Article picking task construction method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses a goods picking task construction method and device, equipment and a storage medium, and relates to the technical field of logistics, and the method comprises the steps: determining a target task type of a target goods picking task according to the difficulty ratio of a to-be-allocated goods picking task and a preset difficulty ratio; the article obtaining tasks are combined to obtain a first order picking task and a second order picking task, the first order picking task is composed of N article obtaining tasks, the second order picking task is composed of N + 1 articles to be distributed, and N is a positive integer; and when it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, determining the first picking task or the second picking task as a target picking task according to the target task type. According to the technical scheme, the goods picking task is obtained by automatically and efficiently combining the goods obtaining task without manual participation, and automatic construction of the goods picking task is achieved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of logistics technology, and in particular to a method, device, equipment, and storage medium for constructing a picking task. Background Art

[0002] The construction of picking tasks is an important module of warehouse management. After receiving multiple item acquisition tasks, it is necessary to combine the multiple item acquisition tasks to obtain a picking task, so that the picking personnel can pick the items based on the picking tasks.

[0003] In the existing technology, each warehouse is equipped with a combination of personnel to manually combine item acquisition tasks. Since the construction of picking tasks is the command center that controls the warehouse's outbound delivery rhythm, the quality of the item acquisition task combination will affect the outbound delivery efficiency. Therefore, combination personnel with rich production experience and a global perspective are required to operate.

[0004] In the process of realizing the present invention, the inventors discovered that the prior art has at least the following technical problems:

[0005] Over-reliance on the experience of assemblers leads to high labor costs and low efficiency in constructing picking tasks. Summary of the Invention

[0006] The present invention provides a method, device, equipment and storage medium for constructing a picking task, so as to realize automatic construction of the picking task.

[0007] In a first aspect, an embodiment of the present invention provides a method for constructing a picking task, comprising:

[0008] Determine the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio;

[0009] The item acquisition tasks are combined to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer;

[0010] When it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type.

[0011] In a second aspect, an embodiment of the present invention further provides a device for constructing a picking task, comprising:

[0012] A first determining module is used to determine a target task type of a target picking task according to the difficulty ratio of the picking task to be assigned and a preset difficulty ratio;

[0013] A second determination module is used to determine a target complexity threshold according to the target task type;

[0014] The grouping module is used to combine the item acquisition tasks. When it is determined that the first complexity of the first picking task composed of N item acquisition tasks is not greater than the target complexity threshold, and the second complexity of the second picking task composed of N+1 item acquisition tasks is greater than the target complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type, where N is a positive integer.

[0015] In a third aspect, an embodiment of the present invention further provides a computer device, comprising:

[0016] one or more processors;

[0017] a storage device for storing one or more programs,

[0018] When the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a picking task as described in any one of the first aspects.

[0019] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer-executable instructions, which, when executed by a computer processor, are used to execute the method for constructing a picking task as described in any one of the first aspects.

[0020] The embodiments of the above invention have the following advantages or beneficial effects:

[0021] An embodiment of the present invention provides a method for constructing a picking task, comprising: determining a target task type of a target picking task based on a difficulty ratio of a picking task to be assigned and a preset difficulty ratio; combining item acquisition tasks to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be assigned, where N is a positive integer; upon determining that a first complexity of the first picking task is not greater than a complexity threshold, and that a second complexity of the second picking task is greater than the complexity threshold, determining the first picking task or the second picking task as the target picking task according to the target task type. The above technical solution can first determine the difficulty ratio of the picking tasks to be assigned in the task pool, compare the difficulty ratio of the picking tasks to be assigned with the preset difficulty ratio, and determine whether the target task type of the target picking task to be generated is a simple task or a complex task based on the comparison result. Secondly, the item acquisition tasks can be combined to obtain the first picking task and the second picking task, thereby realizing the combination of the item acquisition tasks. Then, the first complexity of the first picking task and the second complexity of the second picking task can be determined, and the first complexity, the second complexity and the complexity threshold can be compared. When it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type, and the picking task is obtained by combining the item acquisition tasks in an automated and efficient manner without human participation, thereby realizing the automatic construction of the picking task, reducing the labor cost of the picking task construction while improving the efficiency of the picking task construction. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of a method for constructing a picking task provided by an embodiment of the present invention;

[0023] Figure 2 A flowchart of another method for constructing a picking task provided by an embodiment of the present invention;

[0024] Figure 3 A schematic structural diagram of a device for constructing a picking task provided by an embodiment of the present invention;

[0025] Figure 4 A schematic structural diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and examples. It will be understood that the specific embodiments described herein are intended only to illustrate the present invention and are not intended to limit the present invention. It should also be noted that, for ease of description, the accompanying drawings only illustrate portions relevant to the present invention, not all structures.

[0027] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. When its operation is completed, the process can be terminated, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the features in the embodiments of the present invention and the embodiments can be combined with each other without conflict.

