty subset of V. The constraint conditions for eliminating sub-loops are:

[0068]

[0069] Therefore, the objective function of the traveling salesman algorithm can be:

[0070]

[0071] Constraints:

[0072]

[0073]

[0074]

[0075]

[0076] Where V represents the set of all vertices in the closed loop; E represents the set of edges formed by any two vertices in the closed loop; S represents the set of vertices in the subloop; x ij represents the edge from vertex i to vertex j, x ij In the closed loop path, when x ij =1, x ij When x is not in the closed loop path ij =0;d ij The path cost from vertex i to vertex j.

[0077] After calculating the optimal solution through TSP, all picking tasks form a closed loop to achieve overall optimality.

[0078] Step S103: arbitrarily and continuously select a preset number of picking tasks from the closed loop for combination.

[0079] Figure 2 is a closed loop schematic diagram of a picking task combination in an optional embodiment of the present invention, such as Figure 2 As shown, the present invention combines all picking tasks into a closed loop, and each point on the loop represents a picking task. Any number of picking tasks can be continuously selected from the loop according to actual needs to form a transport task. For example, if 4 picking tasks can be transported at one time, 4 picking tasks are taken from the loop in order to form a transport task. For another example, 3 picking tasks are taken for combination in the first time, and 4 picking tasks are taken for combination in the second time.

[0080] The present invention determines the similarity between any two picking tasks and uses the traveling salesman algorithm to determine the closed loops corresponding to all picking tasks. It can flexibly select the number of combinations of picking tasks and make the combination reach the overall optimal, thereby improving the overall picking efficiency.

[0081] In the embodiment of the present invention, the generated transport task can be carried out by a machine (such as a transport vehicle or an intelligent transport robot) or manually. It should be noted that when the transport is carried out by a machine, the similarity and the closed loop are determined based on the travel path of the machine; when the transport is carried out manually, the similarity and the closed loop are determined based on the travel path of the operator.

[0082] The following takes the intelligent handling robot as an example. Figure 3 The picking task combination method of the embodiment of the present invention is exemplified. The intelligent handling robot body has the functions of SLAM (simultaneous localization and mapping) autonomous navigation, handling, obstacle avoidance, voice prompts, etc., which can assist the picking personnel in the unmanned warehouse to complete the handling tasks. Figure 3 A schematic diagram of the topological map of the intelligent handling robot is shown.

[0083] The picking task is sent from the upstream business system to the intelligent handling robot system. One picking task corresponds to one picking material box, and one handling task corresponds to multiple picking tasks, that is, the intelligent handling robot can complete multiple picking tasks in one handling. The goods corresponding to each picking task are distributed on several different shelves in the warehouse. The intelligent handling robot drives to the corresponding shelf storage location, and the picker puts the goods into the corresponding picking material box. When the picking lists of all picking material boxes are picked, the intelligent handling robot goes to the review table to unload the goods, completing a handling task.

[0084] At present, an intelligent handling robot can complete at most two picking tasks in one handling task, that is, an intelligent handling robot can place at most two picking material boxes, and the existing picking task combination method only supports the combination of two picking tasks. The present invention optimizes the picking task combination method of the intelligent handling robot, supports one handling task to be combined with any number of picking tasks, and makes the combination reach the overall optimal, thereby improving the overall picking efficiency.

[0085] According to a second aspect of an embodiment of the present invention, a device for implementing the above method is provided.

[0086] Figure 4 is a schematic diagram of the main modules of the device for picking task combination according to an embodiment of the present invention, such as Figure 4 As shown, the picking task combination device 400 includes:

[0087] A similarity determination module 401 determines the similarity between any two picking tasks according to the location of each storage point in each picking task;

[0088] The task sorting module 402 determines the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, where each point on the closed loop represents a picking task;

[0089] The task combination module 403 selects a preset number of picking tasks from the closed loop for combination.

[0090] Optionally, the any two picking tasks include a first picking task and a second picking task, and the similarity determination module determines the similarity between the any two picking tasks, including:

[0091] For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance;

[0092] For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance;

[0093] The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

[0094] Optionally, the task sorting module determines the closed loops corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, including:

[0095] According to the similarity between any two picking tasks, the path cost after any two picking tasks are combined is determined;

[0096] Based on the traveling salesman algorithm, a combination order that minimizes the overall path cost of all picking tasks is determined to obtain closed loops corresponding to all picking tasks.

[0097] Optionally, the task sorting module determines the path cost of any two picking tasks combined according to the similarity between any two picking tasks, including: taking the similarity between the any two picking tasks as the path cost of any two picking tasks combined.

[0098] According to a third aspect of an embodiment of the present invention, there is provided an electronic device for picking task combination, comprising:

[0099] one or more processors;

[0100] a storage device for storing one or more programs,

[0101] When the one or more programs are executed by the one or more processors, the one or more processors implement the method provided in the first aspect of the embodiment of the present invention.

[0102] According to a fourth aspect of an embodiment of the present invention, a computer-readable medium is provided, on which a computer program is stored, and when the program is executed by a processor, the method provided by the first aspect of the embodiment of the present invention is implemented.

[0103] Figure 5 An exemplary system architecture 500 is shown to which the method for combining picking tasks or the apparatus for combining picking tasks according to the embodiments of the present invention can be applied.

