CN112396362B - Method, device and storage medium for determining driving destination - Google Patents
Method, device and storage medium for determining driving destination Download PDFInfo
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Abstract
The application discloses a method and a device for determining a driving destination and a storage medium, and relates to an automatic warehouse technology. The specific scheme comprises the following steps: determining, for at least one storage location, a delivery cost of the storage location according to a distance from the storage location to each workstation, a working capacity of each workstation, and congestion information of the storage location; and determining a storage list according to the ex-warehouse cost of each storage, determining a target storage from the storage list according to the ex-warehouse frequency of cargoes transported by the transfer robot, and taking the target storage as a driving destination of the transfer robot. The application can overcome the defect brought by the rough management mode of the travel destination of the transfer robot in the prior art, and carry out fine management on the travel destination of the transfer robot, thereby avoiding the congestion of the transfer robot, improving the storage efficiency and reducing the management and operation cost of storage.
Description
Technical Field
The present application relates to automated warehouse technology, and more particularly, to a method and apparatus for determining a travel destination, and a storage medium.
Background
In automated warehouses, handling robots, storage locations, and workstations are typically included. The carrier robot is provided with a goods shelf, and goods are stored on the goods shelf. The storage is used for parking the transfer robot and storing goods stored on the transfer robot. The workstation is used for sorting goods, storing goods or checking goods and the like. When the goods need to be delivered, put in storage or checked, the transfer robot needs to carry the goods to the workstation, and after the transfer robot finishes the work from the workstation, the problem of returning to which storage position, that is, the problem of taking which storage position as the running destination of the transfer robot exists.
In the prior art, a storage position closest to the vehicle is usually selected as a driving destination; or dividing the storage positions in the automatic warehouse into different areas according to the goods attributes such as goods brands or goods types, and randomly selecting the storage position or selecting the storage position closest to the storage position in the corresponding area as a driving destination. However, in any of the above methods, the management of the driving destination is too extensive, and the frequency of the in-out warehouse of different cargoes is not considered sufficiently, so that the warehouse efficiency is reduced, and the acquisition and centralized storage of the in-out warehouse with high frequency are easy to cause, so that the transportation robot in the automated warehouse is jammed in a local area, the warehouse efficiency is further reduced, and the management and operation cost of the warehouse are improved.
Disclosure of Invention
Accordingly, a main object of the present application is to provide a method for determining a travel destination, which can overcome the drawbacks of the prior art caused by a rough management method for the travel destination of a transfer robot, and perform fine management on the travel destination of the transfer robot, so as to avoid congestion of the transfer robot, thereby improving storage efficiency and reducing storage management and operation costs.
In order to achieve the above purpose, the technical scheme provided by the application is as follows:
in a first aspect, an embodiment of the present application provides a method for determining a driving destination, including the following steps:
Determining, for at least one storage location, a delivery cost of the storage location according to a distance from the storage location to each workstation, a working capacity of each workstation, and congestion information of the storage location; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; the ex-warehouse cost is the cargo transportation efficiency of the transfer robot taking the storage as a driving destination when the transfer robot is stored in the storage;
determining a storage position list according to the ex-warehouse cost of each storage position, determining a target storage position from the storage position list according to the in-warehouse frequency of cargoes transported by the transfer robot, and taking the target storage position as a driving destination of the transfer robot; and the storage list stores each storage according to the size sequence of the ex-warehouse cost.
In one possible embodiment, the step of determining the delivery cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage includes:
Determining, for each of the workstations, a distance cost of the storage to the workstation based on the congestion information of the storage, a minimum value of the distance of the storage to each workstation, and the distance of the storage to the workstation;
obtaining the ex-warehouse cost from the storage location to the workstation according to the distance cost from the storage location to the workstation and the working capacity of the workstation;
and determining the ex-warehouse cost of the storage according to the ex-warehouse cost of the storage to each workstation.
In a possible implementation manner, before the step of determining the delivery cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage, the method further includes the step of determining the working capacity of the workstation:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
For each workstation, determining the working proportion processed by the workstation according to the working amount processed by the workstation and the working total amount processed by each workstation, and taking the working proportion as the working capacity of the workstation.
In a possible implementation manner, before the step of determining the delivery cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage, the method further includes the step of determining the working capacity of the workstation:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
for each workstation, determining the working proportion of the processing of the workstation according to the working amount of the processing of the workstation and the working total amount of the processing of each workstation;
Determining smoothing parameters according to the maximum value and the minimum value in the workload processed by each workstation respectively and preset configuration parameters;
and determining the working capacity of the workstation according to the smoothing parameters and the working proportion processed by the workstation.
In a possible implementation manner, before the step of determining the delivery cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage, the method further includes the step of determining the congestion information of each storage:
acquiring a distribution diagram of the transfer robot;
Dividing the distribution map into grids with preset sizes, and counting the number of the carrying robots in each grid;
Determining a congestion area from each grid according to the number of the transfer robots;
And determining the congestion information of each storage bit according to the congestion area.
In a possible implementation manner, the step of determining a congestion area from each grid according to the number of the handling robots includes:
For each grid, calculating a congestion coefficient according to the number of transfer robots in the grid; the congestion coefficient is the sparseness degree of the transfer robot in the grid;
And if the congestion coefficient of the grid is larger than a preset coefficient threshold value, the grid is the congestion area.
