CN113495557A

CN113495557A - Method and device for determining number of target devices

Info

Publication number: CN113495557A
Application number: CN202010260042.4A
Authority: CN
Inventors: 邱小红
Original assignee: Beijing Jingdong Qianshi Technology Co Ltd
Current assignee: Beijing Jingdong Qianshi Technology Co Ltd
Priority date: 2020-04-03
Filing date: 2020-04-03
Publication date: 2021-10-12

Abstract

The invention discloses a method and a device for determining the number of target devices, and relates to the technical field of computers. One embodiment of the method comprises: establishing a simulation model corresponding to the item picking system, wherein the simulation model comprises an undetermined amount of target equipment; changing the number of target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain an operation result of each operation; and determining the optimal number of the target devices according to the operation result. This embodiment enables the calculation of the optimal number of target devices in an item picking system with a high degree of accuracy.

Description

Method and device for determining number of target devices

Technical Field

The invention relates to the technical field of logistics, in particular to a method and a device for determining the number of target devices.

Background

In an Automated warehouse-based item picking system, it is important to accurately calculate the appropriate number of target devices, such as AGVs (Automated Guided vehicles), therein. In the prior art, the number of target devices is generally estimated through the experience of the plan planner itself.

In the process of implementing the invention, the inventor finds that the prior art has at least the following problems:

in the estimation process, a scheme planner generally assumes a plurality of ideal conditions, such as uniformly using an average value and neglecting individual differences, assuming that there is no interference between AGVs, disregarding acceleration and deceleration of AGVs, neglecting a difference between an empty load state and a load state of an AGV, and thus, the accuracy of the estimation result is low, and the method cannot be applied to an actual scheme.

Disclosure of Invention

In view of this, embodiments of the present invention provide a method and an apparatus for determining the number of target devices, which can calculate the optimal number of target devices in an item picking system with a high accuracy.

To achieve the above object, according to one aspect of the present invention, there is provided a method of determining the number of target devices.

The method for determining the number of the target devices is used for determining the optimal number of the target devices contained in the item picking system; the method comprises the following steps: establishing a simulation model corresponding to the item picking system, wherein the simulation model comprises an undetermined amount of target equipment; changing the number of target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain an operation result of each operation; and determining the optimal number of the target devices according to the operation result.

Optionally, establishing a simulation model corresponding to the item picking system includes: establishing the simulation model according to an externally input simulation map and working parameters; the simulation map comprises a plurality of marking points and specifies the type of each marking point and the communication relation among the marking points.

Optionally, determining the optimal number of target devices according to the operation result includes: acquiring index data of the operation in a preset dimension from an operation result of each operation; determining the number of target devices corresponding to the index data meeting the preset requirement as the initial selection number; and determining the minimum value of the initially selected number as the optimal number of the target equipment.

Optionally, the target device comprises a handling unit, and the operating parameters comprise at least one of: basic parameters, target equipment operation parameters, layout parameters and ex-warehouse parameters; wherein the basic parameters include at least one of: the area range related to the simulation model, the size of a grid in the area, the number of other devices except the target device, and the number of buffer bits of each picking workstation; the target device operating parameters include at least one of: the speed, the acceleration, the rotation time, the lifting time and the falling time of the target equipment in the no-load state and the loading state; the layout parameters include at least one of: the number of goods shelves, the number of storage areas and the storage mode of articles; the storage racks are two-sided multi-layer type storage racks, and the number of the storage racks is smaller than the number of the storage positions determined by the simulation map; each storage area comprises a plurality of storage positions, and different storage areas are divided according to the average distance from the storage positions to the picking workstation; the ex-warehouse parameters comprise at least one of the following: the ratio of ex-warehouse frequency of each storage area, the single-side picking quantity interval of the shelves, the single-piece article picking time of the picking workstation, the face changing probability of the shelves, the picking probability of two sides of the shelves and the ex-warehouse probability of the shelves in the warehouse-returning process.

Optionally, the method further comprises: determining a scheduling rule of each target device before running a simulation model corresponding to the number of the target devices; running a simulation model corresponding to the number of each target device, comprising: and operating the simulation model according to the scheduling rule.

