CN110807612A - Method and device for determining residual capacity - Google Patents
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Abstract
The invention discloses a method and a device for determining residual capacity, and relates to the technical field of computers. One embodiment of the method comprises: determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list; estimating the picking time of the target list according to the information of the man-machine cooperation bin, and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list; and if the residual time is greater than a preset threshold value, issuing the current wave-time task list to a man-machine cooperation bin for sorting. The embodiment estimates the remaining time after the current-time task order is finished by combining the information of the man-machine cooperation bin, and further can judge whether the picking can be carried out in the man-machine cooperation bin, and provides a method for calculating the remaining capacity in the goods picking mode of man-machine cooperation.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for determining residual capacity.
Background
With the large-scale application of intelligent technology, a new man-machine cooperation type picking mode is provided in the e-commerce order fulfillment warehouse process, and the specific explanation is that in a service scene (i.e., man-machine cooperation warehouse) combining Automatic Guided Vehicle (AGV) with a picking container and manual picking, the AGV with the container directly drives to a picking point, and a picking person walks within a small range to complete a picking task. The picking mode can effectively reduce the invalid walking distance of picking personnel and improve the picking operation efficiency. However, there is no effective way to determine the remaining capacity for this new operation mode.
And determining the residual capacity by predicting the completion time according to the issued current order, so as to judge whether the order task of the current order can be issued continuously or not by combining the order interception time. In the prior art, the method for determining the residual capacity is that in a common manned warehouse, a worker estimates the residual capacity according to experience and operation conditions. And for a new picking mode with man-machine cooperation, the residual capacity cannot be effectively calculated.
Disclosure of Invention
Embodiments of the present invention provide a method and an apparatus for determining remaining capacity, which can provide a method for calculating remaining capacity in a pick mode with human-machine cooperation.
To achieve the above object, according to an aspect of an embodiment of the present invention, a method for determining a remaining capacity is provided.
The method for calculating the residual capacity comprises the following steps: determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list; estimating the picking time of the target list according to the information of the man-machine cooperation bin, and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list; and if the remaining time is greater than a preset threshold value, issuing the current-time task list to the man-machine cooperation bin for picking.
Optionally, determining, according to the information of the human-computer cooperation bin and the current wave-time task list, a target list corresponding to the current wave-time task list includes: and performing grouping on the current wave task list according to the grouping mode information of the man-machine cooperation bin to obtain the target list.
Optionally, according to the information of the human-computer cooperation bin, estimating the picking time of the target list includes: determining storage nodes which the target list needs to traverse according to the information of the storage nodes in the man-machine cooperation bin; and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys.
Optionally, the target order comprises at least one pick order; and determining the storage node which needs to be traversed by the target list according to the information of the storage node in the man-machine cooperation bin comprises the following steps: and for each picking list, determining storage position nodes needing to be traversed by each picking list according to the information of the storage position nodes in the man-machine cooperation bin.
Optionally, estimating the picking time of the target list according to the number of available trolleys in the storage node and the human-computer cooperation bin and the speed of the trolleys comprises: calculating the picking time of each picking list according to the storage node which needs to be traversed by each picking list and the speed of the trolley; and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list.
Optionally, calculating the picking time of each picking list according to the bin nodes to be traversed by each picking list and the speed of the trolley comprises: determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list; determining the trolley driving time of each sorting menu according to the trolley driving path of each sorting menu; and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley dead time.
Optionally, the number of available trolleys in the human-computer cooperation bin is calculated through the number of trolleys in the human-computer cooperation bin and charge-discharge coefficients of the trolleys.
To achieve the above object, according to still another aspect of the embodiments of the present invention, there is provided an apparatus for determining remaining capacity.
The device for calculating the residual productivity, provided by the embodiment of the invention, comprises the following steps: the determining module is used for determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list; the estimation module is used for estimating the picking time of the target list according to the information of the man-machine cooperation bin and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list; and the issuing module is used for issuing the current-time task list to the man-machine cooperation bin for sorting if the remaining time is greater than a preset threshold value.
Optionally, the determining module is further configured to: and performing grouping on the current wave task list according to the grouping mode information of the man-machine cooperation bin to obtain the target list.