[0028] After the warehouse management system receives the item acquisition task sent by the upstream system, it can initialize the item acquisition task and put it into the task pool. The combination personnel combine the item acquisition tasks in the task pool based on the combination experience to obtain the picking task.

[0029] Existing technologies cannot reuse task combination experience. The task combination logic of each warehouse is scattered, making it difficult to manage and quantitatively analyze. In addition, it relies too much on the combination experience of the assemblers, resulting in high labor costs.

[0030] Therefore, this application proposes a method for constructing picking tasks to achieve automatic combination of picking tasks.

[0031] The following is a detailed description of the method for constructing the picking task proposed in this application with reference to diagrams and embodiments.

[0032] Figure 1 This is a flowchart of a method for constructing a picking task provided by an embodiment of the present invention. The embodiment of the present invention is applicable to situations where a picking task needs to be automatically constructed. The method can be executed by a picking task construction device, which can be implemented by software and / or hardware. Figure 1 Said method specifically comprises the following steps:

[0033] Step 110: Determine the target task type of the target picking task based on the difficulty ratio of the to-be-assigned picking task and the preset difficulty ratio.

[0034] Among them, the picking tasks to be assigned can be understood as picking tasks that have been combined but not assigned to pickers. Picking tasks can be simple tasks or complex tasks. The difficulty ratio of the picking tasks to be assigned can be understood as the ratio of complex tasks to simple tasks in the picking tasks that have been combined but not assigned to pickers.

[0035] Specifically, the difficulty ratio of the picking task to be assigned can be determined based on the ratio of the number of complex tasks to the number of simple tasks in the tasks to be assigned, and the difficulty ratio of the picking task to be assigned can be compared with the preset difficulty ratio. When it is determined that the difficulty ratio of the picking task to be assigned is greater than the preset difficulty ratio, the target task type of the target picking task is determined to be a simple task. When it is determined that the difficulty ratio of the picking task to be assigned is not greater than the preset difficulty ratio, the target task type of the target picking task is determined to be a complex task.

[0036] In an embodiment of the present invention, after the difficulty ratio of the picking task to be assigned is determined, the difficulty ratio of the picking task to be assigned can be compared with a preset difficulty ratio, and the target task type of the target picking task to be generated is determined to be a simple task or a complex task based on the comparison result.

[0037] Step 120: Combine the item acquisition tasks to obtain a first picking task and a second picking task.

[0038] The first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer.

[0039] Specifically, when combining the tasks to be assigned, N item acquisition tasks can be randomly selected to form the first picking task, and then one item acquisition task can be randomly selected to form the second picking task together with the N tasks to be assigned that constitute the first picking task; when combining the tasks to be assigned, if there is a priority allocation principle, the tasks to be assigned can be sorted according to the priority allocation principle, and then N item acquisition tasks can be selected from the sorting results to combine to obtain the first picking task, and N+1 item acquisition tasks can be selected from the sorting results to combine to obtain the second picking task.

[0040] In practical applications, the priority allocation principle can be time priority or congestion priority. When the priority allocation principle is time priority, it is necessary to first construct picking tasks based on the item acquisition tasks with the shortest remaining time. Therefore, it is possible to determine to sort the item acquisition tasks according to the remaining time, and combine the N item acquisition tasks with the shortest remaining time into the first picking task, and combine the N+1 item acquisition tasks with the shortest remaining time into the second picking task. When the priority allocation principle is congestion priority, it is necessary to first construct picking tasks based on the item acquisition tasks with the shortest congestion. It is possible to sort the item acquisition tasks according to the congestion, and combine the N item acquisition tasks with the shortest congestion into the first picking task, and combine the N+1 item acquisition tasks with the shortest congestion into the second picking task.

[0041] It should be noted that the congestion of the item acquisition task is determined by the similarity between the item acquisition task and the picking task to be assigned.

[0042] In the embodiment of the present invention, the first picking task and the second picking task are obtained by combining the item acquisition tasks, thereby realizing the combination of the item acquisition tasks.

[0043] Step 130: When it is determined that the first complexity of the first picking task is not greater than a complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, determine the first picking task or the second picking task as the target picking task according to the target task type.

[0044] The complexity of the picking task is determined by the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the picking task. The item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the picking task are determined by the item type, item quantity, shelf quantity, congestion, and remaining time of the picking task, as well as the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks within the historical time period.