Optionally, the dimensions include at least one of: average utilization rate of the conveying units, average picking amount per unit time of the picking work stations in a stable state and average picking total amount of the picking work stations; the target device is a handling unit or a picking station.

To achieve the above object, according to another aspect of the present invention, there is provided an apparatus for determining the number of target devices.

The device for determining the number of the target devices is used for determining the optimal number of the target devices contained in the item picking system; the apparatus may include: the modeling unit is used for establishing a simulation model corresponding to the item picking system, and the simulation model comprises an undetermined number of target devices; the operation unit is used for changing the number of the target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain the operation result of each operation; and the optimal quantity calculation unit is used for determining the optimal quantity of the target equipment according to the operation result.

Optionally, the modeling unit may be further operable to: establishing the simulation model according to an externally input simulation map and working parameters; the simulation map comprises a plurality of marking points and specifies the type of each marking point and the communication relation among the marking points; the optimum number calculation unit may be further operable to: acquiring index data of the operation in a preset dimension from an operation result of each operation; determining the number of target devices corresponding to the index data meeting the preset requirement as the initial selection number; and determining the minimum value of the initially selected number as the optimal number of the target equipment.

To achieve the above object, according to still another aspect of the present invention, there is provided an electronic apparatus.

An electronic device 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 number of the target devices provided by the invention.

To achieve the above object, according to still another aspect of the present invention, there is provided a computer-readable storage medium.

A computer-readable storage medium of the present invention has stored thereon a computer program which, when executed by a processor, implements the method of determining the number of target devices provided by the present invention.

According to the technical scheme of the invention, one embodiment of the invention has the following advantages or beneficial effects: firstly, establishing a simulation model corresponding to a real article picking system according to an externally input simulation map and various working parameters, then changing the number of target devices in the simulation model for multiple times, operating the simulation model corresponding to the number of each target device to obtain multiple operation results, and finally selecting the minimum value from the number of the target devices corresponding to the operation results meeting the requirements as the optimal number of the target devices. Because the simulation map and the working parameters are determined by referring to a real article picking system, the established simulation model has higher similarity with the real article picking system, and target devices (such as AGV) in the simulation model have the condition of mutual interference as the real scene; the working parameters use various individualized difference values (such as the number of buffer bits of a picking workstation, the warehouse-out frequency ratio of a storage area and the like), the acceleration and the deceleration of target equipment (such as an AGV) are considered, and the acceleration and the deceleration are respectively input according to an idle load state and a loading state. Through the arrangement, the simulation model which is extremely similar to a real scene can be established, and a credible operation result can be obtained by operating the simulation model, so that the finally calculated optimal quantity of the target equipment has higher accuracy and practical value.

Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.

Drawings

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 diagram illustrating the main steps of a method for determining the number of target devices according to an embodiment of the present invention;

FIG. 2 is a schematic flowchart illustrating a method for determining the number of target devices according to an embodiment of the present invention;

FIG. 3 is a first schematic diagram of a simulation model layout according to a first embodiment of the present invention;

FIG. 4 is a second schematic diagram of a layout of a simulation model according to the first embodiment of the present invention;

FIG. 5 is a schematic diagram of a portion of an apparatus for determining the number of target devices in an embodiment of the present invention;

FIG. 6 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;

fig. 7 is a schematic structural diagram of an electronic device for implementing the method for determining the number of target devices in the embodiment of the present 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.

It should be noted that the embodiments of the present invention and the technical features of the embodiments may be combined with each other without conflict.

Fig. 1 is a schematic diagram of the main steps of a method for determining the number of target devices according to an embodiment of the present invention.