Optionally, the estimation module is further configured to: determining storage nodes which the target list needs to traverse according to the information of the storage nodes in the man-machine cooperation bin; and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys.
Optionally, the target order comprises at least one pick order; and the estimation module is further used for: and for each picking list, determining storage position nodes needing to be traversed by each picking list according to the information of the storage position nodes in the man-machine cooperation bin.
Optionally, the estimation module is further configured to: calculating the picking time of each picking list according to the storage node which needs to be traversed by each picking list and the speed of the trolley; and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list.
Optionally, the estimation module is further configured to: determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list; determining the trolley driving time of each sorting menu according to the trolley driving path of each sorting menu; and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley dead time.
Optionally, the number of available trolleys in the human-computer cooperation bin is calculated through the number of trolleys in the human-computer cooperation bin and charge-discharge coefficients of the trolleys.
To achieve the above object, according to still another aspect of embodiments of the present invention, there is provided an electronic apparatus.
An electronic device of an embodiment of the present invention includes: one or more processors; the storage device is used for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors realize the method for determining the residual capacity according to the embodiment of the invention.
To achieve the above object, according to still another aspect of an embodiment of the present invention, there is provided a computer-readable medium.
A computer readable medium of an embodiment of the present invention stores thereon a computer program, and the computer program, when executed by a processor, implements a method for determining remaining capacity of an embodiment of the present invention.
One embodiment of the above invention has the following advantages or benefits: the remaining time after the current-time task order is finished can be estimated by combining the information of the human-computer cooperation bin, and then whether the picking can be carried out in the human-computer cooperation bin is judged by utilizing the remaining time, so that the method for calculating the remaining capacity under the goods picking mode of human-computer cooperation can be provided; in the embodiment of the invention, the task sheets are formed by using the sheet forming mode of the man-machine cooperation bin, so that the task sheets can be formed by combining the characteristics of the man-machine cooperation bin, and the man-machine goods picking efficiency is improved; in the embodiment of the invention, the picking time is estimated by using the storage nodes in the human-computer cooperation bin and the number and speed of available trolleys, so that the picking time can be estimated by combining the specific information of the human-computer cooperation bin, and the practicability of the scheme is improved; in the embodiment of the invention, the picking time of each picking menu is firstly calculated, so that the picking time for finishing the target menu can be determined by combining the number of available trolleys; in the embodiment of the invention, when the picking time of each picking menu is calculated, the manual running time, the trolley running time, the manual picking time and the trolley dead time of each picking menu are comprehensively considered, so that the accuracy of calculating the picking time can be improved; the number of the available trolleys in the embodiment of the invention is obtained according to the number of the trolleys and the charge-discharge coefficient of the trolleys, so that the influence of the charging factor on the number of the available trolleys can be reduced.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
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The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram illustrating a main process of a method for determining remaining capacity according to an embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a main flow of a method for determining remaining capacity according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the main modules of an apparatus for determining remaining capacity according to an embodiment of the present invention;
FIG. 4 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 5 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram illustrating a main flow of a method for determining remaining capacity according to an embodiment of the present invention. According to a reference embodiment of the present invention, as shown in fig. 1, the method for determining remaining capacity mainly includes the following steps:
step S101: and determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list. The wave times in the invention are divided according to the order intercepting time, for example, according to the order placing time, the order of each half hour is classified into one wave time. There are many orders per wave, and a single order may include multiple orders. The target sheet corresponding to the task sheet refers to a combined list of the task sheets, for example, the current wave task sheet has 10 task sheets S1 to S10, and the 10 task sheets are divided into 3 target sheets P1, P2 and P3, where P1 includes the task sheets S1 to S4, P2 includes the task sheets S5 to S8, and P3 includes the task sheets S9 and S10.
As another embodiment of the present invention, the information of the human-machine cooperation bin may include: and grouping the single mode information. In step S101, determining the target sheet corresponding to the current wave-time task sheet according to the information of the human-machine cooperation bin and the current wave-time task sheet may include: and according to the group list mode information of the man-machine cooperation bin, performing group list on the current wave time task list to obtain a target list. The step is to combine the task groups to generate a target list according to the group single mode information of the man-machine cooperation bin. The group list mode information refers to a group list rule preset by the human-computer cooperation bin, wherein the group list rule can be set according to distribution information of storage nodes in the human-computer cooperation bin or attribute information of products stored in the storage nodes. For example, the product in the job ticket S1 is stored in the depot node A1, the product in the job ticket S2 is stored in the depot node A2, and A1 and A2 are adjacent in the collaborative bin, so the job ticket S1 and the job ticket S2 can be combined to form a target ticket.
