Goods storing and taking method and goods storing and taking device for mobile robot warehousing system

Abstract

The invention provides a goods storing and taking method and a goods storing and taking device for a mobile robot warehousing system. The method comprises the steps of obtaining a storage parameter set of a warehouse in a first time period from a database in response to a cargo access instruction; constructing a cargo access model according to the storage parameter set; solving the region prediction model to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses the goods from the initial access region when executing the goods access task; solving the goods shelf prediction model to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses the goods from an initial storage goods shelf in the initial access area when executing the goods access task; and generating a target prediction result according to the first prediction result and the second prediction result, wherein the target prediction result represents that the mobile robot moves at least one target storage shelf between a workbench corresponding to the cargo access task and the position of the target storage shelf when the cargo access task in the second time period is executed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of logistics warehousing, and more specifically, to a cargo access method, cargo access device, electronic device, computer-readable storage medium, and computer program product for a mobile robot warehousing system. Background Art

[0002] With the rapid development of intelligent logistics and intelligent warehousing, mobile robot warehousing system, as a widely used and developed intelligent warehousing system, has been widely used in manufacturing, e-commerce and other enterprises. Mobile robot warehousing system uses robots to carry shelves instead of manual walking to complete storage and picking work. Manual pickers only need to complete the work of putting goods on shelves for storage and picking and taking out goods at fixed workbenches. Mobile robot warehousing system can greatly reduce labor costs and improve storage and retrieval efficiency.

[0003] At present, the use of mobile robot warehousing systems still relies on empirical rules. The system operation level needs to be optimized to improve the system's operating efficiency and reduce operating costs. Storage and picking are the two main tasks performed by the warehousing system, and are also the main links for optimizing the system. For storage and picking, the storage task is executed according to the supplier's plan and can be determined in advance; while the picking demand is based on the order demand and is generally unknown.

[0004] In the process of realizing the concept of the present disclosure, the inventors found that there are at least the following problems in the related art: the existing cargo storage and retrieval solutions may have a low warehouse turnover rate in actual use, thereby reducing the storage and retrieval speed of the cargo. Summary of the invention

[0005] In view of this, the embodiments of the present disclosure provide a cargo access method, a cargo access device, an electronic device, a computer-readable storage medium, and a computer program product for a mobile robot warehousing system.

[0006] One aspect of an embodiment of the present disclosure provides a cargo access method for a mobile robot warehousing system, comprising:

[0007] In response to the cargo access instruction, a storage parameter set of the warehouse in a first time period is obtained from a database, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, a plurality of access areas in the warehouse, the access areas include a plurality of storage shelves, and a type of cargo is stored in at least one of the access areas;

[0008] Constructing a cargo storage and access model according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model;

[0009] Solving the area prediction model to obtain a first prediction result, wherein the first prediction result indicates that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the plurality of access areas;

[0010] For each of the initial access areas, solving the shelf prediction model to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task;

[0011] A target prediction result is generated based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each of the target access area, and the target prediction result indicates that when the mobile robot performs the cargo access task in the second time period, it moves at least one target storage shelf between the workbench corresponding to the cargo access task and the position of the target storage shelf.

[0012] According to an embodiment of the present disclosure, the above-mentioned cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information;

[0013] The shelf prediction model is solved for each of the initial access areas to obtain a second prediction result, including:

[0014] For any one of the plurality of storage task information and the plurality of pickup task information, calculating the distance information between the workbench in the task information and any one of the initial access areas;

[0015] Based on the first access constraint condition, according to the distance information corresponding to each of the task information, a driving cost of a first sub-prediction result is generated, wherein the first sub-prediction result corresponds to a target storage shelf of each task information, and the driving cost represents a total distance moved by the plurality of mobile robots to perform the cargo access task;

[0016] A first sub-prediction result corresponding to the minimum value among the plurality of travel costs is determined as the second prediction result.

[0017] According to an embodiment of the present disclosure, for each initial access area, the minimum value of the above travel cost is as shown in formula (1):

[0018]

[0019] Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; 0-1 decision variables Indicates that in the tth period, to complete the inventory task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise 0-1 decision variable Indicates that in the tth period, to complete the picking task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise r im Indicates the distance information from workbench i to access area m.

[0020] According to an embodiment of the present disclosure, the first access constraint condition is as shown in formula (2) to formula (14):

[0021]

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Where n is the index of the goods, For the collection of goods; and is the first predicted result, a continuous variable represents the number of goods n stored on the initial storage shelf j through the workbench i in the tth period; Formula (2) indicates that in the area, all arriving goods must be stored on the initial storage shelf; continuous variable represents the number of goods n taken out from the initial storage shelf j by workbench i in period t; Formula (3) indicates that the products taken out from the initial access area m are the sum of the products taken out from all shelves in the area; continuous variable represents the quantity of goods n on the initial storage shelf j in period t; Formula (4) indicates that at the beginning of period t+1, the inventory quantity of initial storage shelf j is the inventory quantity of goods n in period t plus the quantity of goods deposited minus the quantity of goods taken out; s n Indicates the unit specification of the goods n, S j represents the unit capacity of the initial storage shelf j; formula (5) indicates that the inventory on the initial storage shelf j does not exceed the capacity of the initial storage shelf j; formula (6) and formula (7) indicate that in any period t, if there is a need to store goods on the initial storage shelf j, the initial storage shelf j must be moved, where is a preset value; formula (8) indicates that in any period, an initial storage shelf can only be moved between the initial area and a workbench; formulas (9) and (10) indicate that in period t, if there is a need to take goods out from initial storage shelf j, initial shelf j must be moved; formula (11) indicates that in period t, initial storage shelf j can only be moved between the initial area and a workbench.