The method for determining the number of target devices of the embodiment of the invention can be used for determining the optimal number of target devices contained in an item picking system. Wherein the item picking system is based on the real environment of an automated warehouse. In the technical field of logistics, in order to effectively improve the logistics efficiency, more and more service parties adopt an automatic warehouse to produce orders. In an automated warehouse, the server allocates orders received in the order pool to appropriate picking stations, controls a handling unit (e.g., Automated Guided Vehicles (AGVs), shuttles, etc.) to move items on shelves to the picking stations, and finally picks the completed orders out of the warehouse via a human or picking robot. In practical application, the goods shelf is arranged on a storage position of the automatic warehouse. For example, the target device may be a conveying unit or a picking workstation in an item picking system, and in the following description, the target device is mainly described as a conveying unit.

As shown in fig. 1, the method for determining the number of target devices according to the embodiment of the present invention may be specifically performed according to the following steps:

step S101: and establishing a simulation model corresponding to the item picking system, wherein the simulation model comprises an undetermined number of target devices.

In this step, existing logistics system simulation software may be utilized to build a corresponding simulation model against the actual item picking system. It is understood that the simulation model may have a spatial layout, a moving path, and specific equipment such as storage positions, shelves, carrying units, picking workstations, etc. similar to the above item picking system, and may also have details such as obstacles, charging piles, etc. In a specific application, the relevant parameters of the above contents in the simulation model can be determined according to external input (such as user input). It should be noted that, the simulation model of the embodiment of the present invention is used for determining the optimal number of target devices by performing tests on a given plurality of numbers of target devices, and therefore, a specific number of target devices does not need to be determined in this step.

In the embodiment of the present invention, the method for establishing the simulation model may be: firstly, receiving an externally input simulation map and working parameters through a corresponding interface of logistics system simulation software, and then establishing a simulation model according to the received simulation map and the working parameters. The simulation map is a data set for simulating the spatial layout of the item picking system, and the simulation map may include a plurality of marker points (the marker points are basic elements constituting the simulation map) and specify the type of each marker point and the connectivity between the marker points. Exemplary types of marking points are shelf rotation points (this concept will be explained below), path points, obstacle points, storage points, picking station corresponding points, picking station buffer position corresponding points, and the like. The following table shows a part of data included in a certain simulation map.

Wherein, 1, 2 of the mark point types can represent two different mark point types, for example, 1 represents a path point, and 2 represents an obstacle point. Each mark point is marked by its horizontal and vertical coordinates, any adjacent point is expressed in the form of horizontal and vertical coordinates, and several adjacent points are separated by marks.

In a specific application, the above-mentioned operating parameter may be any type of data, and is not limited to a numerical value. As a preferred aspect, the operating parameters may include at least one of: basic parameters, target equipment operation parameters, layout parameters and ex-warehouse parameters. The target equipment operation parameters refer to relevant parameters of the target equipment in the operation process; layout parameters refer to parameters related to spatial layout and item storage; the ex-warehouse parameters refer to parameters related to the ex-warehouse of the articles; the basic parameters refer to parameters other than the above parameters that need to be set to run the simulation model. Typically, the logistics system simulation software has a corresponding interface for inputting the above parameters.

In the embodiment of the invention, before the simulation model is operated, an externally input dispatching rule of the conveying unit can be received through a corresponding interface of the logistics system simulation software, and the dispatching rule is used for determining the selection logic of the shelves and the conveying units and the corresponding relation between the shelves and the picking work stations in each article conveying process.

Step S102: and changing the number of the target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain an operation result of each operation.

In this step, different numbers of target devices may be input into the simulation model established in step S101, and the simulation model may be run once after each number is input, so as to obtain a running result of each running, and the optimal number of target devices may be determined by comparing the running results of each running. In a specific application, the carrying units in the simulation model can be scheduled according to an externally input scheduling rule in the process of running the simulation model. The user can observe whether the operation condition is consistent with the plan in real time in the operation process of the simulation model, and correspondingly adjust the operation condition in the follow-up process. In addition, the operation time length of each operation can be set to be equal or unequal according to requirements.

For example, the operation result may include time ratios of each of the conveying devices in an idle state, an idle operation state, a loading operation state and a picking state, time ratios of each of the picking stations in an idle state and a picking state, a picking amount of each of the picking stations in a unit time, a total picking amount, and the like.

Step S103: and determining the optimal number of the target devices according to the operation result.