Step S102: and estimating the picking time of the target list according to the information of the man-machine cooperation bin, and calculating the remaining time according to the list picking time, the current time and the picking time of the target list. In the invention, a man-machine cooperation picking operation mode is adopted to estimate the time spent on picking the target sheet, namely the time spent on picking the current secondary task sheet. The order-cutting time refers to the set time for completing the current-time order picking of the task, and the current time refers to the current time. The remaining time in the present invention is a value obtained by subtracting the current time from the order cutting time and then subtracting the sorting time. For example, if the order-picking time is 15 points, the current time is 14 points, and the estimated picking time is 20 minutes, the remaining time is 40 minutes.
As another embodiment of the present invention, the information of the human-machine cooperation bin may include: information of storage position nodes in the man-machine cooperation bin, the number of available trolleys in the man-machine cooperation bin and the speed of the trolleys. The estimating of the picking time of the target list according to the information of the human-machine cooperation bin in step S102 may include: determining storage nodes which need to be traversed by a target list according to information of the storage nodes in the man-machine cooperation bin; and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys. The car in the invention is an AGV car.
Wherein, the information of the storage node may include: the product stored in the storage node, therefore, the storage node that the target form needs to traverse can be determined according to the information of the storage node, for example, the storage node a1 is used for storing water and bread of the T brand, the storage node a2 is used for storing water and bread of the K brand, and if the target form includes water and bread of the T brand, the storage node that needs to traverse is a 1; alternatively, the bin node a1 is used to store water of T brand and water of K brand, the bin node a2 is used to store bread of T brand and bread of K brand, and if the target list includes water of T brand and bread, the bin nodes to be traversed are a1 and a 2. And then, calculating the time for picking the target list according to the conditions of the storage nodes and the trolleys in the man-machine cooperation bin.
As yet another embodiment of the present invention, the target order may include at least one pick menu. The pick sheet of the present invention refers to a list of products that are picked at a time. For example, the current wave time task list has 10 task lists, and if the 10 task lists are divided into 3 target lists, each target list is a sorting list. The step of estimating the picking time of the target list may specifically include: for each picking list, determining storage node needing to be traversed by each picking list according to the information of the storage node in the man-machine cooperation bin; calculating the picking time of each picking list according to the speed of the storage node and the trolley which need to be traversed by each picking list; and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list. The step firstly calculates the picking time of each picking menu, and then estimates the picking time for finishing the current task list according to the picking time of all the picking menus and the number of the trolleys.
The invention finishes the sorting of the current wave Task list, namely, at least one sorting menu is sorted, and the Task of the sorting list is picked in a man-machine cooperation biniAnd distributing to a trolley. Suppose that each pick order TaskiThe estimated picking time is Task _ timeiThe cart j completes the assigned menu TaskiAt the time point of CTjIts initial value is to complete the current menu-selecting TaskiBy the current picklist TaskiThe start time plus the task completion time. Because a plurality of trolleys respectively execute different picking single tasks TaskiEach picking order TaskiIs selected, the pick time Task _ timeiUnequal, the current menu picking Task is executed at the earliestiThe trolley firstly receives a new picking single TaskiTherefore, the time CT is updated as follows: traversal of the picking Task from the headiSelecting k as argminCTjUpdating CT for task completion time of trolley kk＝CTk+Task_timei. Wherein, updating the task completion time of the trolley k can be understood as: assuming that the Task is completed by 5 trolleys and the trolley k is the fastest, the next executed picking Task is performed according to the order-of-group sequenceiIs sent to the trolley and carries out the time point CT of the arrangement task of the trolleykAnd (6) updating. Pre-estimating and picking current wave taskThe required time of the sheet is the time length required by the trolley which takes the longest time when all the task sheets of the wave are executed, so that the following steps can be obtained: the picking time of the target sheet, i.e. the picking time of the current wave order sheet, is maxCTj。
As still another embodiment of the present invention, the calculating the picking time of each picking menu in the above steps may include: determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list; determining the trolley driving time of each sorting list according to the trolley driving path of each sorting list; and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley stagnation time.