[0045] Specifically, first, the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the first picking task can be determined based on the item type, item quantity, shelf number, congestion, and remaining time of the first picking task, as well as the item type distribution, item quantity distribution, shelf number distribution, congestion distribution, and remaining time distribution of historical picking tasks within the historical time period; and the item type factor, item quantity factor, shelf number factor, congestion factor, and remaining time factor of the second picking task can be determined based on the item type, item quantity, shelf number, congestion, and remaining time of the second picking task, as well as the item type distribution, item quantity distribution, shelf number distribution, congestion distribution, and remaining time distribution of historical picking tasks within the historical time period; and then, the first complexity of the first picking task can be determined based on the item type factor, item quantity factor, shelf number factor, congestion factor, and remaining time factor of the first picking task; and the second complexity of the second picking task can be determined based on the item type factor, item quantity factor, shelf number factor, congestion factor, and remaining time factor of the second picking task. Comparing the first complexity of the first picking task with the complexity threshold, and the second complexity of the second picking task with the complexity threshold, if it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task can be determined as the target picking task based on the target task type. If the target task type is a simple task, the first picking task can be determined as the target picking task; if the target task type is a complex task, the second picking task can be determined as the target picking task.

[0046] In an embodiment of the present invention, after determining the first complexity of the first picking task and the second complexity of the second picking task, by comparing the first complexity, the second complexity and the complexity threshold, when it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type, thereby realizing the construction of the target picking task according to the item acquisition task.

[0047] The method for constructing a picking task provided by an embodiment of the present invention includes: determining a target task type of a target picking task based on a difficulty ratio of a picking task to be assigned and a preset difficulty ratio; combining item acquisition tasks to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be assigned, where N is a positive integer; upon determining that a first complexity of the first picking task is not greater than a complexity threshold, and that a second complexity of the second picking task is greater than the complexity threshold, determining the first picking task or the second picking task as the target picking task according to the target task type. The above technical solution can first determine the difficulty ratio of the picking tasks to be assigned in the task pool, compare the difficulty ratio of the picking tasks to be assigned with the preset difficulty ratio, and determine whether the target task type of the target picking task to be generated is a simple task or a complex task based on the comparison result. Secondly, the item acquisition tasks can be combined to obtain the first picking task and the second picking task, thereby realizing the combination of the item acquisition tasks. Then, the first complexity of the first picking task and the second complexity of the second picking task can be determined, and the first complexity, the second complexity and the complexity threshold can be compared. When it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type. The picking task is obtained by automatically and efficiently combining the item acquisition tasks without human participation, thereby realizing the automatic construction of the picking task, reducing the labor cost of the picking task construction while improving the efficiency of the picking task construction.

[0048] Figure 2 This is a flowchart of another method for constructing a picking task provided by an embodiment of the present invention. The embodiment of the present invention is applicable to situations where it is necessary to automatically construct a picking task. Based on the above embodiment, the present embodiment of the present invention adds "determining the difficulty ratio of the picking task to be assigned based on the task type of each picking task to be assigned in the task pool." before determining the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2The method for constructing a picking task provided by an embodiment of the present invention includes:

[0049] Step 210: Determine the difficulty ratio of each picking task to be assigned based on the task type of each picking task to be assigned in the task pool.

[0050] Specifically, first determine whether the task type of each picking task to be assigned in the task pool is a simple task or a complex task, and then determine the difficulty ratio of the picking task to be assigned based on the ratio of the number of complex tasks to the number of simple tasks in the tasks to be assigned.

[0051] In the embodiment of the present invention, the difficulty ratio of the to-be-assigned picking task is determined according to the task type of each to-be-assigned task in the task pool.

[0052] Step 220: Determine the target task type of the target picking task based on the difficulty ratio of the to-be-assigned picking task and the preset difficulty ratio.

[0053] The preset difficulty ratio is a positive integer, which is used to configure the ratio of complex tasks to simple tasks. This allows complex tasks to be interspersed with simple tasks, making it easier to control the rhythm of the picking site and maintain a proper balance between tension and relaxation. In actual applications, after constructing a complex task, multiple simple tasks can be constructed.

[0054] In one implementation, step 220 may specifically include:

[0055] Compare the difficulty ratio of the to-be-assigned picking task with the preset difficulty ratio; if it is determined that the difficulty ratio of the to-be-assigned picking task is greater than the preset difficulty ratio, determine that the target task type of the target picking task is a simple task; otherwise, determine that the target task type of the target picking task is a complex task.

[0056] Specifically, the difficulty ratio of the picking task to be assigned is compared with the preset difficulty ratio. When it is determined that the difficulty ratio of the picking task to be assigned is greater than the preset difficulty ratio, the target task type of the target picking task is determined to be a simple task. When it is determined that the difficulty ratio of the picking task to be assigned is not greater than the preset difficulty ratio, the target task type of the target picking task is determined to be a complex task.

[0057] It should be noted that the preset difficulty ratio can be pre-set or determined based on experiments. When the preset difficulty ratio is determined based on experiments, the values ​​of other parameters can be fixed, and the preset difficulty ratio can be subjected to experimental diversion and data effect comparison to determine the final value of the preset difficulty ratio.

[0058] In addition, if the task type of a picking task is simple, the complexity of the picking task is no greater than the complexity threshold. If the task type of a picking task is complex, the complexity of the picking task is greater than the complexity threshold. The complexity of the picking task is determined by the type of items in the picking task, the number of items, the number of shelves, the congestion of the picking aisle, and the remaining time.