In one possible embodiment, the step of determining the target storage location from the storage location list according to the frequency of the in-out of the cargoes transported by the transfer robot includes:
According to the frequency of the goods transported by the transfer robot in and out of the warehouse, determining more than two alternative storage positions from the storage position list;
And determining any idle storage bit in the alternative storage bits as the target storage bit.
In a second aspect, an embodiment of the present application further provides a device for determining a driving destination, including:
The cost calculation module is used for determining the ex-warehouse cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage aiming at least one storage; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; the ex-warehouse cost is the cargo transportation efficiency of the transfer robot taking the storage as a driving destination when the transfer robot is stored in the storage;
A list generation module for determining a list of storage locations based on the cost of delivery of each of the storage locations,
The destination determining module is used for determining a target storage position from the storage position list according to the in-out frequency of cargoes transported by the transfer robot, and taking the target storage position as a running destination of the transfer robot; and the storage list stores each storage according to the size sequence of the ex-warehouse cost.
In a third aspect, embodiments of the present application further provide a computer readable storage medium, which may improve warehouse efficiency and reduce management and operation costs of warehouse. The specific scheme is as follows:
A computer readable storage medium storing computer instructions which when executed by a processor perform the steps of any one of the possible implementations of the first aspect and the first aspect.
In a fourth aspect, the embodiment of the application further provides an electronic device, which can improve warehousing efficiency and reduce management and operation costs of warehousing. The specific scheme is as follows:
an electronic device comprising the computer readable storage medium described above, and further comprising a processor executable to the computer readable storage medium.
In summary, the present application provides a method, an apparatus and a storage medium for determining a driving destination. When determining which storage position is used as the driving destination of the transfer robot, the distance from the storage position to the working station, the working capacity of the working station and the congestion information of the storage position are comprehensively considered, the storage cost of the storage position is determined according to the distance from the storage position to the working station, the working capacity of the working station and the congestion information of the storage position, and the storage position list is travelled according to the storage cost of the storage position. Further, in the present application, when determining which storage location should be specifically selected from the storage location list as the travel destination of the transfer robot, the destination storage location is selected from the storage location list as the travel destination of the transfer robot according to the frequency of the storage access of the goods transported by the transfer robot, taking into consideration the frequency information of the storage access of the goods transported by the transfer robot. Therefore, the application comprehensively considers the distance from the storage position to the workstation, the working capacity of the workstation, the congestion information of the storage position and the free selling degree of goods transported by the transfer robot, establishes a one-to-one relationship between the transfer robot and the target storage position on the basis, finely manages the running destination of the transfer robot and improves the storage efficiency. In addition, due to the fact that congestion information of the storage position is considered, congestion of the transfer robot can be avoided as much as possible, storage efficiency is further improved, and management and operation costs of storage are reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a flow chart of a method for determining a driving destination according to an embodiment of the present application;
fig. 2 is a flow chart of another method for determining a driving destination according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a storage list and target storage determination;
Fig. 4 is a schematic structural diagram of a driving destination determining apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In automated warehouses, handling robots, storage locations, and workstations are typically included. The carrier robot is provided with a goods shelf, and goods are stored on the goods shelf. The storage is used for parking the transfer robot and storing goods stored on the transfer robot. The workstation is used for sorting goods, storing goods or checking goods and the like. When the goods need to be delivered, put in storage or checked, the goods need to be carried by the carrying robot to the workstation, and after the carrying robot finishes the work from the workstation, the goods carried by the carrying robot may change or the sales information of the goods carried by the carrying robot may change, so the carrying robot will not return to the storage place when starting in most cases. Therefore, after the transfer robot completes the work from the workstation, there is a problem of which storage location is the travel destination of the transfer robot.
In the prior art, the following two schemes are generally adopted to solve the problem of determining the driving destination:
(1) And selecting the storage position closest to the driving destination.
The scheme is too simple, the frequency of the in-out and out-in of the cargoes is completely absent, the cargoes in and out-in at low frequency are easily arranged at the position close to the workstation, the cargoes in and out-in at high frequency are arranged at the position far away from the workstation, the transportation distance of the transfer robot is increased, and the storage efficiency is reduced.
(2) According to the goods attributes such as goods brands or goods types, the storage positions in the automatic warehouse are divided into different areas, and the storage positions are randomly selected in the corresponding areas or the storage position closest to the storage position is selected as a driving destination.
The method for dividing the storage according to the cargo attribute has the advantages that the division standard is too wide, and even if the storage positions of the same type are still different, the distances between the storage positions and the workstations are different; the same goods in the same class are difficult to ensure the same frequency of the in-out warehouse, and after the storage positions are divided according to the property of the goods, the same goods with different in-out warehouse frequencies are treated equally. In addition, the variety of goods stored in the automated warehouse is various, and each type of goods needs to be independently configured with reserved storage quantity and storage position, and the configuration process is usually carried out according to expert experience, and is very dependent on personal experience, so that the management and working cost of the warehouse are increased, the manual intervention degree is too high, the intelligent is not realized, and errors are easy to occur. Thirdly, according to the method for dividing the storage positions according to the goods attribute, the same type of goods are stored in the storage positions close to each other in a concentrated mode, if the type of goods are mostly mass-market goods, the area transfer robot is jammed, storage efficiency is further reduced, and storage management and working cost is improved.