Suppose, a single Task is picked1The bin nodes to be traversed are { A4, A6, A18, A7, A10 }. And driving according to the sequence of Start, A4, A6, A18, A7, A10 and End, wherein the Start and the End are respectively a unified departure point and a picking completion virtual point of the trolley. The total distance of the running path of the trolley in the picking area is recorded as Taskpath1. Wherein the distance between two storage nodes is the shortest distance, the running speed of the trolley is v, and then the picking single Task is carried out1Is selected time Task _ time1Comprises the following steps:
Task_timei＝Taskpath1/v+T1*5+T2+T3
wherein, Taskpath1The driving time of the trolley refers to the driving time of the trolley in the storage node area; t is15 is the manual driving time, 5 is the number of the storage nodes needing to be traversed, T1The method comprises the steps of estimating the time from a person to a storage node, acquiring the average distance from all the storage nodes according to the size of a working logic area where the person is located, and then acquiring the average time from the person to the storage node according to the average walking speed of the person; t is2The statistical quantity can be directly obtained from the picking data of a common manned warehouse; t is3Refers to the dead time of the trolley, and refers to the trolley from the beginning of the inventionThe time estimation from the virtual termination point to the starting point again, namely the time from the composite packaging to the binding to the parking area to the starting point, does not need to consider the waiting time of the trolley in the parking area due to idle, and the virtual termination point is a point which is virtually simulated on a map when the TSP is calculated and is used as the termination point of the TSP. TSP is a traveler Problem, Traveling Salesman Problem, a classical combinatorial optimization Problem, the sources are: a salesperson who goes to several cities to market goods needs to go from one city, go through all cities, return to the departure place, and select a traveling route to minimize the total travel. It is meant here that the shortest path is needed for the computing cart to pass through all the bin nodes.
In the embodiment of the invention, the number of the available trolleys in the man-machine cooperation bin is obtained by calculating the number of the trolleys in the man-machine cooperation bin and the charge-discharge coefficient of the trolleys. In the invention, the influence of trolley charging on the residual capacity is considered, the number n of trolleys is multiplied by a coefficient gamma, and if the trolley charging and discharging coefficient is c, the gamma is c/(1+ c), so that the number of available trolleys in the man-machine cooperation bin is n gamma.
Step S103: and if the residual time is greater than a preset threshold value, issuing the current wave-time task list to a man-machine cooperation bin for sorting. The remaining time is calculated in the step S102, and is a difference obtained by subtracting the current time from the sheet cutting time and then subtracting the picking time, if the difference is greater than a preset threshold (the present invention may be, but is not limited to, 15 minutes, and may be adjusted according to actual conditions), the current wave-time task is allowed to be issued to the man-machine cooperation bin for man-machine picking, otherwise, the current wave-time task sheet is not recommended to be issued to the ordinary man-machine bin for picking. Wherein, the preset threshold is used for buffering the abnormal condition, and the longer the threshold time is, the more conservative the scheme is. In addition, the invention can estimate the remaining time every 1 minute (which can be but not limited to 1 minute, and needs to be set according to the actual situation), and add an upper limit threshold value, for example 500, to the number of receivable task lists each time, so that the shortage of the remaining capacity caused by the overhigh number of task lists issued at one time can be prevented.