[0059] Of course, the complexity threshold can be pre-set or determined based on experiments. When determining the complexity threshold based on experiments, the values ​​of other parameters can be fixed, and the complexity threshold can be independently tested for splitting experiments and comparing data effects to determine the final value of the complexity threshold.

[0060] In an embodiment of the present invention, after determining the difficulty ratio of the picking tasks to be assigned in the task pool, the difficulty ratio of the picking tasks to be assigned can be compared with the preset difficulty ratio, and the target task type of the target picking task to be generated can be determined as a simple task or a complex task based on the comparison result.

[0061] Step 230: Combine the item acquisition tasks to obtain a first picking task and a second picking task.

[0062] The first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer.

[0063] In one implementation, step 230 may specifically include:

[0064] N item acquisition tasks are randomly selected from the item acquisition task pool and combined to obtain the first picking task; the N item acquisition tasks constituting the first picking task and one item acquisition task randomly selected from the item acquisition task pool are combined to obtain the second picking task.

[0065] Specifically, when combining the tasks to be assigned, if there is no priority assignment principle, N item acquisition tasks can be randomly selected from the item acquisition task pool for combination to obtain the first picking task, and then one item acquisition task can be randomly selected from the item acquisition task pool and combined with the N item acquisition tasks that constitute the first picking task to obtain the second picking task.

[0066] In another embodiment, step 230 may specifically include:

[0067] After determining a task sorting method based on a priority allocation principle, sort the item acquisition tasks in the item acquisition task pool based on the task sorting method; select N item acquisition tasks according to the sorting result and combine them to obtain the first picking task; select N+1 item acquisition tasks according to the sorting result and combine them to obtain the first picking task.

[0068] Optionally, the priority allocation principle is time priority. Accordingly, determining the task sorting method based on the priority allocation principle includes:

[0069] The task sorting method is determined based on the time priority to sort the item acquisition tasks according to the remaining time.

[0070] Optionally, the priority allocation principle is time priority. Accordingly, determining the task sorting method based on the priority allocation principle includes:

[0071] The task sorting method is determined based on the time priority to sort the item acquisition tasks according to the remaining time.

[0072] Optionally, the priority allocation principle is congestion priority. Accordingly, determining the task sorting method based on the priority allocation principle includes:

[0073] Specifically, when combining the tasks to be assigned, if there is a priority assignment principle, the task sorting method can be determined according to the priority assignment principle, and the tasks to be assigned can be sorted according to the task sorting method. Then, N item acquisition tasks are selected from the sorting results for combination to obtain the first picking task, and N+1 item acquisition tasks are selected from the sorting results for combination to obtain the second picking task.

[0074] When the priority allocation principle is time priority, the task sorting method can be determined to sort the item acquisition tasks according to the remaining time. At this time, the item acquisition tasks can be sorted from small to large or from large to small based on the remaining time, and the N item acquisition tasks with the smallest remaining time are combined into the first picking task, and the N+1 item acquisition tasks with the smallest remaining time are combined into the second picking task.

[0075] When the priority allocation principle is congestion priority, the task sorting method can be determined to sort the item acquisition tasks according to congestion. At this time, the item acquisition tasks can be sorted from small to large or from large to small based on congestion, and the N item acquisition tasks with the smallest congestion are combined into the first picking task, and the N+1 item acquisition tasks with the smallest congestion are combined into the second picking task.

[0076] It should be noted that the congestion of the item acquisition task is determined by the similarity between the lane vector s1 corresponding to the item acquisition task and the lane vector s2 corresponding to the to-be-assigned picking task. Specifically, it can be determined by the cosine similarity between the lane vector s1 corresponding to the item acquisition task and the lane vector s2 corresponding to the to-be-assigned picking task, that is, the congestion of the item acquisition task can be determined. The lane vector s1 corresponding to the item acquisition task is determined by the lane corresponding to the shelf to which the item belongs in the item acquisition task, and the lane vector s2 corresponding to the to-be-assigned picking task is determined by the lane corresponding to the shelf to which the item belongs in the to-be-assigned picking task. For example, when there are lanes 1, 2, 3, 4, and 5 in the warehouse, the lanes are marked as 0 (no need to pick up) or 1 (need to pick up) according to whether the item acquisition task needs to pick up in the lane. When the item acquisition task needs to pick up at 2 or 5, the lane vector s1 corresponding to the item acquisition task can be determined to be {0, 1, 0, 1, 0}. The lane vector corresponding to the to-be-assigned picking task is determined based on the number of to-be-assigned picking tasks that need to be picked in each lane. When there is one to-be-assigned picking task that needs to be picked in lane 1 and five to-be-assigned picking tasks that need to be picked in lane 2, the lane vector s2 corresponding to the to-be-assigned picking task can be determined to be {1, 5, 0, 0, 0}. Furthermore, according to Determines the congestion level of item acquisition tasks.