[0035] According to an embodiment of the present disclosure, the above-mentioned cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information;

[0036] The above-mentioned regional prediction model is solved to obtain the first prediction result, including:

[0037] For any one of the plurality of storage task information and the plurality of pickup task information, calculating the cost information between the workbench in the task information and any one of the initial access areas;

[0038] Based on the second access constraint condition, according to the cost information corresponding to each of the task information, a total cost of a second sub-prediction result is generated, wherein the second sub-prediction result includes an initial access area corresponding to each of the task information, and the total cost represents the cost consumed by the plurality of mobile robots to execute the cargo access task information;

[0039] A second sub-prediction result corresponding to the minimum value among the plurality of total costs is determined as the first prediction result.

[0040] According to an embodiment of the present disclosure, the above total cost is shown in formula (15):

[0041]

[0042] Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; r im Represents the distance information from workbench i to access area m; continuous variable represents the number of goods n stored in access area m via workbench i in period t, It represents the number of goods n taken out from the access area m through the workbench i in the tth period.

[0043] According to an embodiment of the present disclosure, the second access constraint condition is as shown in formula (16) to formula (20):

[0044]

[0045]

[0046]

[0047]

[0048]

[0049] in, represents the quantity demanded for good n at workstation i in period t, represents the inventory quantity of good n in access area m in period t; S represents the capacity of the initial storage shelf; formula (16) indicates that all arriving goods must be stored in the access area; formula (17) indicates that the goods required for all orders must be taken out of the access area; formula (18) indicates that at the beginning of period t+1, the inventory quantity of goods in access area m is the inventory quantity of good n in period t plus the quantity of goods stored minus the quantity of goods taken out; formula (19) indicates that the stored products cannot exceed the capacity of access area m.

[0050] According to an embodiment of the present disclosure, the cargo access method further includes:

[0051] The target prediction result is adjusted based on the actual cargo storage and retrieval task in the second time period to obtain an adjusted target prediction result.

[0052] According to an embodiment of the present disclosure, the target prediction result is adjusted based on the actual cargo access task in the second time period to obtain the adjusted target prediction result, including:

[0053] In the case that the quantity of goods in at least one target storage shelf in the target prediction result does not meet the goods demand of the actual goods access task, the associated storage shelf is added to the target prediction result to obtain the adjusted target prediction result, wherein the associated storage shelf and the target storage shelf are located in the same target access area;

[0054] When the quantity of goods in all target storage shelves in the above access area in the above target prediction result does not meet the goods demand of the above actual goods access task, the associated access area is added to the above target prediction result to obtain the above adjusted target prediction result, wherein the goods stored in the above associated storage area are the same as those stored in the above target storage area.

[0055] Another aspect of the disclosed embodiments provides a cargo storage and retrieval device for a mobile robot storage system, comprising:

[0056] an acquisition module, configured to obtain, in response to a cargo access instruction, a storage parameter set of a warehouse within a first time period from a database, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, a plurality of access areas within the warehouse, the access areas including a plurality of storage shelves, and a type of cargo stored in at least one of the access areas;

[0057] A construction module, used to construct a cargo storage and access model according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model;

[0058] A first prediction module is used to solve the regional prediction model to obtain a first prediction result, wherein the first prediction result indicates that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the plurality of access areas;

[0059] A second prediction module is used to solve the shelf prediction model for each of the initial access areas to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task;

[0060] A generation module is used to generate a target prediction result based on the above-mentioned first prediction result and the above-mentioned second prediction result, wherein the above-mentioned target prediction result includes at least one target access area and at least one target storage shelf corresponding to each of the above-mentioned target access area, and the above-mentioned target prediction result represents that when performing the cargo access task in the second time period, the above-mentioned mobile robot moves at least one target storage shelf between the workbench corresponding to the above-mentioned cargo access task and the position of the above-mentioned target storage shelf.

[0061] Another aspect of an embodiment of the present disclosure provides an electronic device, comprising: one or more processors; and a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0062] Another aspect of an embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method described above when executed.

[0063] Another aspect of an embodiment of the present disclosure provides a computer program product, wherein the computer program product includes computer executable instructions, and the instructions are used to implement the method described above when executed.

[0064] According to an embodiment of the present disclosure, a cargo access model is constructed according to a storage parameter set of a warehouse within a first time period, and the regional prediction model is solved to obtain a first prediction result representing the initial access area in the warehouse when accessing the storage shelf. The shelf prediction model is solved to obtain a second prediction result representing the movement of the target storage shelf between the workbench and the position of the target storage shelf when executing the cargo access task information. Since all workbenches, cargo storage tasks and storage shelves in all storage parameter sets are taken into account in the prediction process, and the cargo access task is divided into two stages to determine the target prediction result, when executing the cargo access task based on the target prediction result, the turnover efficiency of the storage shelves in the warehouse can be effectively planned and the access speed of the goods can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0066] Figure 1 An exemplary system architecture to which the cargo access method according to an embodiment of the present disclosure can be applied is schematically shown;

[0067] Figure 2 A flowchart of a cargo storage and retrieval method according to an embodiment of the present disclosure is schematically shown;

[0068] Figure 3 A block diagram schematically shows a cargo storage and retrieval device according to an embodiment of the present disclosure;

[0069] Figure 4 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0070] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0071] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise", "include", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0072] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0073] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0074] The embodiments of the present disclosure provide a cargo access method and cargo access device for a mobile robot warehousing system. The method includes, in response to a cargo access instruction, obtaining a storage parameter set of a warehouse in a first time period from a database, wherein the storage parameter set includes multiple workbenches, cargo access task information, multiple access areas in the warehouse, the access area includes multiple storage shelves, and a type of cargo is stored in at least one access area; constructing a cargo access model according to the storage parameter set, wherein the cargo access model includes a regional prediction model and a shelf prediction model; solving the regional prediction model to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses cargo from an initial access area when performing a cargo access task, wherein the initial access area is one of multiple access areas at least one; for each initial access area, solving the shelf prediction model to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task; generating a target prediction result based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each target access area, and the target prediction result represents that the mobile robot moves at least one target storage shelf between the workbench corresponding to the goods access task and the position of the target storage shelf when performing the goods access task in the second time period.