FIG. 2 is a schematic diagram illustrating a main flow of a method for determining remaining capacity according to an embodiment of the present invention. As shown in fig. 2, the main process of the method for determining remaining capacity of the present invention may include:
step S201: receiving a task for determining the residual capacity of the current-time task list, and acquiring information of a human-machine cooperation bin for executing the task, wherein the information of the human-machine cooperation bin may include: group single mode information, information of storage position nodes in the man-machine cooperation bin, the number of available trolleys in the man-machine cooperation bin and the speed of the trolleys;
step S202: according to the group list mode information of the man-machine cooperation bin, performing group list on the current wave-time task list to obtain a target list corresponding to the current wave-time task list, wherein the target list comprises at least one sorting list;
step S203: aiming at a picking list, determining storage node needing to be traversed by the picking list according to the information of the storage node in the man-machine cooperation bin;
step S204: determining the manual traveling time, the trolley traveling path, the manual picking time and the trolley dead time of the picking order according to the storage nodes which need to be traversed by the picking order;
step S205: determining the trolley travel time of the sorting list according to the trolley travel path of the sorting list;
step S206: calculating the picking time of the picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley stagnation time;
step S207: judging whether the sorting time of all the sorting menus is calculated, if so, executing the step S208;
step S208: estimating the picking time of the target list according to the number of the available trolleys and the picking time of all the picking lists;
step S209: calculating the remaining time according to the picking time, the order cutting time and the current time;
step S210: judging whether the remaining time is greater than a preset threshold value, if so, executing a step S211, otherwise, executing a step S212;
step S211: sending the current wave task list to a man-machine cooperation bin for sorting;
step S212: and issuing the current wave-time task list to a common manned warehouse for sorting.
According to the technical scheme for determining the residual capacity, the residual time after the current secondary task order is finished can be estimated by combining the information of the human-computer cooperation bin, and then whether the picking can be carried out in the human-computer cooperation bin is judged by utilizing the residual time, so that the method for calculating the residual capacity under the human-computer cooperation picking mode can be provided; in the embodiment of the invention, the task sheets are formed by using the sheet forming mode of the man-machine cooperation bin, so that the task sheets can be formed by combining the characteristics of the man-machine cooperation bin, and the man-machine goods picking efficiency is improved; in the embodiment of the invention, the picking time is estimated by using the storage nodes in the human-computer cooperation bin and the number and speed of available trolleys, so that the picking time can be estimated by combining the specific information of the human-computer cooperation bin, and the practicability of the scheme is improved; in the embodiment of the invention, the picking time of each picking menu is firstly calculated, so that the picking time for finishing the target menu can be determined by combining the number of available trolleys; in the embodiment of the invention, when the picking time of each picking menu is calculated, the manual running time, the trolley running time, the manual picking time and the trolley dead time of each picking menu are comprehensively considered, so that the accuracy of calculating the picking time can be improved; the number of the available trolleys in the embodiment of the invention is obtained according to the number of the trolleys and the charge-discharge coefficient of the trolleys, so that the influence of the charging factor on the number of the available trolleys can be reduced.
Fig. 3 is a schematic diagram of main modules of an apparatus for determining remaining capacity according to an embodiment of the present invention. As shown in fig. 3, the apparatus 300 for determining remaining capacity of the present invention mainly comprises the following modules: a determination module 301, a prediction module 302 and a sending down module 303.
The determining module 301 may be configured to determine a target list corresponding to the current wave-time task list according to the information of the human-machine cooperation bin and the current wave-time task list. The estimation module 302 may be configured to estimate the picking time of the target list according to the information of the human-computer cooperation bin, and calculate the remaining time according to the picking time of the target list, the current time, and the picking time of the target list. The issuing module 303 may be configured to issue the current wave-time task list to the human-machine cooperation bin for sorting if the remaining time is greater than a preset threshold.
In the embodiment of the present invention, the information of the human-computer cooperation bin may include: and grouping the single mode information. The determination module 301 may also be configured to: and according to the group list mode information of the man-machine cooperation bin, performing group list on the current wave time task list to obtain a target list.
In the embodiment of the present invention, the information of the human-computer cooperation bin may include: information of storage position nodes in the man-machine cooperation bin, the number of available trolleys in the man-machine cooperation bin and the speed of the trolleys. The prediction module 302 may also be configured to: determining storage nodes which need to be traversed by a target list according to information of the storage nodes in the man-machine cooperation bin; and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys.
In an embodiment of the present invention, the target menu may include at least one pick menu. The prediction module 302 may also be configured to: and aiming at each picking list, determining storage position nodes which need to be traversed by each picking list according to the information of the storage position nodes in the man-machine cooperation bin.
In this embodiment of the present invention, the estimation module 302 may further be configured to: calculating the picking time of each picking list according to the speed of the storage node and the trolley which need to be traversed by each picking list; and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list.
In this embodiment of the present invention, the estimation module 302 may further be configured to: determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list; determining the trolley driving time of each sorting list according to the trolley driving path of each sorting list; and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley stagnation time.