[0077] In the embodiment of the present invention, the first picking task and the second picking task are obtained by combining the item acquisition tasks, thereby realizing the combination of the item acquisition tasks.

[0078] Step 240: Determine the first complexity of the first picking task and the second complexity of the second picking task.

[0079] In one implementation, step 240 may specifically include:

[0080] Determine the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the first picking task based on the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks within a historical time period and the item type, item quantity, shelf quantity, congestion, and remaining time of the first picking task; determine the first complexity of the first picking task based on the item type factor, item quantity factor, aisle number factor, congestion factor, and remaining time factor and the weights of each factor of the first picking task; determine the second complexity of the second picking task based on the item type factor, item quantity factor, aisle number factor, congestion factor, and remaining time factor and the weights of each factor of the second picking task.

[0081] When handling a large number of different items in a picking task, more time and effort are required to accurately select the items. Therefore, the greater the number and variety of items involved in the picking task, the higher the complexity. Items are placed on a large number of different shelves in a picking task, requiring more time and effort to find and retrieve them. Therefore, the more shelves the items belong to, the higher the complexity. The picking environment also affects complexity. Picking tasks performed in a confined space increase the difficulty of operation. Therefore, the greater the congestion, the higher the complexity. Picking tasks with strict time constraints, such as those requiring the acquisition of a large number of items within a specified time, are more complex. Therefore, the smaller the remaining time, determined by the difference between the picking task's cut-off time and the current time, the higher the complexity.

[0082] Therefore, it can be determined that the complexity of the current picking task is determined by the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the current picking task, and the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the current picking task are determined by the item type, item quantity, shelf quantity, congestion, and remaining time of the current picking task, as well as the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks in the historical time period.

[0083] First, the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks within the historical time period can be determined. Specifically, the historical picking tasks within the historical time period can be obtained, and the item type, item quantity, shelf quantity, congestion, and remaining time of each historical picking task within the historical time period can be determined. Then, based on the item type, item quantity, shelf quantity, congestion, and remaining time of each historical picking task within the historical time period, the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of the historical picking tasks within the historical time period can be determined.

[0084] Furthermore, the item type factor of the current picking task can be determined according to the percentage of the position to which the item type of the current picking task belongs in the item type distribution. The percentage of the position to which the item type of the current picking task belongs in the item type distribution can be understood as the percentage of the sorting position of the item type of the current picking task in the sorting result after sorting the item types of historical picking tasks in the historical time period; the item quantity factor of the current picking task can be determined according to the percentage of the position to which the item quantity factor of the current picking task belongs in the item quantity distribution. The percentage of the position to which the item quantity of the current picking task belongs in the item quantity distribution can be understood as the percentage of the sorting position of the item quantity of the current picking task in the sorting result after sorting the item quantity of historical picking tasks in the historical time period; the shelf quantity factor of the current picking task can be determined according to the percentage of the position to which the shelf quantity factor of the current picking task belongs in the shelf quantity distribution. The percentage of the site in the rack quantity distribution can be understood as the percentage of the ranking position of the rack quantity of the current picking task in the sorting result after sorting the rack quantity of the historical picking tasks in the historical time period; the congestion factor of the current picking task can be determined according to the percentage of the site of the congestion factor of the current picking task in the congestion distribution, and the percentage of the site of the congestion of the current picking task in the congestion distribution can be understood as the percentage of the ranking position of the congestion of the current picking task in the sorting result after sorting the congestion of the historical picking tasks in the historical time period; the remaining time factor of the current picking task can be determined according to the percentage of the site of the remaining time factor of the current picking task in the remaining time distribution, and the percentage of the site of the remaining time of the current picking task in the remaining time distribution can be understood as the percentage of the ranking position of the remaining time of the current picking task in the sorting result after sorting the remaining time of the historical picking tasks in the historical time period.

[0085] After determining the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the current picking task, the complexity of the current picking task can be determined as follows: (W1 / 2*(item type factor+item quantity factor))+(W2*shelf quantity factor)+(W3*congestion factor)+(W4*remaining time factor)) / 4, where W1, W2, W3, and W4 are complexity factor weights, W1 is the item weight, W2, W3, and W4 are the shelf weight, congestion weight, and remaining time weight, respectively, and W1+W2+W3+W4=1. W1, W2, W3, and W4 can be pre-set or pre-determined based on experiments. When determining the preset difficulty ratio based on experiments, the values ​​of other parameters can be fixed, and experimental diversion and data effect comparison can be performed on the item weight, shelf weight, congestion weight, and remaining time weight separately to determine the final values ​​of the item weight, shelf weight, congestion weight, and remaining time weight.