[0075] Figure 1 The exemplary system architecture 100 to which the cargo access method according to the embodiment of the present disclosure can be applied is schematically shown. It should be noted that: Figure 1 What is shown is merely an example of a system architecture to which the embodiments of the present disclosure can be applied, in order to help those skilled in the art understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0076] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0077] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social platform software, etc. (only for example).

[0078] The first terminal device 101, the second terminal device 102, and the third terminal device 103 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, and desktop computers.

[0079] The server 105 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. The background management server may analyze and process the received data such as user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

[0080] It should be noted that the cargo access method provided in the embodiment of the present disclosure can generally be executed by the server 105. Accordingly, the cargo access device provided in the embodiment of the present disclosure can generally be set in the server 105. The cargo access method provided in the embodiment of the present disclosure can also be executed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the cargo access device provided in the embodiment of the present disclosure can also be set in a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Alternatively, the cargo access method provided in the embodiment of the present disclosure can also be executed by the first terminal device 101, the second terminal device 102, the third terminal device 103, or can also be executed by other terminal devices different from the first terminal device 101, the second terminal device 102, and the third terminal device 103. Correspondingly, the cargo storage and retrieval device provided in the embodiment of the present disclosure may also be arranged in the first terminal device 101, the second terminal device 102, the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102, the third terminal device 103.

[0081] It should be understood that Figure 1The 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.

[0082] Figure 2 The flowchart of the cargo storage and retrieval method according to the embodiment of the present disclosure is schematically shown.

[0083] like Figure 2 As shown, the cargo access method for the mobile robot warehousing system includes operations S201 to S205.

[0084] In operation S201, in response to a cargo access instruction, a storage parameter set of a warehouse in a first time period is obtained from a database, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, and a plurality of access areas in the warehouse, the access areas include a plurality of storage shelves, and a type of cargo is stored in at least one access area;

[0085] In operation S202, a cargo storage and access model is constructed according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model;

[0086] In operation S203, the area prediction model is solved to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the multiple access areas;

[0087] In operation S204, for each initial access area, the shelf prediction model is solved to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task;

[0088] In operation S205, a target prediction result is generated based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each target access area, and the target prediction result represents that when performing the cargo access task in the second time period, the mobile robot moves at least one target storage shelf between a workbench corresponding to the cargo access task and the position of the target storage shelf.

[0089] According to an embodiment of the present disclosure, the cargo access instruction may be generated in response to an input operation of a staff member on an electronic device such as a computer, or may be automatically generated by the electronic device at a preset time.

[0090] According to an embodiment of the present disclosure, the first time period is earlier than the second time period, for example, the first time period is today and the second time period is tomorrow.

[0091] According to an embodiment of the present disclosure, the workbench includes the number of workbenches, the location of each workbench, the type of task performed by the workbench, etc. The cargo storage and retrieval task information includes inventory task information and picking task information. The inventory task information includes the type and number of cargo to be stored, the workbench performing the inventory task, and the time when the inventory task is performed; the picking task information includes the type and number of cargo to be picked up, the workbench performing the picking task, and the time when the picking task is performed. The storage and access area in the warehouse includes the number of shelves, the capacity and location of the shelves, the type of cargo, the quantity of each type of cargo, the inventory type and quantity of cargo on each shelf, and the cargo location information of the storage shelf. The number of mobile robots can be specifically set according to actual needs. Preferably, a mobile robot is arranged under each storage shelf so that the mobile robot can move the storage shelf.

[0092] According to an embodiment of the present disclosure, a regional prediction model and a shelf prediction model are respectively constructed according to a storage parameter set to obtain a cargo access model, and the regional prediction model is then solved to obtain a first prediction result, which can characterize that for different cargo access task information, the storage shelf taken out from the i-th workbench can be stored in the m-th initial access area or the storage shelf in the m-th initial access area can be moved to the i-th workbench.

[0093] According to an embodiment of the present disclosure, for each movement scheme determined by the first prediction result, the shelf prediction model is solved to obtain a second prediction result, which can represent that the storage shelf taken out from the i-th workbench is stored in the j-th storage shelf position in the m-th initial access area, or the storage shelf at the j-th storage shelf position in the m-th initial access area is moved to the i-th workbench. The target prediction result of the second time period is generated based on the first prediction result and the second prediction result, thereby determining that when performing the cargo access task in the second time period, the mobile robot is required to move at least one target storage shelf between the workbench corresponding to the cargo access task and the position of the target storage shelf.

[0094] According to an embodiment of the present disclosure, a cargo access model is constructed according to a storage parameter set of a warehouse within a first time period, and the regional prediction model is solved to obtain a first prediction result representing the initial access area in the warehouse when accessing the storage shelf. The shelf prediction model is solved to obtain a second prediction result representing the movement of the target storage shelf between the workbench and the position of the target storage shelf when executing the cargo access task information. Since all workbenches, cargo storage tasks and storage shelves in all storage parameter sets are taken into account in the prediction process, and the cargo access task is divided into two stages to determine the target prediction result, the turnover efficiency of the storage shelves in the warehouse and the access speed of the goods can be effectively improved when the cargo access task is performed based on the target prediction result.