In view of the above, the present application provides a method, an apparatus and a storage medium for determining a driving destination. The application has the core points that the distance from the storage position to the working station, the working capacity of the working station, the congestion information of the storage position and the selling degree of goods transported by the transfer robot are comprehensively considered for selecting the driving destination of the transfer robot, a one-to-one relation between the transfer robot and the target storage position is established on the basis, the driving destination of the transfer robot is finely managed, the storage efficiency is improved, the congestion of the transfer robot is avoided as much as possible, and the storage management and operation cost is reduced.
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail with specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Example 1
Fig. 1 is a schematic flow chart of a first embodiment of the present application, as shown in fig. 1, the embodiment mainly includes:
S101: determining, for at least one storage location, a delivery cost of the storage location according to a distance from the storage location to each workstation, a working capacity of each workstation, and congestion information of the storage location; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; when the carrying robot is stored in the storage position, the carrying robot takes the storage position as the cargo transportation efficiency of the driving destination.
The method for determining the driving destination provided by the embodiment of the application can determine the driving destination of the transfer robot in real time when the transfer robot finishes the work such as warehouse-out, warehouse-in or inventory-in at the workstation each time, and can also determine whether the transfer robot needs to replace a storage position at preset time intervals.
When determining a destination, it is necessary to determine the delivery cost of a storage location, which is the cargo transportation efficiency of a transfer robot using the storage location as the destination when the transfer robot is stored in the storage location. Here, the cost may refer to time cost or distance cost. The delivery costs are used to describe the cargo transportation efficiency of a storage location.
According to the embodiment of the application, the ex-warehouse cost of the storage location is determined according to the distance from the storage location to each workstation, the working capacity of each workstation and the congestion information of the storage location. The distance from the storage location to the workstation is the actual distance from the storage location to the workstation, and can be directly obtained or determined by using any common distance calculation method. The working capacity of the workstation represents the working efficiency of the workstation for measuring the cargo throughput parameters of the workstation. The congestion information of the storage is used for representing the influence of congestion on the driving path of the transfer robot on the transportation efficiency of the goods in the storage.
S102: determining a storage position list according to the ex-warehouse cost of each storage position, determining a target storage position from the storage position list according to the in-warehouse frequency of cargoes transported by the transfer robot, and taking the target storage position as a driving destination of the transfer robot; and the storage list stores each storage according to the size sequence of the ex-warehouse cost.
The strategy of sequencing the storage according to the order of the ex-warehouse cost is to comprehensively consider the positions of the storage and the work stations, the working capacity of the work stations and the congestion condition of the transfer robot, and the storage list is not static and unchanged but is dynamically adjusted according to real-time data or historical data of a preset time interval, so that the storage sequencing is more flexible and accurate.
The storage list can arrange each storage according to the order of the ex-warehouse cost from small to large, namely the storage with the minimum ex-warehouse cost is arranged at the first position of the storage list; and each storage position can be arranged in reverse order from large to small according to the size sequence of the ex-warehouse cost, namely the storage position with the minimum ex-warehouse cost is arranged at the last position of the storage position list. Regardless of the arrangement, the listing of storage locations characterizes the cargo transportation efficiency ranking of each storage location.
The frequency of the in-out warehouse of the goods transported by the transfer robot can be represented by sales information, mass sales information, click rate information or click rate information of the goods transported by the transfer robot, the target storage position is determined from the storage position list according to the in-out warehouse frequency of the goods transported by the transfer robot, and the target storage position is used as a driving destination of the transfer robot, so that the transfer robot with high in-out warehouse frequency and carrying goods can be parked at the storage position with high goods transportation efficiency, storage efficiency is improved, and storage management and operation cost is reduced.
Example two
As shown in fig. 2, another method for determining a driving destination according to an embodiment of the present application includes:
s201: for at least one storage location, a distance of the storage location to each workstation is determined.
The manhattan distance from the storage to the workstation may be calculated using any of the commonly used algorithms such as Dijkstra, a Star algorithm (a-Star), etc., and the manhattan distance is used as the distance from the storage to the workstation.
In determining the travel destination, a target storage location may be selected based on each storage location in the automated warehouse; the automated warehouse may also be divided into at least one area, and the target storage locations are selected based on the storage locations in the at least one area.
S202: and determining congestion information of the storage bit.
The congestion information of the storage is determined according to the congestion area of the transfer robot in the automated warehouse on the travelling path, and the congestion area in the automated warehouse needs to be determined first before the congestion information of the storage is determined. By way of example, the following steps 1 to 4 may be employed to determine congestion information for a reservoir:
step 1, acquiring a distribution diagram of the transfer robot.
Specifically, a real-time profile of the transfer robot in the automated warehouse may be obtained, or a history profile of the transfer robot in the automated warehouse within a time period may be obtained. In practical implementation, the distribution diagram of the transfer robot is generally a distribution grid diagram of the transfer robot, and one cell in the distribution grid diagram may be sized to accommodate one transfer robot, and the cell is generally square, but the shape of the cell is not limited thereto, and may be hexagonal. The distribution map of the transfer robot may be a grid map of the entire automated warehouse, or may be a regional grid map including the travel path of the robot in the automated warehouse.