[0104] like Figure 5 As shown, system architecture 500 may include terminal devices 501, 502, 503, a network 504 and a server 505. Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0105] The user can use the terminal devices 501, 502, 503 to interact with the server 505 through the network 504 to receive or send messages, etc. Various communication client applications can be installed on the terminal devices 501, 502, 503, such as smart control applications, shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0106] The terminal devices 501 , 502 , and 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0107] The server 505 may be a server that provides various services, such as a background management server (only an example) that provides support for the intelligent control web pages browsed by the user using the terminal devices 501, 502, and 503. The background management server may analyze and process the received data such as the request for generating a handling task, and feed back the processing results (such as information on which picking tasks are combined - only an example) to the terminal device.

[0108] It should be noted that the method for combining picking tasks provided in the embodiment of the present invention is generally executed by the server 505 , and accordingly, the device for combining picking tasks is generally disposed in the server 505 .

[0109] It should be understood that Figure 5 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0110] Reference below Figure 6 , which shows a schematic diagram of the structure of a computer system 600 of a terminal device suitable for implementing an embodiment of the present invention. Figure 6 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0111] like Figure 6 As shown, the computer system 600 includes a central processing unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage part 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0112] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage section 608 as needed.

[0113] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, the above-mentioned functions defined in the system of the present invention are executed.

[0114] It should be noted that the computer-readable medium shown in the present invention may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0115] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present invention. In this regard, each box in the flow chart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flow chart, and the combination of the boxes in the block diagram or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0116] The modules involved in the embodiments of the present invention may be implemented in software or hardware. The modules described may also be set in a processor, for example, they may be described as: a processor comprising: a similarity determination module, which determines the similarity between any two picking tasks according to the positions of each storage point in each picking task; a task sorting module, which determines the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, and each point on the closed loop represents a picking task; a task combination module, which selects a preset number of picking tasks from the closed loop for combination. The names of these modules do not constitute a limitation on the modules themselves in some cases. For example, the task combination module may also be described as a "module for determining the similarity between any two picking tasks".

[0117] As another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiment; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by a device, the device includes: determining the similarity between any two picking tasks according to the positions of each storage point in each picking task; determining the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, and each point on the closed loop represents a picking task; and arbitrarily selecting a preset number of picking tasks from the closed loop for combination.

[0118] According to the technical solution of the embodiment of the present invention, by determining the similarity between any two picking tasks and using the traveling salesman algorithm to determine the closed loop corresponding to all picking tasks, the number of combinations of picking tasks can be flexibly selected, and the combination can be made to reach the overall optimal, thereby improving the overall picking efficiency.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for combining picking tasks, characterized in that: include: Determine the similarity between any two picking tasks based on the location of each storage point in each picking task; The similarity is determined based on the driving path or travel path of carrying the two picking tasks; According to the similarity between any two picking tasks, a closed loop corresponding to all picking tasks is determined based on the traveling salesman algorithm, where each point on the closed loop represents a picking task; Continuously selecting a preset number of picking tasks from the closed loop for combination; According to the similarity between any two picking tasks, the closed loop corresponding to all picking tasks is determined based on the traveling salesman algorithm, including: according to the similarity between any two picking tasks, the path cost after any two picking tasks are combined is determined; Based on the traveling salesman algorithm, a combination order that minimizes the overall path cost of all picking tasks is determined to obtain closed loops corresponding to all picking tasks.

2. The method according to claim 1, characterized in that The arbitrary two picking tasks include a first picking task and a second picking task, and determining the similarity between the arbitrary two picking tasks includes: For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance; For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance; The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

3. The method according to claim 1, characterized in that Determining the path cost after combining any two picking tasks according to the similarity between any two picking tasks includes: taking the similarity between the any two picking tasks as the path cost after combining the any two picking tasks.

4. A device for combining picking tasks, characterized in that: include: A similarity determination module determines the similarity between any two picking tasks based on the locations of each storage point in each picking task; The similarity is determined based on the driving path or travel path of carrying the two picking tasks; A task sorting module determines the closed loop corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, where each point on the closed loop represents a picking task; A task combination module, which selects a preset number of picking tasks from the closed loop and combines them; The task sorting module determines the closed loops corresponding to all picking tasks based on the traveling salesman algorithm according to the similarity between any two picking tasks, including: determining the path cost after any two picking tasks are combined according to the similarity between any two picking tasks; Based on the traveling salesman algorithm, a combination order that minimizes the overall path cost of all picking tasks is determined to obtain closed loops corresponding to all picking tasks.

5. The device according to claim 4, characterized in that The arbitrary two picking tasks include a first picking task and a second picking task, and determining the similarity between the arbitrary two picking tasks includes: For any storage location in the first picking task, determine the driving distance between the any storage location and each storage location in the second picking task, and take the minimum value of all driving distances as the distance between the any storage location and the second picking task; take the maximum value of the distance between each storage location in the first picking task and the second picking task as the first candidate distance; For any storage location in the second picking task, determine the driving distance between the any storage location and each storage location in the first picking task, and take the minimum value of all driving distances as the distance between the any storage location and the first picking task; take the maximum value of the distance between each storage location in the second picking task and the first picking task as the second candidate distance; The larger value of the first candidate distance and the second candidate distance is used as the similarity between the first picking task and the second picking task.

6. The device according to claim 4, characterized in that The task sorting module determines the path cost of the combination of any two picking tasks according to the similarity between any two picking tasks, including: taking the similarity between the any two picking tasks as the path cost of the combination of the any two picking tasks.

7. An electronic device for picking task combination, characterized in that: include: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 3.

8. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 3 is implemented.

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