[0095] According to an embodiment of the present disclosure, the cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information;

[0096] For each initial access area, the shelf prediction model is solved to obtain a second prediction result, including:

[0097] For any one of the plurality of storage task information and the plurality of pickup task information, calculate the distance information between the workbench in the task information and any one of the initial access areas;

[0098] Based on the first access constraint condition, according to a distance information corresponding to each task information, a driving cost of a first sub-prediction result is generated, wherein the first sub-prediction result corresponds to a target storage shelf of each task information, and the driving cost represents a total distance moved by the multiple mobile robots to perform the cargo access task information;

[0099] A first sub-prediction result corresponding to the minimum value among the plurality of travel costs is determined as the second prediction result.

[0100] According to an embodiment of the present disclosure, for each initial access area, for each task information, if it is determined that the task information requires the access of goods from the mth initial access area, the driving distance between the workbench corresponding to the task information and each initial storage shelf in the mth initial access area, that is, the driving cost, is calculated.

[0101] According to an embodiment of the present disclosure, multiple access plans can be determined according to the driving distances of multiple task information. For example, there are 2 task information within a period, which are the picking tasks of workbench 1 and workbench 2 respectively. At this time, it is determined that the two workbenches pick up goods from initial access area 1 and initial access area 2 respectively. Each initial access area has 3 initial storage shelves. Therefore, there are 3 picking plans for workbench 1: workbench 1-initial storage shelf 1 of initial access area 1, workbench 1-initial storage shelf 2 of initial access area 1, and workbench 1-initial storage shelf 3 of initial access area 1. There are 3 picking plans for workbench 2: workbench 2-initial storage shelf 1 of initial access area 2, workbench 2-initial storage shelf 2 of initial access area 2, and workbench 2-initial storage shelf 3 of initial access area 2.

[0102] According to an embodiment of the present disclosure, based on the above scheme, it can be determined that there are a total of 3*3=9 first sub-prediction results for the cargo storage and retrieval task information, and the first sub-prediction result corresponding to the minimum value of the driving cost among the 9 types can be determined as the second prediction result for the current period.

[0103] According to an embodiment of the present disclosure, for each of the initial access areas, the minimum value of the driving cost is as shown in formula (1):

[0104]

[0105] Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; 0-1 decision variables Indicates that in the tth period, to complete the inventory task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise 0-1 decision variable Indicates that in the tth period, to complete the picking task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise r im Indicates the distance information from workbench i to access area m.

[0106] According to an embodiment of the present disclosure, since there may be multiple cargo storage and retrieval tasks within the first time period, the first time period can be divided into multiple periods. For example, for today's cargo storage and retrieval task information, each hour can be divided into a period. Of course, the periods can also be divided according to other standards. For example, every 5 minutes can be divided into a period. The division standard can be set according to actual needs, mainly based on the busyness of the warehouse.

[0107] According to an embodiment of the present disclosure, the first access constraint condition is as shown in formula (2) to formula (14):

[0108]

[0109]

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116]

[0117]

[0118]

[0119]

[0120]

[0121] Where n is the index of the goods, For the collection of goods; and is the first predicted result, a continuous variable represents the number of goods n stored on the initial storage shelf j through the workbench i in the tth period; Formula (2) indicates that in the area, all arriving goods must be stored on the initial storage shelf; continuous variable represents the number of goods n taken out from the initial storage shelf j by workbench i in period t; Formula (3) indicates that the products taken out from the initial access area m are the sum of the products taken out from all shelves in the area; continuous variable represents the quantity of goods n on the initial storage shelf j in period t; Formula (4) indicates that at the beginning of period t+1, the inventory quantity of initial storage shelf j is the inventory quantity of goods n in period t plus the quantity of goods deposited minus the quantity of goods taken out; s n Indicates the unit specification of the goods n, S j represents the unit capacity of the initial storage shelf j; formula (5) indicates that the inventory on the initial storage shelf j does not exceed the capacity of the initial storage shelf j; formula (6) and formula (7) indicate that in any period t, if there is a need to store goods on the initial storage shelf j, the initial storage shelf j must be moved, where is a preset value; formula (8) indicates that in any period, an initial storage shelf can only be moved between the initial area and a workbench; formulas (9) and (10) indicate that in period t, if there is a need to take goods out from initial storage shelf j, initial shelf j must be moved; formula (11) indicates that in period t, initial storage shelf j can only be moved between the initial area and a workbench.

[0122] According to an embodiment of the present disclosure, in any access area m, the solution obtained by the first prediction result is Further optimization is performed to the initial storage shelf level, where the goods are stored or retrieved on a specific initial storage shelf in the area. At this time, the travel cost is determined based on the first access constraint condition of formula (2) to formula (14), thereby minimizing the minimum distance from the initial storage shelf to store and retrieve all goods through formula (1).

[0123] According to an embodiment of the present disclosure, the cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information;

[0124] Wherein, solving the regional prediction model to obtain the first prediction result includes:

[0125] For any one of the plurality of storage task information and the plurality of pickup task information, cost information between the workbench in the task information and any one of the initial access areas is calculated;

[0126] Based on the second access constraint condition, according to the cost information corresponding to each task information, a total cost of a second sub-prediction result is generated, wherein the second sub-prediction result includes an initial access area corresponding to each task information, and the total cost represents the cost consumed by the multiple mobile robots to execute the cargo access task information;

[0127] A second sub-prediction result corresponding to a minimum value among the plurality of total costs is determined as the first prediction result.