And step 2, dividing the distribution map into grids with preset sizes, and counting the number of the carrying robots in each grid.
The distribution map is divided into grids of a predetermined size, and a square shape is generally used as the grid shape, but the grid shape is not limited thereto, and may be a hexagon or the like. When the distribution map of the transfer robot is a mesh map, each divided mesh includes a predetermined number of cells. The number of transfer robots in each grid may be counted in particular by counting the number of cells in each grid that are occupied by transfer robots.
And 3, determining a congestion area from each grid according to the number of the transfer robots.
The congestion area can be determined directly according to the number of the transfer robots in each grid, and the grid with the number of the transfer robots being greater than a preset number threshold is used as the congestion area. The congestion coefficient may be calculated for each grid according to the number of transfer robots in the grid; the congestion coefficient is the sparseness degree of the transfer robot in the grid. The congestion factor may in particular be determined from the number of cells in the grid occupied by the handling robot and the total number of cells contained in the grid, typically the ratio of the number of cells occupied by the handling robot to the total number of cells contained in the grid. A preset coefficient value may be set, and if the congestion coefficient of the grid is greater than a preset coefficient threshold, the grid is the congestion area. Preferably, the coefficient threshold value δ may be set to 0.3. Ltoreq.δ < 1.
And 4, determining the congestion information of each storage bit according to the congestion area.
Specifically, a storage bit with a distance from the congestion area within a preset distance range can be marked as a storage bit affected by congestion; and/or, according to the travel path of the transfer robot in the congestion area, the storage of the departure place or destination of the transfer robot may be marked as the storage affected by the congestion.
In one possible implementation, the congestion identifier a may be used as congestion information, where the congestion identifier a is marked on a storage location affected by congestion, and the congestion identifier a is not marked on a storage location unaffected by congestion.
In another possible implementation manner, after determining the storage affected by the congestion, the congestion area affecting the storage is recorded, and then congestion information may be determined according to the number of transfer robots in the congestion area, where the congestion information of the storage affected by the congestion is a congestion coefficient affecting the congestion area of the storage. The congestion information of the storage bit not affected by congestion is 0.
S203: and determining the distance cost of the storage position to the working station according to the congestion information of the storage position, the minimum value in the distance of the storage position to each working station and the distance of the storage position to the working station for each working station.
By way of example, the following equation (1) may be used to determine the cost of distance from the storage location to each workstation, i aw, based on the congestion information for the storage location, the minimum of the distances from the storage location to each workstation, and the distance from the storage location to the workstation:
wherein l aw is the distance cost of the storage to the workstation. And l w is the distance from the storage location to the workstation. For the minimum in the distance of the storage to each workstation.
And determining according to the congestion information of the storage bit. In one possible implementation, when using the congestion identification a as congestion information: when the bin is marked with a congestion identification a,Is a coefficient threshold delta; when the bin is not marked with the congestion identification a,Is 0. In another possible embodiment, when determining congestion information according to the number of transfer robots in the congestion area,Is the value of the congestion information.
S204: the operating capabilities of the workstation are determined.
The working capacity of each workstation represents the cargo throughput of the workstation and is also the working efficiency of the workstation in sorting, ex-warehouse, warehouse-in, inventory and other operations of the cargo. By way of example, the following two possible implementations may be employed to determine the operational capabilities of the workstation.
A: in a possible implementation manner, the workload processed by each workstation in a preset time range is acquired; for each workstation, determining the working proportion processed by the workstation according to the working amount processed by the workstation and the working total amount processed by each workstation, and taking the working proportion as the working capacity of the workstation. The preset time range may be in a dimension of days, for example, 15 days or 30 days; the time may be in the dimension of hours, for example, 24 hours or 12 hours may be used as the preset time range. The obtained workload processed by each workstation is the number of times of processing work of the workstation in a preset time range, for example, the number of times of business operations such as warehouse-out, warehouse-in or inventory-checking processed by the workstation respectively. The total amount of work processed by each workstation is the sum of the amount of work processed by each workstation, specifically the sum of the number of times each workstation processes work. The ratio of the work load handled by the workstation to the total work load handled by each workstation is typically taken as the work proportion handled by the workstation. And the working proportion of the workstation is taken as the working capacity of the workstation.
B: in another possible embodiment, since the work ratio values of each work station may be very different, the work ratio values may be smoothed after the work ratio of each work station is obtained. Specifically, the following steps 1 to 4 may be used to determine the working capacity of the workstation.
Step 1, acquiring the workload processed by each workstation in a preset time range.
Here, the workload of each workstation processing is also the number of times the workstation processes the work within a preset time range.
And 2, determining the working proportion of the processing of each workstation according to the working amount of the processing of the workstation and the working total amount of the processing of each workstation.
Likewise, the ratio of the amount of work handled by the workstation to the total amount of work handled by each workstation may be used as the work ratio for the workstation.
And step 3, determining smoothing parameters according to the maximum value and the minimum value in the workload processed by each workstation respectively and preset configuration parameters.