In the embodiment of the invention, the number of the available trolleys in the man-machine cooperation bin is obtained by calculating the number of the trolleys in the man-machine cooperation bin and the charge-discharge coefficient of the trolleys.
From the above description, it can be seen that the remaining time after the current secondary task order is completed can be estimated by combining the information of the human-computer cooperation bin, and then whether the picking can be performed in the human-computer cooperation bin can be judged by using the remaining time, so that a method for calculating the remaining capacity in the human-computer cooperation picking mode can be provided; in the embodiment of the invention, the task sheets are formed by using the sheet forming mode of the man-machine cooperation bin, so that the task sheets can be formed by combining the characteristics of the man-machine cooperation bin, and the man-machine goods picking efficiency is improved; in the embodiment of the invention, the picking time is estimated by using the storage nodes in the human-computer cooperation bin and the number and speed of available trolleys, so that the picking time can be estimated by combining the specific information of the human-computer cooperation bin, and the practicability of the scheme is improved; in the embodiment of the invention, the picking time of each picking menu is firstly calculated, so that the picking time for finishing the target menu can be determined by combining the number of available trolleys; in the embodiment of the invention, when the picking time of each picking menu is calculated, the manual running time, the trolley running time, the manual picking time and the trolley dead time of each picking menu are comprehensively considered, so that the accuracy of calculating the picking time can be improved; the number of the available trolleys in the embodiment of the invention is obtained according to the number of the trolleys and the charge-discharge coefficient of the trolleys, so that the influence of the charging factor on the number of the available trolleys can be reduced.
Fig. 4 illustrates an exemplary system architecture 400 of a method for determining remaining capacity or an apparatus for determining remaining capacity to which embodiments of the present invention may be applied.
As shown in fig. 4, the system architecture 400 may include terminal devices 401, 402, 403, a network 404, and a server 405. The network 404 serves as a medium for providing communication links between the terminal devices 401, 402, 403 and the server 405. Network 404 may include various types of connections, such as wire, wireless communication links, or fiber optic cables, to name a few.
A user may use terminal devices 401, 402, 403 to interact with a server 405 over a network 404 to receive or send messages or the like. The terminal devices 401, 402, 403 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 401, 402, 403 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 405 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 401, 402, 403. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the method for determining the remaining capacity provided by the embodiment of the invention is generally executed by the server 405, and accordingly, the device for determining the remaining capacity is generally disposed in the server 405.
It should be understood that the number of terminal devices, networks, and servers in fig. 4 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 5, shown is a block diagram of a computer system 500 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 5, the computer system 500 includes a Central Processing Unit (CPU)501 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the system 500 are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
The following components are connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 501.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a determination module, an estimation module, and a delivery module. The names of the modules do not limit the modules, for example, the determining module can also be described as a module for determining a target list corresponding to the current wave-time task list according to the information of the human-machine cooperation bin and the current wave-time task list.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list; estimating the picking time of the target list according to the information of the man-machine cooperation bin, and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list; and if the residual time is greater than a preset threshold value, issuing the current wave-time task list to a man-machine cooperation bin for sorting.
According to the technical scheme of the embodiment of the invention, the remaining time after the current-time task order is finished can be estimated by combining the information of the man-machine cooperation bin, and then whether the picking can be carried out in the man-machine cooperation bin is judged by utilizing the remaining time, so that the method for calculating the remaining capacity under the goods picking mode of man-machine cooperation can be provided; in the embodiment of the invention, the task sheets are formed by using the sheet forming mode of the man-machine cooperation bin, so that the task sheets can be formed by combining the characteristics of the man-machine cooperation bin, and the man-machine goods picking efficiency is improved; in the embodiment of the invention, the picking time is estimated by using the storage nodes in the human-computer cooperation bin and the number and speed of available trolleys, so that the picking time can be estimated by combining the specific information of the human-computer cooperation bin, and the practicability of the scheme is improved; in the embodiment of the invention, the picking time of each picking menu is firstly calculated, so that the picking time for finishing the target menu can be determined by combining the number of available trolleys; in the embodiment of the invention, when the picking time of each picking menu is calculated, the manual running time, the trolley running time, the manual picking time and the trolley dead time of each picking menu are comprehensively considered, so that the accuracy of calculating the picking time can be improved; the number of the available trolleys in the embodiment of the invention is obtained according to the number of the trolleys and the charge-discharge coefficient of the trolleys, so that the influence of the charging factor on the number of the available trolleys can be reduced.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (16)
1. A method for determining remaining capacity, comprising:
determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list;
estimating the picking time of the target list according to the information of the man-machine cooperation bin, and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list;
and if the remaining time is greater than a preset threshold value, issuing the current-time task list to the man-machine cooperation bin for picking.