[0128] According to an embodiment of the present disclosure, for each task information, the cost information between the workbench in the task information and the initial access area storing the goods required by the workbench is first calculated, for example, the cost information may be the driving distance. Based on the second access constraint condition, according to a piece of cost information corresponding to each task information, the total cost of multiple second sub-prediction results of multiple task information is determined, so that the second sub-prediction result corresponding to the minimum value in the total cost is determined as the first prediction result, thereby determining which initial access area each task information in the goods access task information specifically corresponds to, so as to determine it as the target access area of ​​the first prediction result, so as to facilitate the later determination of which target storage shelf in the target access area each task information corresponds to based on the first prediction result.

[0129] According to an embodiment of the present disclosure, the total cost is shown in formula (15):

[0130]

[0131] Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; r im Represents the distance information from workbench i to access area m; continuous variable represents the number of goods n stored in access area m via workbench i in period t, It represents the number of goods n taken out from the access area m through the workbench i in the tth period.

[0132] According to an embodiment of the present disclosure, the second access constraint condition is as shown in formula (16) to formula (20):

[0133]

[0134]

[0135]

[0136]

[0137]

[0138] in, represents the quantity demanded for good n at workstation i in period t, = represents the inventory quantity of item n in access area m at period t; S represents the capacity of the initial storage shelf; formula (16) indicates that all arriving goods must be stored in the access area; formula (17) indicates that all order goods must be taken out of the access area; formula (18) indicates that at the beginning of period t+1, the inventory quantity of goods in access area m is the inventory quantity of goods n in period t plus the quantity of goods stored minus the quantity of goods taken out; formula (19) indicates that the stored products cannot exceed the capacity of access area m. Formula (20) represents a continuous variable The value range of .

[0139] According to an embodiment of the present disclosure, the cargo access method further includes:

[0140] The target prediction result is adjusted based on the actual cargo storage and retrieval task in the second time period to obtain an adjusted target prediction result.

[0141] According to an embodiment of the present disclosure, since the actual cargo storage and retrieval tasks of tomorrow are not clear today (i.e., the storage and retrieval of cargo is uncertain), the target prediction results determined by the present disclosure may not be able to complete the actual cargo storage and retrieval tasks of tomorrow. Therefore, after determining the actual cargo storage and retrieval tasks of tomorrow, the target prediction results can be adjusted to obtain adjusted target prediction results, and the adjusted target prediction results can better complete the actual cargo storage and retrieval tasks of tomorrow.

[0142] According to an embodiment of the present disclosure, the target prediction result is adjusted based on the actual cargo storage and retrieval task in the second time period to obtain the adjusted target prediction result, including:

[0143] When the quantity of goods in at least one target storage shelf in the target prediction result does not meet the goods demand of the actual goods access task, adding the associated storage shelf to the target prediction result to obtain an adjusted target prediction result, wherein the associated storage shelf and the target storage shelf are located in the same target access area;

[0144] When the quantity of goods in all target storage shelves in the access area in the target prediction result does not meet the goods demand of the actual goods access task, the associated access area is added to the target prediction result to obtain an adjusted target prediction result, wherein the goods stored in the associated storage shelves are the same as those stored in the target storage shelves.

[0145] According to the embodiments of the present disclosure, for example, if the storage space on a designated storage shelf is insufficient, the storage shelf with storage space that can accommodate the remaining stock in the same access area will be used for storage. If there is no storage shelf that can accommodate the remaining stock in the same access area, the storage shelf that can accommodate the remaining stock will be taken from the access area with the shortest distance. The solution result of the goods access model will also be inconsistent with the actual number of goods to be picked up. If the stock quantity on the storage shelf is insufficient, the goods will be taken from other storage shelves with sufficient stock in the same access area; if all the storage shelves in the access area are insufficient to take out the goods in one time, the goods will be taken from two storage shelves; if all the storage shelves in the access area are insufficient to take out the goods in two times, the goods will be taken from other nearest access areas according to the same rules, and so on.

[0146] According to the embodiment of the present disclosure, the two-stage solution (TSF) proposed in the present disclosure is compared under different parameters (ρ is the ratio of demand change, M is the number of access areas, and T is the number of planning periods), the solution for directly solving the original problem (DET), the existing classification storage solution (VBS, where VBS1 is to divide the access area according to the ratio of 1:1:1:1, VBS1 is to divide the access area according to the ratio of 1:2:3:4), and the random storage solution (RDS), and RDS is used as the comparison benchmark. The improvement percentage of strategy P is calculated according to the formula (RDS-P) / RDS×100%. The parameters of the mobile robot warehousing system in the embodiment are set as follows: the number of storage shelves is set to 200, the storage shelf capacity is set to 200 units, the number of goods is 100, and in the initial state, each storage shelf has 5 kinds of goods and the inventory quantity of each kind of goods is 20, and 5 goods are stored and taken out in each period. There are two workbenches in the warehouse, and all storage and access areas are divided into 4. The distance from the workbench to each access area is shown in Table 1.

[0147] Table 1

[0148] area Workbench 1 Workbench 2 A 47.49 47.69 B 20.72 21.45 C 55.90 51.90 D 41.27 37.27

[0149] Table 2 shows the improvement of different schemes compared with the random storage scheme. TSF improves by up to 50%, while DET, the solution to the original problem, is about 20% to 30%. Table 3 shows the running time of TSF and DET. TSF can get the result in three seconds, while DET's running time is much longer than TSF.