Preferably, in this step, the index data of each operation in the preset dimension may be obtained from the operation result of each operation. Illustratively, the dimensions may include at least one of: average utilization rate of conveying units, average picking amount per unit time of picking work stations in a steady state and average picking total amount of the picking work stations. Thereafter, the number of target devices corresponding to the index data meeting the preset requirement (i.e., the number of target devices in a certain operation process corresponding to the index data meeting the preset requirement) may be determined as the initial selection number. It can be understood that the preset requirement may correspond to an actual ex-warehouse requirement, which may be quantified as a lower threshold value of a preset dimension, and that meeting the preset requirement means that the index data needs to be greater than or equal to the lower threshold value of each preset dimension; the preliminary selected quantity that meets the preset requirements may be considered a qualified target product quantity. And finally, determining the minimum value of the initially selected number as the optimal number of the target equipment. Obviously, on the premise of meeting the preset requirement, the minimum initial selection number can save the usage amount of the target equipment and avoid resource waste, and therefore the minimum value in the initial selection numbers is the optimal number of the target equipment. It should be noted that, if there is only one primary selection number, the minimum value is the primary selection number.

It will be appreciated that when the predetermined dimension includes the average picking amount per unit time of the picking station in a steady state, the running time of each operation is required to ensure that each picking station can reach the steady state (i.e. the picking amount is more steady); when the predetermined dimension includes an average value of the total picking amounts of the picking stations, the operation time length of each operation needs to be set to be equal.

Fig. 2 is a schematic specific flowchart of a method for determining the number of target devices in the embodiment of the present invention. As shown in fig. 2, the method for determining the number of target devices according to the embodiment of the present invention may specifically perform the following steps:

step S201: and selecting a new model option in the logistics system simulation software, and starting to establish a simulation model. Step S202: and inputting basic parameters and target equipment operation parameters on a corresponding interface of the logistics system simulation software. Step S203: and receiving the simulated map imported by the user. Step S204: the layout corresponding to the simulation map is generated, and the simulation map is a data set, so the simulation map needs to be intuitively displayed on a model interface of the logistics system simulation software in the form of the layout. Step S205: and receiving the layout parameters input by a user at a corresponding interface of the logistics system simulation software. Step S206: and receiving the ex-warehouse parameters input by the user on the corresponding interface. Generally, after all basic parameters, target device operation parameters, layout parameters and ex-warehouse parameters are determined, the simulation model can be considered to be successfully created. Step S207: the number of target devices is set in the simulation model. Step S208: and operating the simulation model to obtain an operation result, and calculating index data of a preset dimension according to the operation result. Step S209: judging whether the index data meet the preset requirements: if yes, executing the next step; otherwise, the process returns to step S207. Step S210: and determining the number of the target devices corresponding to the operation as the initial selection number. Step S211: judging whether the number of the currently determined initial selection number reaches a preset threshold value, wherein the threshold value is an integer greater than 1: if yes, executing the next step; otherwise, the process returns to step S207. Step S212: and determining the minimum value in the initial selection number as the optimal number of the target equipment.

Through the steps, the optimal number of the target devices in the item picking system can be determined quickly and accurately based on simulation, and reliable data support can be provided for scheme planning through operation of the simulation model.

The method for determining the number of target devices according to the first embodiment of the present invention is described below with reference to specific application scenarios. The first embodiment applies to an automated warehouse-based item-to-person picking system that includes a storage location, a rack, and a picking station, the rack being placed on the storage location. The goods shelf is a two-sided multi-layer goods shelf, namely, one goods shelf is provided with a plurality of layers, each layer is provided with two sides, and each side of each layer can be used for placing articles. When the goods need to be taken out of the warehouse, the server can select the shelf on which the corresponding goods are placed, and the shelf is transported to the corresponding picking work station through the carrying unit. If the picking personnel at the picking workstation can not directly operate the facing surface of the shelf, the carrying unit selects a shelf rotating point to complete the surface changing action of the shelf in the transportation process. After the shelves arrive at the picking station, the picking personnel pick the required items from the shelves, and the carrying units generally transport the shelves to the original storage positions for storage after the picking is completed. FIG. 3 is a first schematic diagram of a simulation model layout in a first embodiment of the invention, showing a two-dimensional spatial layout of the simulation model and illustrating a storage bay 301, a picking workstation 302, and a shelf rotation point 303. The arrows in fig. 2 are used to indicate the direction in which the handling unit can be moved.