[0086] In practical applications, W1, W2, W3, and W4 can be 0.1, 0.3, 0.7, and 0.9, resulting in 256 possible weight combinations. After determining the complexity based on various weight combinations, a picking task is constructed. Data is collected over an experimental period (e.g., one month) to calculate the number of completed picking tasks corresponding to each weight combination. Weight combinations with higher completion numbers have better automated picking task construction results, and these can be determined as the final parameter values ​​for W1, W2, W3, and W4.

[0087] Specifically, we can first determine the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the first picking task and the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the second picking task. Then, we can determine the first complexity of the first picking task based on the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the first picking task, and determine the second complexity of the second picking task based on the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the second picking task.

[0088] In the embodiment of the present invention, a first complexity of a first picking task and a second complexity of a second picking task are determined.

[0089] Step 250: When it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, determine the first picking task or the second picking task as the target picking task according to the target task type.

[0090] In one embodiment, determining the first picking task or the second picking task as the target picking task according to the target task type includes:

[0091] When the target task type is a simple task, the first picking task is determined as the target picking task; when the target task type is a complex task, the second picking task is determined as the target picking task.

[0092] Specifically, after determining the first complexity of the first picking task and the second complexity of the second picking task, the first complexity of the first picking task is compared with the complexity threshold, and the second complexity of the second picking task is compared with the complexity threshold. If it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task can be determined as the target picking task based on the target task type. If the target task type is a simple task, the first picking task can be determined as the target picking task. If the target task type is a complex task, the second picking task can be determined as the target picking task.

[0093] It should be noted that in the process of combining item acquisition tasks, if the target task type is a simple task and the complexity of the picking task composed of the currently remaining item acquisition tasks is less than the complexity threshold, then the target picking task with the target task type of a simple task is composed based on the currently remaining item acquisition tasks. If the target task type is a complex task and the complexity of the picking task composed of the currently remaining item cargo tasks is less than the complexity threshold, then the target picking task with the target task type of a simple task is also composed based on the currently remaining item acquisition tasks.

[0094] In an embodiment of the present invention, after determining the first complexity of the first picking task and the second complexity of the second picking task, by comparing the first complexity, the second complexity and the complexity threshold, when it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type, thereby realizing the construction of the target picking task according to the item acquisition task.

[0095] The method for constructing a picking task provided by an embodiment of the present invention includes: determining the difficulty ratio of the picking tasks to be assigned based on the task type of each picking task to be assigned in a task pool; determining the target task type of the target picking task based on the difficulty ratio of the picking tasks to be assigned and a preset difficulty ratio; combining the item acquisition tasks to obtain a first picking task and a second picking task; determining the first complexity of the first picking task and the second complexity of the second picking task; when it is determined that the first complexity of the first picking task is not greater than a complexity threshold, and that the second complexity of the second picking task is greater than the complexity threshold, determining the first picking task or the second picking task as the target picking task according to the target task type. The above technical solution can first determine the difficulty ratio of the picking tasks to be assigned in the task pool, compare the difficulty ratio of the picking tasks to be assigned with the preset difficulty ratio, and determine whether the target task type of the target picking task to be generated is a simple task or a complex task based on the comparison result. Secondly, the item acquisition tasks can be combined to obtain the first picking task and the second picking task, thereby realizing the combination of the item acquisition tasks. Then, the first complexity of the first picking task and the second complexity of the second picking task can be determined, and the first complexity, the second complexity and the complexity threshold can be compared. When it is determined that the first complexity of the first picking task is not greater than the complexity threshold, and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type. The picking task is obtained by automatically and efficiently combining the item acquisition tasks without human participation, thereby realizing the automatic construction of the picking task, reducing the labor cost of the picking task construction while improving the efficiency of the picking task construction.

[0096] In addition, the parameter configuration of automatic construction of picking tasks is realized based on the configured preset difficulty ratio, complexity threshold, complexity factor weight, and priority allocation principle, thereby realizing the customization of picking tasks by warehouse division. The customization of picking tasks by warehouse division realized by parameter configuration can realize the staggered construction of complex tasks and simple tasks, and also realize the priority construction of tasks based on items with less remaining time or less congestion, thereby improving the practicality and applicability of picking task construction.

[0097] Figure 3 This is a schematic diagram of the structure of a picking task construction device provided in an embodiment of the present invention. This device and the picking task construction methods described in the aforementioned embodiments are based on the same inventive concept. For details not fully described in the embodiments of the picking task construction device, reference can be made to the embodiments of the aforementioned picking task construction methods.

[0098] The specific structure of the construction device of the picking task is as follows Figure 3 Shown, including:

[0099] A determination module 310 is configured to determine a target task type of a target picking task based on the difficulty ratio of the to-be-assigned picking task and a preset difficulty ratio;

[0100] a combining module 320 configured to combine the item acquisition tasks to obtain a first picking task and a second picking task, wherein the first picking task is composed of N item acquisition tasks, and the second picking task is composed of N+1 items to be distributed, where N is a positive integer;

[0101] The execution module 330 is used to determine the first picking task or the second picking task as the target picking task according to the target task type when it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold.