[0150] Table 1. Effect comparison

[0151]

[0152] Table 2. Comparison of running time

[0153]

[0154] Figure 3 A block diagram of a cargo storage and retrieval device according to an embodiment of the present disclosure is schematically shown.

[0155] like Figure 3 As shown, the cargo storage and retrieval device 300 for a mobile robot warehousing system includes an acquisition module 310 , a construction module 320 , a first prediction module 330 , a second prediction module 340 , and a generation module 350 .

[0156] The acquisition module 310 is used to obtain a storage parameter set of the warehouse in the first time period from the database in response to the cargo access instruction, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, and a plurality of access areas in the warehouse, the access area includes a plurality of storage shelves, and a type of cargo is stored in at least one access area;

[0157] A construction module 320, configured to construct a cargo storage and access model according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model;

[0158] A first prediction module 330 is used to solve the regional prediction model to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the multiple access areas;

[0159] A second prediction module 340 is used to solve the shelf prediction model for each initial access area to obtain a second prediction result, wherein the second prediction result represents the mobile robot accessing goods from the initial storage shelf in the initial access area when performing the goods access task;

[0160] A generation module 350 is used to generate a target prediction result based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each target access area, and the target prediction result represents that when performing the cargo access task in the second time period, the mobile robot moves at least one target storage shelf between the workbench corresponding to the cargo access task and the position of the target storage shelf.

[0161] According to an embodiment of the present disclosure, a cargo access model is constructed according to a storage parameter set of a warehouse within a first time period, and the regional prediction model is solved to obtain a first prediction result representing the initial access area in the warehouse when accessing the storage shelf. The shelf prediction model is solved to obtain a second prediction result representing the movement of the target storage shelf between the workbench and the position of the target storage shelf when executing the cargo access task information. Since all workbenches, cargo storage tasks and storage shelves in all storage parameter sets are taken into account in the prediction process, and the cargo access task is divided into two stages to determine the target prediction result, the turnover efficiency of the storage shelves in the warehouse and the access speed of the goods can be effectively improved when the cargo access task is performed based on the target prediction result.

[0162] According to an embodiment of the present disclosure, the cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information;

[0163] According to an embodiment of the present disclosure, the second prediction module 340 includes a first calculation unit, a first generation unit, and a first determination unit.

[0164] A first calculation unit is used to calculate the distance information between the workbench in the task information and any initial access area for any one of the plurality of storage task information and the plurality of pickup task information;

[0165] A first generating unit is used to generate a travel cost of a first sub-prediction result based on a first access constraint condition and a distance information corresponding to each task information, wherein the first sub-prediction result corresponds to a target storage shelf of each task information, and the travel cost represents a total distance moved by the multiple mobile robots to execute the cargo access task information;

[0166] The first determining unit is used to determine a first sub-prediction result corresponding to a minimum value among the multiple driving costs as a second prediction result.

[0167] According to an embodiment of the present disclosure, the cargo storage and retrieval task information includes a plurality of storage task information and a plurality of pickup task information.

[0168] According to an embodiment of the present disclosure, the first prediction module 330 includes a second calculation unit, a second generation unit, and a second determination unit.

[0169] A second calculation unit is used to calculate, for any one of the plurality of storage task information and the plurality of pickup task information, cost information between the workbench in the task information and any one of the initial access areas;

[0170] A second generating unit is used to generate a total cost of a second sub-prediction result based on a second access constraint condition and a cost information corresponding to each task information, wherein the second sub-prediction result includes an initial access area corresponding to each task information, and the total cost represents a cost consumed by the plurality of mobile robots to execute the cargo access task information;

[0171] The second determining unit is used to determine the second sub-prediction result corresponding to the minimum value of the multiple total costs as the first prediction result.

[0172] According to an embodiment of the present disclosure, the cargo storage and retrieval device 300 further includes an adjustment module.

[0173] The adjustment module is used to adjust the target prediction result based on the actual cargo storage and retrieval task in the second time period to obtain an adjusted target prediction result.

[0174] According to an embodiment of the present disclosure, the adjustment module includes a first adjustment unit and a second adjustment unit.

[0175] A first adjustment unit is used to add an associated storage shelf to the target prediction result to obtain an adjusted target prediction result when the quantity of goods in at least one target storage shelf in the target prediction result does not meet the goods demand of the actual goods storage and retrieval task, wherein the associated storage shelf and the target storage shelf are located in the same target access area;

[0176] The second adjustment unit is used to add the associated access area to the target prediction result to obtain an adjusted target prediction result when the quantity of goods in all target storage shelves in the access area in the target prediction result does not meet the goods demand of the actual goods access task, wherein the goods stored in the associated storage shelves are the same as those stored in the target storage shelves.

[0177] According to the modules and units of the embodiments of the present disclosure, any multiple or at least part of the functions of any multiple thereof can be implemented in one module. According to the modules and units of the embodiments of the present disclosure, any one or more can be split into multiple modules for implementation. According to the modules and units of the embodiments of the present disclosure, any one or more can be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a System on a Chip, a System on a Substrate, a System on a Package, an Application Specific Integrated Circuit (ASIC), or can be implemented by hardware or firmware of any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation modes of software, hardware and firmware or in any appropriate combination of any of them. Alternatively, according to the modules and units of the embodiments of the present disclosure, one or more can be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding function can be executed.