In the present embodiment, the user may perform the following steps to determine the number of target apparatuses, which will be described below by taking the target apparatus as a carrying unit as an example. Firstly, a simulation model of the article picking system is established by taking logistics system simulation software FlexSim as a platform. It is understood that other logistics system simulation software can be used in practical applications, and is not limited to FlexSim. The FlexSim's main interfaces are menu bar, tool bar, model interface and control interface. The menu bar can be used for creating or storing the model, the tool bar can be used for setting the operation time of the model and controlling the operation of the model, the model interface is used for displaying the established model, and a user can observe whether the model layout is correct and whether the model operation process is consistent with the plan through the model interface. The control interface comprises three tabs of input, strategy and output, wherein the input tab is used for inputting basic parameters and target equipment operation parameters, the strategy tab is used for inputting layout parameters and ex-warehouse parameters, and the output tab is used for displaying or outputting operation results and index data of each operation.

In the second step, the user sets the area range related to the simulation model, the grid size in the area and the number of picking workstations on the input tabs of the control interface, and the parameters belong to basic parameters. The area range may be determined by combining an abscissa range and an ordinate range, and the size of the grid represents the minimum step length in the horizontal axis direction and the vertical axis direction. For example, the grid size may be set to 1 meter in the horizontal axis direction and 1 meter in the vertical axis direction. In this step, it is also possible to set a maximum number of handling devices and a maximum number of picking stations.

Thirdly, the user can set the operation parameters of the target equipment at the input tab, wherein the operation parameters comprise at least one of the following parameters: the speed, the acceleration, the rotation time, the lifting time and the falling time of the target equipment in the no-load state and the loading state. The acceleration includes an acceleration during acceleration (in this case, the acceleration is a positive value) and an acceleration during deceleration (in this case, the acceleration is a negative value). One example of the settings for the target device operating parameters may be shown in the following two tables:

the shelf rotation time in the above table is a basic parameter, and may be set in this step.

And fourthly, the user can import the simulation map according to the corresponding path, and when the pop-up map import completion window shows that the simulation map is successfully imported. After importing the simulation map, the model interface may display a corresponding layout map, and perform a related operation on the layout map, that is, a window for setting the number of buffer bits for the picking workstation may appear, and at this time, an appropriate number of buffer bits may be set for each workstation.

Fifthly, the user can set various layout parameters and ex-warehouse parameters on the strategy tab of the control interface. The layout parameters may include at least one of: number of shelves, number of storage areas, and storage manner of articles. The number of shelves set by the user at this location should be less than the number of bays defined by the simulated map. A bin is a collection of bins, each bin being divided according to the average distance of the bin from the picking station. The article storage mode may include a sequential mode and a random mode, the sequential mode refers to storing the articles according to the sequence of the storage position numbers corresponding to the articles from small to large or from large to small, and the random mode refers to randomly generating the storage position numbers to store the corresponding articles. The layout diagram formed by inputting layout parameters is shown in fig. 4, shelves 401 and picking workstations 302 are distributed in different storage areas, and the shelves in different storage areas are distinguished by different gray scales.

The ex-warehouse parameters comprise at least one of the following: the ratio of ex-warehouse frequency of each storage area, the single-side picking quantity interval of the shelves, the single-piece article picking time of the picking workstation, the face changing probability of the shelves, the picking probability of two sides of the shelves and the ex-warehouse probability of the shelves in the warehouse-returning process. The ratio of the warehouse-out frequency of a certain storage area refers to the percentage of the warehouse-out frequency of the articles in the storage area to the total warehouse-out number of the articles in all the storage areas, and the single-side picking number interval of the goods shelf comprises the single-side minimum picking number and the single-side maximum picking number. The single item picking time of the picking workstation may be set to a constant or may be set to a distribution function that varies with item type, and the user may also set the time it takes for the picking workstation to pick multiple items. The shelf changing refers to that the shelf rotates 180 degrees in one transportation process to change the plane, the two-side picking of the shelf refers to that the shelf needs to pick articles on two sides when reaching a certain work station, and the shelf transferring out of the shelf during returning to the storage refers to that the picked shelf is transferred to the picking work station during returning to the original storage position (not reaching the original storage position) to carry out the next picking. The following two tables show the setting situation of the ex-warehouse parameters.