2. The method of claim 1, wherein determining the target sheet corresponding to the current wave order according to the information of the human-machine cooperation bin and the current wave order comprises: and performing grouping on the current wave task list according to the grouping mode information of the man-machine cooperation bin to obtain the target list.
3. The method of claim 1, wherein estimating the picking time of the target sheet according to the information of the human-machine cooperation bin comprises:
determining storage nodes which the target list needs to traverse according to the information of the storage nodes in the man-machine cooperation bin;
and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys.
4. The method of claim 3, wherein the target menu comprises at least one pick menu; and
determining the storage node which the target list needs to traverse according to the information of the storage node in the man-machine cooperation bin comprises the following steps: and for each picking list, determining storage position nodes needing to be traversed by each picking list according to the information of the storage position nodes in the man-machine cooperation bin.
5. The method of claim 4, wherein estimating the picking time for the target sheet based on the number of available carts in the depots node and the ergonomic bin and the speed of the carts comprises:
calculating the picking time of each picking list according to the storage node which needs to be traversed by each picking list and the speed of the trolley;
and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list.
6. The method of claim 5, wherein calculating the picking time for each pick sheet based on the bin nodes that each pick sheet needs to traverse and the speed of the cart comprises:
determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list;
determining the trolley driving time of each sorting menu according to the trolley driving path of each sorting menu;
and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley dead time.
7. The method as claimed in claim 3, wherein the number of available trolleys in the human-machine cooperation bin is calculated by the number of trolleys in the human-machine cooperation bin and charge-discharge coefficients of the trolleys.
8. An apparatus for determining remaining capacity, comprising:
the determining module is used for determining a target list corresponding to the current wave-time task list according to the information of the man-machine cooperation bin and the current wave-time task list;
the estimation module is used for estimating the picking time of the target list according to the information of the man-machine cooperation bin and calculating the remaining time according to the list cutting time, the current time and the picking time of the target list;
and the issuing module is used for issuing the current-time task list to the man-machine cooperation bin for sorting if the remaining time is greater than a preset threshold value.
9. The apparatus of claim 8, wherein the determining module is further configured to: and performing grouping on the current wave task list according to the grouping mode information of the man-machine cooperation bin to obtain the target list.
10. The apparatus of claim 8, wherein the prediction module is further configured to:
determining storage nodes which the target list needs to traverse according to the information of the storage nodes in the man-machine cooperation bin;
and estimating the picking time of the target list according to the number of the available trolleys in the storage node and the man-machine cooperation bin and the speed of the trolleys.
11. The apparatus of claim 10, wherein the target list comprises at least one pick menu; the estimation module is further configured to: and for each picking list, determining storage position nodes needing to be traversed by each picking list according to the information of the storage position nodes in the man-machine cooperation bin.
12. The apparatus of claim 11, wherein the prediction module is further configured to:
calculating the picking time of each picking list according to the storage node which needs to be traversed by each picking list and the speed of the trolley;
and estimating the picking time of the target list according to the number of the available trolleys in the man-machine cooperation bin and the picking time of each picking list.
13. The apparatus of claim 12, wherein the prediction module is further configured to:
determining the manual running time, the trolley running path, the manual picking time and the trolley dead time of each picking list according to the storage node which needs to be traversed by each picking list;
determining the trolley driving time of each sorting menu according to the trolley driving path of each sorting menu;
and calculating the picking time of each picking menu according to the manual running time, the trolley running time, the manual picking time and the trolley dead time.
14. The device of claim 10, wherein the number of available trolleys in the human-machine cooperation bin is calculated through the number of trolleys in the human-machine cooperation bin and charge-discharge coefficients of the trolleys.
15. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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