[0178] For example, any multiple of the acquisition module 310, the construction module 320, the first prediction module 330, the second prediction module 340, and the generation module 350 can be combined in one module / unit for implementation, or any one of the modules / units can be split into multiple modules / units. Alternatively, at least part of the functions of one or more of these modules / units can be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit. According to an embodiment of the present disclosure, at least one of the acquisition module 310, the construction module 320, the first prediction module 330, the second prediction module 340, and the generation module 350 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or implemented in any one of the three implementation methods of software, hardware, and firmware or in a suitable combination of any of them. Alternatively, at least one of the acquisition module 310 , the construction module 320 , the first prediction module 330 , the second prediction module 340 , and the generation module 350 may be at least partially implemented as a computer program module, which may perform corresponding functions when executed.

[0179] It should be noted that the cargo storage and retrieval device part in the embodiment of the present disclosure corresponds to the cargo storage and retrieval method part in the embodiment of the present disclosure. The description of the cargo storage and retrieval device part specifically refers to the cargo storage and retrieval method part, which will not be repeated here.

[0180] Figure 4 A block diagram of an electronic device suitable for implementing the method described above according to an embodiment of the present disclosure is schematically shown. Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0181] like Figure 4 As shown, the electronic device 400 according to the embodiment of the present disclosure includes a processor 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403. The processor 401 may include, for example, a general-purpose microprocessor (such as a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (for example, an application-specific integrated circuit (ASIC)), etc. The processor 401 may also include an onboard memory for caching purposes. The processor 401 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiment of the present disclosure.

[0182] In RAM 403, various programs and data required for the operation of electronic device 400 are stored. Processor 401, ROM 402 and RAM 403 are connected to each other via bus 404. Processor 401 performs various operations of the method flow according to the embodiment of the present disclosure by executing the program in ROM 402 and / or RAM 403. It should be noted that the program can also be stored in one or more memories other than ROM 402 and RAM 403. Processor 401 can also perform various operations of the method flow according to the embodiment of the present disclosure by executing the program stored in the one or more memories.

[0183] According to an embodiment of the present disclosure, the electronic device 400 may further include an input / output (I / O) interface 405, which is also connected to the bus 404. The system 400 may further include one or more of the following components connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0184] According to an embodiment of the present disclosure, the method flow according to an embodiment of the present disclosure can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains 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 409, and / or installed from the removable medium 411. When the computer program is executed by the processor 401, the above-mentioned functions defined in the system of the embodiment of the present disclosure are executed. According to an embodiment of the present disclosure, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0185] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist independently without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present disclosure is implemented.

[0186] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM (Erasable Programmable Read Only Memory, EPROM) or flash memory), a portable compact disk read-only memory (Computer Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

[0187] For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include the ROM 402 and / or the RAM 403 described above and / or one or more memories other than the ROM 402 and the RAM 403 .

[0188] The embodiments of the present disclosure also include a computer program product, which includes a computer program, and the computer program contains a program code for executing the method provided by the embodiments of the present disclosure. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the cargo access method provided by the embodiments of the present disclosure.

[0189] When the computer program is executed by the processor 401, the above functions defined in the system / device of the embodiment of the present disclosure are executed. According to the embodiment of the present disclosure, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0190] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 409, and / or installed from the removable medium 411. The program code contained in the computer program may be transmitted using any appropriate network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0191] According to an embodiment of the present disclosure, the program code for executing the computer program provided by the embodiment of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (for example, using an Internet service provider to connect through the Internet).

[0192] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that 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 flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments and / or claims of the present disclosure can be combined and / or combined in a variety of ways, even if such a combination or combination is not explicitly recorded in the present disclosure. In particular, without departing from the spirit and teaching of the present disclosure, the features described in the various embodiments and / or claims of the present disclosure may be combined and / or combined in a variety of ways. All of these combinations and / or combinations fall within the scope of the present disclosure.

[0193] The embodiments of the present disclosure are described above. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present disclosure. Although the embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the attached claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make a variety of substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A cargo access method for a mobile robot storage system, comprising: In response to the cargo access instruction, a storage parameter set of the warehouse within a first time period is obtained from a database, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, a plurality of access areas in the warehouse, the access areas include a plurality of storage shelves, and a type of cargo is stored in at least one of the access areas; Constructing a cargo storage and access model according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model; Solving the area prediction model to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the multiple access areas; For each of the initial access areas, solving the shelf prediction model to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task; A target prediction result is generated based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each of the target access area, and the target prediction result represents that when the mobile robot performs a cargo access task in a second time period, it moves at least one target storage shelf between a workbench corresponding to the cargo access task and the position of the target storage shelf.

2. The method according to claim 1, wherein: The cargo storage and retrieval task information includes a plurality of storage task information and a plurality of retrieval task information; The step of solving the shelf prediction model for each of the initial access areas to obtain a second prediction result includes: For any one of the plurality of storage task information and the plurality of pickup task information, calculating the distance information between the workbench in the task information and any one of the initial access areas; Based on the first access constraint condition, according to the distance information corresponding to each of the task information, a driving cost of a first sub-prediction result is generated, wherein the first sub-prediction result corresponds to a target storage shelf of each task information, and the driving cost represents a total distance moved by the plurality of mobile robots to execute the cargo access task information; A first sub-prediction result corresponding to a minimum value among the plurality of driving costs is determined as the second prediction result.

3. The method according to claim 2, wherein: For each of the initial access areas, the minimum value of the driving cost is as shown in formula (1): Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; 0-1 decision variables Indicates that in the tth period, to complete the inventory task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise 0-1 decision variable Indicates that in the tth period, to complete the picking task, the initial storage shelf j is moved from the access area m to the workbench i, or returned from the workbench i to the access area m, otherwise r im Indicates the distance information from workbench i to access area m.