By way of example, the smoothing parameter C may be determined using the following equation (2):
Wherein,
In the formula (2),The minimum of the workload handled separately for each workstation.The smoothing parameter C can be determined according to formula (2) for the maximum of the workload handled separately for each workstation.
In some practical situations, such as a new automated warehouse in use or at a large-scale promotion time, the smoothing parameter C needs to be set. In the case of an automated warehouse newly put into use, the initial values of the smoothing parameters may be empirically set; under the condition of large-scale promotion time, the working capacity of at least one workstation can be artificially increased or reduced by setting the smooth parameters of some workstations, so that the ex-warehouse cost of at least one storage position is changed, and the aim of dispersing the storage positions of cargoes with high ex-warehouse frequency is fulfilled. The work capacity of the work stations can be regulated and controlled by planning in advance and purposefully opening or closing certain work stations, so that the ex-warehouse cost of the storage is affected, and a better storage allocation strategy is formed by manual regulation and control before the large-scale promotion time comes.
And 4, determining the working capacity of the workstation according to the smoothing parameters and the working proportion processed by the workstation.
By way of example, the following equation (3) may be used to determine the workstation's operating capability κ w based on the smoothing parameters and the workstation's operating ratio of processes:
Wherein,
In equation (3), P w is the work ratio of the workstation process, C is the smoothing parameter, κ ' w is the intermediate parameter for the workstation's work capacity κ w, Σκ w ' represents summing the intermediate parameters for each workstation in the automated warehouse.
S205: and obtaining the ex-warehouse cost from the storage position to the workstation according to the distance cost from the storage position to the workstation and the working capacity of the workstation.
Specifically, the working capacity of the workstation may be taken as the weight of the distance cost, and the ex-warehouse cost of the storage location to the workstation is obtained according to the distance cost of the storage location to the workstation and the working capacity of the workstation, and for example, the ex-warehouse cost of the storage location to the workstation may be determined by using the following formula (4):
caw＝κw·law (4)
Where κ w is the working capacity of the workstation, l aw is the distance cost from the storage to the workstation, and c aw is the ex-warehouse cost from the storage to the workstation.
S206: and determining the ex-warehouse cost of the storage according to the ex-warehouse cost of the storage to each workstation.
Specifically, the cost of the storage to the each workstation may be summed to obtain the cost of the storage to the store. By way of example, the ex-warehouse cost of the reservoir may be determined using equation (5) below:
ca＝∑wcaw (5)
Where c aw is the outbound cost of the storage to the workstation, Σ wcaw represents summing the outbound costs of the storage to each workstation, and c a is the outbound cost of the storage.
S207: and determining a storage list according to the ex-warehouse cost of each storage.
Preferably, the storage list shown in fig. 3 may be formed by arranging the storage according to the size of the delivery cost from large to small. Because the storage list considers the working capacity of the workstation, the distance from the storage to the workstation and the congestion information, in practical implementation, the ex-warehouse cost of each storage in a similar area is scattered due to the different working capacities of different workstations, and the ex-warehouse cost of each storage in the congestion area is also scattered due to the addition of the congestion information, so that goods with high ex-warehouse frequency are distributed due to the dispersion of the ex-warehouse cost of the storage and are arranged in the area with a far distance, the condition that the goods with high in-warehouse frequency are stored in the similar area to cause congestion is avoided, the congestion risk is reduced, and the storage efficiency is improved.
Specifically, it may be determined in which form the storage bits are stored in the bit list according to the actually employed data structure, for example, identification information of the storage bits or numbers of the storage bits may be stored in the storage list, or the like.
The storage positions with the same ex-warehouse cost can be ranked randomly, ranked according to the number size of the storage positions, or ranked according to the identification information of the storage positions.
S208: and determining a target storage position from the storage position list according to the frequency of the goods transported by the transfer robot in and out of the warehouse, and taking the target storage position as a running destination of the transfer robot.
Specifically, the following two possible embodiments may be adopted, and the target storage location is determined from the storage location list according to the frequency of the goods transported by the transfer robot going in and out.
A: in a possible embodiment, the target storage position can be determined from the storage position list directly according to the frequency of the goods transported by the transfer robot in and out of the warehouse. Specifically, ranking is performed for the transfer robots according to the frequency of the goods transported by the transfer robots in and out of the warehouse, and ranking information of the transfer robots is determined, wherein the ranking information is usually a numerical value of ranking of the transfer robots. The goods transported by the transport robots can be ranked from big to small according to the frequency of the goods in and out of the warehouse, namely the goods with the largest frequency of the goods in and out of the warehouse, and the transport robots for transporting the goods are ranked at the first position; the goods can be ranked from small to large according to the frequency of the goods transported by the transfer robot, namely, the goods with the smallest frequency of the goods in and out are ranked at the first position by the transfer robot for transporting the goods. The transfer robots corresponding to cargoes with the same frequency in and out of the warehouse can be ranked randomly, ranked according to the number of the transfer robots, or ranked according to the identification information of the transfer robots.
When the storage is arranged from large to small according to the size of the ex-warehouse cost to form a storage list as shown in fig. 3, the storage is ranked from large to small according to the frequency of the in-warehouse delivery of the goods transported by the transfer robot.