	Ratio of delivery frequency
		The first storage area	40
The second storage area	25
		The third storage area	20
The fourth storage area	15

Minimum number of pickers on single side of goods shelf	3
		Maximum number of pickers on single side of shelf	7
Sorting time (seconds)	10
		Shelf plane change probability (%)	40
Probability of picking on both sides of shelf (%)	33
		Probability of ex-warehouse in shelf warehouse-back trip (%)	22

Through the steps, the user can input the simulation map and various working parameters, and establish the simulation model on the basis. After that, the user may further input a scheduling rule of the target device in the simulation model, where the scheduling rule is used to determine the selection logic of the shelves and the conveying units and the corresponding relationship between the shelves and the picking workstations during each article conveying process, so that the target device may be scheduled during the operation of the simulation model. Illustratively, the scheduling rule may be: in the process of delivering an article out of a warehouse at a certain time, the article required by the picking workstation A is a, the article required by the picking workstation B adjacent to the picking workstation A is B, at the moment, the storage area where the goods shelf is located is determined according to the delivery frequency ratio of each storage area, then the goods shelf with the goods shelf a and the goods shelf B placed at the same time is selected, and the carrying equipment which is closest to the goods shelf and is in an idle state is selected to carry the goods shelf to the A or the B.

Sixthly, the user gives the number of target devices in the simulation model for a plurality of times and runs the simulation model corresponding to each number of the target devices through corresponding buttons in the toolbar. During the model operation process, a user can observe whether the operation condition is consistent with the pre-planning in the model interface. After the operation is finished, the user can observe the operation result of each operation in the output tab of the control interface.

As a preferable scheme, the operation result of each operation may include the following four aspects. The first aspect is the time ratio of each handling unit in various states, which may include: idle state, idle running state, load running state, picking state, busy state (when the handling unit is lifting, descending, car body rotating or executing rack rotating), stop state (when the handling unit waits for rack rotating at rack rotating point), first waiting state (when the handling unit waits at picking station buffer position), second waiting state (when the handling unit waits at non-picking station buffer position). The utilization rate of each transport unit (for example, the time ratio of the non-idle state may be determined as the utilization rate) may be calculated according to the operation result of the first aspect, and then the average utilization rate of all transport units (i.e., the arithmetic average of the utilization rates of each transport unit) may be obtained.

The result of the operation of the second aspect is a time proportion of each picking station in various states, which may include an idle state and a picking state. The result of the operation according to the second aspect may be calculated to obtain a utilization rate for each picking station (for example, the time ratio of the picking state may be determined as the utilization rate), and thus an average utilization rate (i.e. an arithmetic average of the utilization rates for each picking station) for all picking stations.

The result of the operation of the third aspect is the amount picked (i.e., the number of items picked) per unit time (e.g., 1 hour) per picking station. Generally, for any picking station, the picking amount of the simulation model is small at the initial stage of operation, and the picking amount of the simulation model reaches a steady state after the simulation model operates for a period of time (for example, the fluctuation of the picking amount per unit time of continuous hours is less than a preset amount), and the picking station can be considered to reach a steady state. The result of the operation according to the third aspect enables the picking quantity per unit time of each picking station in the steady state to be calculated, and the average picking quantity per unit time of all picking stations in the steady state (i.e. the arithmetic mean of the picking quantities per unit time of each picking station in the steady state) to be determined. It will be appreciated that when using the indicator of the average picking capacity per unit time for picking stations at steady state, a reasonable length of simulation run time needs to be set so that each picking station can reach steady state during each run.