4. The method according to claim 3, wherein: The first access constraint condition is shown in formula (2) to formula (14): Where n is the index of the goods, For the collection of goods; and is the first predicted result, a continuous variable represents the number of goods n stored on the initial storage shelf j through the workbench i in the tth period; Formula (2) indicates that in the area, all arriving goods must be stored on the initial storage shelf; continuous variable represents the number of goods n taken out from the initial storage shelf j by workbench i in period t; Formula (3) indicates that the products taken out from the initial access area m are the sum of the products taken out from all shelves in the area; continuous variable represents the quantity of goods n on the initial storage shelf j in period t; Formula (4) indicates that at the beginning of period t+1, the inventory quantity of initial storage shelf j is the inventory quantity of goods n in period t plus the quantity of goods deposited minus the quantity of goods taken out; s n Indicates the unit specification of the goods n, S j represents the unit capacity of the initial storage shelf j; formula (5) indicates that the inventory on the initial storage shelf j does not exceed the capacity of the initial storage shelf j; formula (6) and formula (7) indicate that in any period t, if there is a need to store goods on the initial storage shelf j, the initial storage shelf j must be moved, where is a preset value; formula (8) indicates that in any period, an initial storage shelf can only be moved between the initial area and a workbench; formulas (9) and (10) indicate that in period t, if there is a need to take goods out from initial storage shelf j, initial shelf j must be moved; formula (11) indicates that in period t, initial storage shelf j can only be moved between the initial area and a workbench.

5. The method according to claim 1, wherein: The cargo storage and retrieval task information includes a plurality of storage task information and a plurality of retrieval task information; Wherein, solving the regional prediction model to obtain a first prediction result includes: For any one of the plurality of storage task information and the plurality of pickup task information, calculating the cost information between the workbench in the task information and any one of the initial access areas; Based on the second access constraint condition, according to the cost information corresponding to each of the task information, a total cost of a second sub-prediction result is generated, wherein the second sub-prediction result includes an initial access area corresponding to each of the task information, and the total cost represents the cost consumed by the plurality of mobile robots to execute the cargo access task information; A second sub-prediction result corresponding to a minimum value among the plurality of the total costs is determined as the first prediction result.

6. The method according to claim 5, wherein: The total cost is shown in formula (15): Among them, t is the index of each period in the first time period, represents the set of all periods, i.e. the first period; i is the index of the workbench, is the set of all workbenches; j is the index of the initial storage shelf, is the set of initial storage shelves; m is the index of the access area, is the set of access areas; r im Represents the distance information from workbench i to access area m; continuous variable represents the number of goods n stored in access area m via workbench i in period t, It represents the number of goods n taken out from the access area m through the workbench i in the tth period.

7. The method according to claim 6, wherein: The second access constraint condition is shown in formula (16) to formula (20): in, represents the quantity demanded for good n at workstation i in period t, represents the inventory quantity of good n in access area m in period t; S represents the capacity of the initial storage shelf; formula (16) indicates that all arriving goods must be stored in the access area; formula (17) indicates that the goods required for all orders must be taken out of the access area; formula (18) indicates that at the beginning of period t+1, the inventory quantity of goods in access area m is the inventory quantity of good n in period t plus the quantity of goods stored minus the quantity of goods taken out; formula (19) indicates that the stored products cannot exceed the capacity of access area m.

8. The method according to claim 1, further comprising: The target prediction result is adjusted based on the actual cargo storage and retrieval tasks in the second time period to obtain an adjusted target prediction result.

9. The method according to claim 8, wherein: The step of adjusting the target prediction result based on the actual cargo storage and retrieval task in the second time period to obtain an adjusted target prediction result includes: In the case where the quantity of goods in at least one target storage shelf in the target prediction result does not meet the goods demand of the actual goods access task, adding an associated storage shelf to the target prediction result to obtain the adjusted target prediction result, wherein the associated storage shelf and the target storage shelf are located in the same target access area; In the case that the quantity of goods in all target storage shelves in the access area in the target prediction result does not meet the goods demand of the actual goods access task, the associated access area is added to the target prediction result to obtain the adjusted target prediction result, wherein the goods stored in the associated storage shelves are the same as those stored in the target storage shelves.

10. A cargo storage and retrieval device for a mobile robot storage system, comprising: an acquisition module, configured to acquire, in response to a cargo access instruction, a storage parameter set of a warehouse within a first time period from a database, wherein the storage parameter set includes a plurality of workbenches, cargo access task information, a plurality of access areas within the warehouse, the access areas including a plurality of storage shelves, and a type of cargo stored in at least one of the access areas; A construction module, used to construct a cargo storage and access model according to the storage parameter set, wherein the cargo storage and access model includes a regional prediction model and a shelf prediction model; A first prediction module is used to solve the regional prediction model to obtain a first prediction result, wherein the first prediction result represents that the mobile robot accesses goods from an initial access area when performing a goods access task, wherein the initial access area is at least one of the multiple access areas; A second prediction module is used to solve the shelf prediction model for each of the initial access areas to obtain a second prediction result, wherein the second prediction result represents that the mobile robot accesses goods from the initial storage shelf in the initial access area when performing the goods access task; A generation module is used to generate a target prediction result based on the first prediction result and the second prediction result, wherein the target prediction result includes at least one target access area and at least one target storage shelf corresponding to each of the target access areas, and the target prediction result represents that when the mobile robot performs a cargo access task in a second time period, it moves at least one target storage shelf between a workbench corresponding to the cargo access task and the position of the target storage shelf.

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