And then determining the position of the target storage in the storage list according to the ranking information of the transfer robots and the total number of the transfer robots, and further determining the target storage from the storage list. Assuming that the frequency of the entry and exit of the article carried by a certain carrying robot is ranked at the nth position, the ranking information of the carrying robot is N, and the total number of carrying robots in the automated warehouse is N. The location of the target bin in the bin list is the first bin in the bin listAnd a number of bins, wherein B is the total number of bins contained in the bin list. That is, the first in the bin listThe individual storage locations are target storage locations.
B: in another possible embodiment, since the target storage location determined from the storage location list may be occupied directly according to the frequency of the in-out warehouse of the goods transported by the transfer robot, the following steps 1 and 2 may be used to determine the target storage location from the storage location list:
Step 1, determining more than two alternative storage positions from the storage position list according to the frequency of the goods transported by the transfer robot in and out of the warehouse.
For example, as shown in fig. 3, a preset parameter r, r being a positive integer greater than 1, may be used. Assuming that the ranking information of the transfer robots is N, in the case where the total number of transfer robots in the automated warehouse is N, the position of the target storage in the storage list is determinedThen, according to the preset parameter r, the position in the storage list is at the first positionOr (b)More than two bits of a bit are used as alternative bits.
And step 2, determining any idle storage bit in the alternative storage bits as the target storage bit.
In one possible embodiment, the location that may be in the stored list is at the first Or (b)And selecting one free storage bit as a target storage bit from the alternative storage bits of the bits.
In another possible embodiment, the first of all in the list of bits can also be determinedWhether the candidate storage bits are idle or not, if so, determining the candidate storage bits as target storage bits; if not, judge the firstWhether the candidate storage bits are idle or not, if so, determining the candidate storage bits as target storage bits; if not, continue to judge the firstA plurality of alternative storage locations; if not, continue to judge the firstWhether the storage bit is idle or not until the judgment is madeOr (b)None of the storage bits is idle or until an idle storage bit is found as the target storage bit.
And determining the target storage location as a driving destination of the transfer robot. The target storage location and the travel destination may be redetermined each time the transfer robot completes the work of the workstation, or may be redetermined at predetermined time intervals, for example, once a day, at regular intervals.
Compared with the prior art that the automatic warehouse is divided into different areas, the method provided by the embodiment of the application is more flexible, accurate and refined, the storage scene of the automatic warehouse and the processing capacity of the workstation are considered, and in the practical implementation, the storage positions which are arranged at the front can be distributed in the automatic warehouse in a fan-shaped and dispersed manner, so that the congestion risk of the transfer robot is reduced. The method provided by the embodiment of the application can be calculated according to the real-time data, can be calculated according to the historical data in the preset time range, and can be performed by combining the real-time data and the historical data, so that the working capacity and the congestion information of the real-time and historical workstations are fully considered, and the basis for determining the target storage is richer and more accurate.
Based on the same design concept, the embodiment of the application also provides a device for determining the driving destination and a readable storage medium.
Example III
As shown in fig. 4, a device 400 for determining a driving destination according to an embodiment of the present application includes:
A cost calculation module 401, configured to determine, for at least one storage location, a delivery cost of the storage location according to a distance from the storage location to each workstation, a working capacity of each workstation, and congestion information of the storage location; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; the ex-warehouse cost is the cargo transportation efficiency of the transfer robot taking the storage as a driving destination when the transfer robot is stored in the storage;
a list generation module 402 for determining a list of storage locations based on the cost of the delivery of each of the storage locations,
A destination determining module 403, configured to determine a target storage location from the storage location list according to a frequency of entering and exiting the cargo transported by the transfer robot, and take the target storage location as a driving destination of the transfer robot; and the storage list stores each storage according to the size sequence of the ex-warehouse cost.
In a possible implementation manner, the cost calculation module 401 is configured to:
Determining, for each of the workstations, a distance cost of the storage to the workstation based on the congestion information of the storage, a minimum value of the distance of the storage to each workstation, and the distance of the storage to the workstation;
obtaining the ex-warehouse cost from the storage location to the workstation according to the distance cost from the storage location to the workstation and the working capacity of the workstation;
and determining the ex-warehouse cost of the storage according to the ex-warehouse cost of the storage to each workstation.
In a possible implementation manner, the device 400 for determining a driving destination further includes a working capability determining module 404, configured to:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
For each workstation, determining the working proportion processed by the workstation according to the working amount processed by the workstation and the working total amount processed by each workstation, and taking the working proportion as the working capacity of the workstation.
In a possible implementation, the working capacity determining module 404 is further configured to:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
for each workstation, determining the working proportion of the processing of the workstation according to the working amount of the processing of the workstation and the working total amount of the processing of each workstation;
Determining smoothing parameters according to the maximum value and the minimum value in the workload processed by each workstation respectively and preset configuration parameters;
and determining the working capacity of the workstation according to the smoothing parameters and the working proportion processed by the workstation.
In a possible implementation manner, the device 400 for determining a driving destination further includes a congestion information determining module 405, configured to:
acquiring a distribution diagram of the transfer robot;
Dividing the distribution map into grids with preset sizes, and counting the number of the carrying robots in each grid;
Determining a congestion area from each grid according to the number of the transfer robots;
And determining the congestion information of each storage bit according to the congestion area.