[0102] Based on the above embodiment, the device further includes:

[0103] The difficulty ratio determination module is used to determine the difficulty ratio of the picking tasks to be assigned according to the task type of each picking task to be assigned in the task pool.

[0104] Based on the above embodiment, the determination module 310 is specifically configured to:

[0105] Compare the difficulty ratio of the to-be-assigned picking task with the preset difficulty ratio; if it is determined that the difficulty ratio of the to-be-assigned picking task is greater than the preset difficulty ratio, determine that the target task type of the target picking task is a simple task; otherwise, determine that the target task type of the target picking task is a complex task.

[0106] Based on the above embodiment, the combination module 320 is specifically configured to:

[0107] N item acquisition tasks are randomly selected from the item acquisition task pool and combined to obtain the first picking task; the N item acquisition tasks constituting the first picking task and one item acquisition task randomly selected from the item acquisition task pool are combined to obtain the second picking task.

[0108] Based on the above embodiment, the combination module 320 is specifically configured to:

[0109] After determining a task sorting method based on a priority allocation principle, sort the item acquisition tasks in the item acquisition task pool based on the task sorting method; select N item acquisition tasks according to the sorting result and combine them to obtain the first picking task; select N+1 item acquisition tasks according to the sorting result and combine them to obtain the first picking task.

[0110] In one embodiment, the priority allocation principle is time priority. Accordingly, determining the task sorting method based on the priority allocation principle includes:

[0111] The task sorting method is determined based on the time priority to sort the item acquisition tasks according to the remaining time.

[0112] In one embodiment, the priority allocation principle is congestion priority. Accordingly, determining the task sorting method based on the priority allocation principle includes:

[0113] The task sorting method is determined based on the priority of the congestion level, and the item acquisition tasks are sorted according to the congestion level.

[0114] Based on the above embodiment, the device further includes:

[0115] The complexity determination module is configured to determine the first complexity of the first picking task and the second complexity of the second picking task.

[0116] Based on the above embodiment, the complexity determination module is specifically configured to:

[0117] Determine the item type factor, item quantity factor, shelf quantity factor, congestion factor, and remaining time factor of the first picking task based on the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks within a historical time period and the item type, item quantity, shelf quantity, congestion, and remaining time of the first picking task; determine the first complexity of the first picking task based on the item type factor, item quantity factor, aisle number factor, congestion factor, and remaining time factor and the weights of each factor of the first picking task; determine the second complexity of the second picking task based on the item type factor, item quantity factor, aisle number factor, congestion factor, and remaining time factor and the weights of each factor of the second picking task.

[0118] Based on the above embodiment, the execution module 330 is further configured to:

[0119] When the target task type is a simple task, the first picking task is determined as the target picking task; when the target task type is a complex task, the second picking task is determined as the target picking task.

[0120] The device for constructing a picking task provided in an embodiment of the present invention can execute the method for constructing a picking task provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the method for constructing a picking task.

[0121] It is worth noting that in the embodiment of the above-mentioned picking task construction device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0122] Figure 4 A schematic structural diagram of a computer device provided in an embodiment of the present invention. Figure 4 A block diagram of an exemplary computer device 4 suitable for use in implementing embodiments of the present invention is shown. Figure 4 The computer device 4 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.

[0123] like Figure 4 As shown, computer device 4 is implemented as a general-purpose computing electronic device. Components of computer device 4 may include, but are not limited to, one or more processors or processing units 16, system memory 28, and bus 18 connecting various system components (including system memory 28 and processing unit 16).

[0124] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. Examples of these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0125] Computer device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 4, including volatile and non-volatile media, removable and non-removable media.

[0126] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be configured to read and write non-removable, non-volatile magnetic media ( Figure 4 Not shown, often called a "hard drive"). Although Figure 4Not shown, a magnetic disk drive for reading and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of various embodiments of the present invention.

[0127] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0128] The computer device 4 may also communicate with one or more external devices 14 (e.g., a keyboard, a pointing device, a display 24, etc.), one or more devices that enable a user to interact with the computer device 4, and / or any device that enables the computer device 4 to communicate with one or more other computing devices (e.g., a network card, a modem, etc.). Such communication may be performed through an input / output (I / O) interface 22. Furthermore, the computer device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the computer device 4 via the bus 18. Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with computer device 4, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0129] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, for example, implementing the method for constructing a picking task provided in an embodiment of the present invention, which includes:

[0130] Determine the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio;

[0131] The item acquisition tasks are combined to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer;

[0132] When it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type.

[0133] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the method for constructing a picking task provided in any embodiment of the present invention.

[0134] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for constructing a picking task provided in an embodiment of the present invention is implemented, for example. The method includes:

[0135] Determine the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio;

[0136] The item acquisition tasks are combined to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer;

[0137] When it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type.

[0138] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: 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 this document, a computer-readable storage medium can 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.