In a possible implementation manner, the congestion information determining module 405 is further configured to:
For each grid, calculating a congestion coefficient according to the number of transfer robots in the grid; the congestion coefficient is the sparseness degree of the transfer robot in the grid;
And if the congestion coefficient of the grid is larger than a preset coefficient threshold value, the grid is the congestion area.
In a possible implementation manner, the device 400 for determining a driving destination further includes a distance determining module 406, configured to: the distance of the storage location to the workstation is determined.
In a possible implementation manner, the destination determining module 403 is configured to:
According to the frequency of the goods transported by the transfer robot in and out of the warehouse, determining more than two alternative storage positions from the storage position list;
And determining any idle storage bit in the alternative storage bits as the target storage bit.
Example IV
A computer readable storage medium storing instructions that when executed by a processor cause the processor to perform the steps of the method provided in embodiment one or embodiment two. In practice, the computer readable medium may be contained in the apparatus/device/system described in the above embodiments or may exist alone without being assembled into the apparatus/device/system. The computer-readable storage medium carries one or more programs which, when executed, implement the steps of the method provided in accordance with the first or second embodiments of the apparatus provided in accordance with the third reference embodiment.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: portable computer diskette, hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the foregoing, but are not intended to limit the scope of the application. In the disclosed embodiments, 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.
The method steps of the present application may be implemented by hardware, such as logic gates, switches, application Specific Integrated Circuits (ASIC), programmable logic controllers, embedded microcontrollers, etc., in addition to data processing programs. Such hardware capable of carrying out the methods of the application may therefore also constitute the application.
Example five
The embodiment of the application also provides an electronic device which can be a computer or a server, wherein the device of the third embodiment of the application can be integrated. As shown in fig. 5, an electronic device 500 according to a third embodiment of the apparatus of the present application is shown.
The electronic device may include one or more processors 501 of a processing core, one or more computer-readable storage media 502. The electronic device may further comprise a power supply 503, an input output unit 504. Those skilled in the art will appreciate that fig. 5 is not intended to be limiting of an electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
Wherein:
The processor 501 is a control part of the electronic device, and the steps of the method provided in the first or second embodiment are performed by running or executing a software program stored in the computer-readable storage medium 502 by connecting the respective parts using various interfaces and lines.
The computer-readable storage medium 502 may be used to store a software program, that is, a program involved in the method provided in the first embodiment or the second embodiment.
The processor 501 executes a software program stored in the computer-readable storage medium 502 to perform various functional applications and data processing. The computer-readable storage medium 502 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function, and the like; the storage data area may store data or the like that is used according to the needs of the electronic device. In addition, computer-readable storage medium 502 can include high-speed random access memory, and can also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the computer-readable storage medium 502 may also include a memory controller to provide the processor 501 with access to the computer-readable storage medium 502.
The electronic device further comprises a power supply 503 for powering the various components, preferably the power supply 503 is logically connected to the processor 501 via a power management system, whereby the functions of managing charging, discharging, and power consumption are performed by the power management system. The power supply 503 may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
The server may also include an input output unit 504, such as may be used to receive input numeric or character information, and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control; such as various graphical user interfaces that may be used to display information entered by or provided to a user and a server, which may be composed of graphics, text, icons, video, and any combination thereof.
The flowcharts 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 disclosure. 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.
Those skilled in the art will appreciate that the features recited in the various embodiments of the application and/or in the claims may be combined in various combinations and/or combinations even if such combinations or combinations are not explicitly recited in the application. In particular, the features recited in the various embodiments of the application and/or in the claims may be combined in various combinations and/or combinations without departing from the spirit and teachings of the application, all of which are within the scope of the disclosure.
The principles and embodiments of the present application have been described in detail in the present application, the above examples are provided to facilitate understanding of the method of the present application and the core ideas thereof, and are not intended to limit the present application. It will be apparent to those skilled in the art that variations can be made in the present embodiments and applications within the spirit and principles of the application, and any modifications, equivalents, improvements, etc. are intended to be included within the scope of the present application.
Claims (9)
1. A method for determining a travel destination, comprising:
Determining, for at least one storage location, a delivery cost of the storage location according to a distance from the storage location to each workstation, a working capacity of each workstation, and congestion information of the storage location; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; the ex-warehouse cost is the cargo transportation efficiency of the transfer robot taking the storage as a driving destination when the transfer robot is stored in the storage;
Determining a storage position list according to the ex-warehouse cost of each storage position, determining a target storage position from the storage position list according to the in-warehouse frequency of cargoes transported by the transfer robot, and taking the target storage position as a driving destination of the transfer robot; wherein, the storage list stores each storage according to the size sequence of the ex-warehouse cost;
Wherein, the step of determining the target storage position from the storage position list according to the frequency of the goods transported by the transfer robot in and out of the warehouse comprises:
according to the frequency of the goods transported by the transfer robot in and out of the warehouse, determining more than two alternative storage positions from the storage position list; determining any idle storage bit in the alternative storage bits as the target storage bit;
Wherein the determining more than two alternative storage bits from the storage bit list comprises:
Place the position in the storage list at the first position More than two storage bits of bits are used as alternative storage bits; wherein r is a preset parameter, is a positive integer greater than 1, and n is the ranking information of the transfer robot, and the ranking information is determined according to the frequency of the transfer robot to transport goods in and out of the warehouse; n is the total number of transfer robots in the automated warehouse; b is the total number of bits contained in the bit list.
2. The method of claim 1, wherein the step of determining the outbound cost of the storage based on the distance of the storage from each workstation, the operational capacity of each workstation, and the congestion information of the storage comprises:
Determining, for each of the workstations, a distance cost of the storage to the workstation based on the congestion information of the storage, a minimum value of the distance of the storage to each workstation, and the distance of the storage to the workstation;
obtaining the ex-warehouse cost from the storage location to the workstation according to the distance cost from the storage location to the workstation and the working capacity of the workstation;
and determining the ex-warehouse cost of the storage according to the ex-warehouse cost of the storage to each workstation.
3. The method of claim 1, wherein prior to the step of determining the outbound cost of the storage based on the distance of the storage from each workstation, the operational capability of each workstation, and the congestion information of the storage, the method further comprises the step of determining the operational capability of a workstation:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
For each workstation, determining the working proportion processed by the workstation according to the working amount processed by the workstation and the working total amount processed by each workstation, and taking the working proportion as the working capacity of the workstation.
4. The method of claim 1, wherein prior to the step of determining the outbound cost of the storage based on the distance of the storage from each workstation, the operational capability of each workstation, and the congestion information of the storage, the method further comprises the step of determining the operational capability of a workstation:
The method comprises the steps of obtaining the workload processed by each workstation respectively in a preset time range;
for each workstation, determining the working proportion of the processing of the workstation according to the working amount of the processing of the workstation and the working total amount of the processing of each workstation;
Determining smoothing parameters according to the maximum value and the minimum value in the workload processed by each workstation respectively and preset configuration parameters;
and determining the working capacity of the workstation according to the smoothing parameters and the working proportion processed by the workstation.
5. The method of claim 1, wherein prior to the step of determining the outbound costs of the storage location based on the distance of the storage location from each workstation, the operational capabilities of each workstation, and the congestion information of the storage location, the method further comprises the step of determining the congestion information of each storage location:
acquiring a distribution diagram of the transfer robot;
Dividing the distribution map into grids with preset sizes, and counting the number of the carrying robots in each grid;
Determining a congestion area from each grid according to the number of the transfer robots;
And determining the congestion information of each storage bit according to the congestion area.
6. The method of claim 5, wherein the step of determining a congestion area from each grid based on the number of transfer robots comprises:
For each grid, calculating a congestion coefficient according to the number of transfer robots in the grid; the congestion coefficient is the sparseness degree of the transfer robot in the grid;
And if the congestion coefficient of the grid is larger than a preset coefficient threshold value, the grid is the congestion area.
7. A travel destination determining apparatus, comprising:
The cost calculation module is used for determining the ex-warehouse cost of the storage according to the distance from the storage to each workstation, the working capacity of each workstation and the congestion information of the storage aiming at least one storage; the working capacity is used for measuring the cargo throughput of the workstation; the congestion information is used for representing the influence of congestion on the travelling path of the transfer robot on the transportation efficiency of the goods in the storage position; the ex-warehouse cost is the cargo transportation efficiency of the transfer robot taking the storage as a driving destination when the transfer robot is stored in the storage;
A list generation module for determining a list of storage locations based on the cost of delivery of each of the storage locations,
The destination determining module is used for determining a target storage position from the storage position list according to the in-out frequency of cargoes transported by the transfer robot, and taking the target storage position as a running destination of the transfer robot; wherein, the storage list stores each storage according to the size sequence of the ex-warehouse cost;
The destination determining module is specifically configured to determine, when determining a target storage location from the storage location list according to the frequency of in-out of the cargoes transported by the transfer robot, more than two alternative storage locations from the storage location list according to the frequency of in-out of the cargoes transported by the transfer robot; determining any idle storage bit in the alternative storage bits as the target storage bit; wherein the determining more than two alternative storage bits from the storage bit list comprises: place the position in the storage list at the first position More than two storage bits of bits are used as alternative storage bits; wherein r is a preset parameter, is a positive integer greater than 1, and n is the ranking information of the transfer robot, and the ranking information is determined according to the frequency of the transfer robot to transport goods in and out of the warehouse; n is the total number of transfer robots in the automated warehouse; b is the total number of bits contained in the bit list.
8. A computer readable storage medium storing computer instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 6.
9. An electronic device comprising the computer-readable storage medium of claim 8, further comprising a processor executable to the computer-readable storage medium.
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| CN113450049B (en) * | 2021-06-02 | 2024-05-24 | 北京迈格威科技有限公司 | A method, device and storage medium for determining a delivery station |
| CN114021991A (en) * | 2021-11-08 | 2022-02-08 | 北京京东乾石科技有限公司 | Warehouse-out container positioning method and device |
| CN114326707B (en) * | 2021-11-30 | 2024-05-10 | 深圳优地科技有限公司 | Movement control method for robot, and computer-readable storage medium |
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