[0139] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0140] Program code embodied on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0141] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0142] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computing device. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computer device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

[0143] Note that the above are only preferred embodiments of the present invention and the technical principles employed. Those skilled in the art will appreciate that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments, and substitutions are possible for those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments and may include many other equivalent embodiments without departing from the scope of the present invention. The scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for constructing a picking task, characterized in that: include: Determine the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio; The item acquisition tasks are combined to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer; When it is determined that the first complexity of the first picking task is not greater than a complexity threshold and the second complexity of the second picking task is greater than the complexity threshold, the first picking task or the second picking task is determined as the target picking task according to the target task type.

2. The method for constructing a picking task according to claim 1, characterized in that: Before determining the target task type of the target picking task based on the difficulty ratio of the to-be-assigned picking task and the preset difficulty ratio, the following steps are also included: The difficulty ratio of each picking task to be assigned is determined according to the task type of each picking task to be assigned in the task pool.

3. The method for constructing a picking task according to claim 1, characterized in that: Determine the target task type of the target picking task based on the difficulty ratio of the picking task to be assigned and the preset difficulty ratio, including: comparing the difficulty ratio of the to-be-assigned picking task with the preset difficulty ratio; If it is determined that the difficulty ratio of the to-be-assigned picking task is greater than the preset difficulty ratio, the target task type of the target picking task is determined to be a simple task; otherwise, the target task type of the target picking task is determined to be a complex task.

4. The method for constructing a picking task according to claim 1, wherein: The item acquisition tasks are combined to obtain the first picking task and the second picking task, including: Randomly selecting N item acquisition tasks from the item acquisition task pool and combining them to obtain the first picking task; The N item acquisition tasks constituting the first picking task and one item acquisition task randomly selected from the item acquisition task pool are combined to obtain the second picking task.

5. The method for constructing a picking task according to claim 1, characterized in that: The item acquisition tasks are combined to obtain the first picking task and the second picking task, including: After determining a task sorting method based on a priority allocation principle, sorting the item acquisition tasks in the item acquisition task pool based on the task sorting method; According to the sorting result, N item acquisition tasks are selected and combined to obtain the first picking task; According to the sorting result, N+1 item acquisition tasks are selected and combined to obtain the first picking task.

6. The method for constructing a picking task according to claim 5, characterized in that: The priority allocation principle is time priority. Accordingly, the task sorting method is determined based on the priority allocation principle, including: Determining the task sorting method based on the time priority is to sort the item acquisition tasks according to the remaining time; The priority allocation principle is congestion priority. Accordingly, the task sorting method is determined based on the priority allocation principle, including: The task sorting method is determined based on the priority of the congestion level, and the item acquisition tasks are sorted according to the congestion level.

7. The method for constructing a picking task according to claim 1, characterized in that: After combining the item acquisition tasks to obtain the first picking task and the second picking task, the following steps are further included: The first complexity of the first picking task and the second complexity of the second picking task are determined.

8. The method for constructing a picking task according to claim 7, characterized in that: Determining the first complexity of the first picking task and the second complexity of the second picking task includes: Determine an item type factor, an item quantity factor, a shelf quantity factor, a congestion factor, and a remaining time factor for the first picking task based on the item type distribution, item quantity distribution, shelf quantity distribution, congestion distribution, and remaining time distribution of historical picking tasks within a historical time period and the item type, item quantity, shelf quantity, congestion, and remaining time of the first picking task; determining the first complexity of the first picking task based on an item type factor, an item quantity factor, an aisle number factor, a congestion factor, a remaining time factor, and weights of each factor of the first picking task; The second complexity of the second picking task is determined according to the item type factor, item quantity factor, lane number factor, congestion factor, remaining time factor and weights of each factor of the second picking task.

9. The method for constructing a picking task according to claim 1, characterized in that: Determining the first picking task or the second picking task as the target picking task according to the target task type includes: When the target task type is a simple task, the first picking task is determined as the target picking task; When the target task type is a complex task, the second picking task is determined as the target picking task.

10. A device for constructing a picking task, characterized in that: include: a determination module, configured to determine a target task type of a target picking task based on the difficulty ratio of the picking task to be assigned and a preset difficulty ratio; a combining module, configured to combine the item acquisition tasks to obtain a first picking task and a second picking task, wherein the first picking task consists of N item acquisition tasks, and the second picking task consists of N+1 items to be distributed, where N is a positive integer; an execution module, configured to, upon determining that the first complexity of the first picking task is not greater than a complexity threshold and that the second complexity of the second picking task is greater than the complexity threshold, determine the first picking task or the second picking task as the target picking task according to the target task type.

11. A computer device, characterized in that: The computer device comprises: 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 for constructing a picking task as described in any one of claims 1 to 9.

12. A storage medium containing computer-executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to execute the method for constructing a picking task as described in any one of claims 1 to 9.

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