The result of the operation of the fourth aspect is the total picking quantity of each picking station during the operation of the simulation model, from which the average of the total picking quantities of all picking stations can be determined, i.e. the arithmetic average of the total picking quantities of each picking station. It will be appreciated that when using the indicator of the average total picking amount at the picking station, the running time of each run needs to be set equal, otherwise the indicator does not reflect the actual running condition.

Seventh, index data of each operation in a preset dimension is calculated according to the operation result of each operation, and the dimensions can be one or more of the average utilization rate of the conveying units, the average utilization rate of the picking work stations, the average picking amount per unit time of the picking work stations in a steady state, and the average value of the picking total amount of the picking work stations, which are already described above. The index data may be displayed on an input tab of the control interface. And then, judging which operation process index data meets the preset requirements, wherein the preset requirements can include lower threshold values of three dimensions, namely, the average utilization rate of the conveying units, the average picking amount per unit time of the picking work stations in a stable state and the average picking total amount of the picking work stations, namely, the condition that the operation index data meets the preset requirements is equivalent to the condition that the operation index data in the three dimensions is respectively greater than or equal to the corresponding lower threshold values. And finally, taking the number of the target devices corresponding to the running process meeting the preset requirement as the initial selection number, and determining the minimum value in the initial selection number as the optimal number of the target devices. It is understood that, if the number of the currently determined initial selection numbers is smaller than the preset threshold, the number of one or more target devices in the simulation model can be given continuously, and the above sixth step and the seventh step can be executed repeatedly until the desired number of initial selection numbers is obtained.

Through the steps, the optimal number of the target devices can be accurately and quickly determined. It will be appreciated that the method of determining the number of target devices provided by the embodiments of the present invention may be applied to any item picking system and is not limited to the system according to the first embodiment. Meanwhile, the setting sequence of various working parameters can be flexibly specified according to actual requirements and requirements of simulation software, and is not limited to the sequence in the steps.

In the technical scheme of the embodiment of the invention, firstly, a simulation model corresponding to a real article picking system is established according to an externally input simulation map and various working parameters, then the number of target devices in the simulation model is changed for multiple times, the simulation model corresponding to the number of each target device is operated to obtain multiple operation results, and finally, the minimum value is selected from the number of the target devices corresponding to the operation results meeting the requirements to serve as the optimal number of the target devices. Because the simulation map and the working parameters are determined by referring to a real article picking system, the established simulation model has higher similarity with the real article picking system, and target devices in the simulation model have the same mutual interference condition as a real scene; the working parameters use various individualized difference values, the acceleration and the deceleration of the target equipment are considered, and the difference values are respectively input according to the no-load state and the loading state. Through the arrangement, the simulation model which is very similar to a real scene can be established, and a credible operation result can be obtained by operating the simulation model, so that the finally calculated optimal number of the target equipment has higher accuracy and practical value, and meanwhile, reliable data support is provided for scheme planning.

It should be noted that, for the convenience of description, the foregoing method embodiments are described as a series of acts, but those skilled in the art will appreciate that the present invention is not limited by the order of acts described, and that some steps may in fact be performed in other orders or concurrently. Moreover, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no acts or modules are necessarily required to implement the invention.

To facilitate a better implementation of the above-described aspects of embodiments of the present invention, the following also provides relevant means for implementing the above-described aspects.

Referring to fig. 5, an apparatus 500 for determining the number of target devices according to an embodiment of the present invention is used to determine an optimal number of target devices included in an item picking system, which may include: a modeling unit 501, an execution unit 502, and an optimal number calculation unit 503.

The modeling unit 501 may be configured to establish a simulation model corresponding to the item picking system, where the simulation model includes an indefinite number of target devices; the running unit 502 may be configured to change the number of target devices in the simulation model for multiple times and run the simulation model corresponding to the number of each target device, so as to obtain a running result of each running; the optimal number calculation unit 503 may be configured to determine the optimal number of target devices according to the operation result.

In this embodiment of the present invention, the modeling unit 501 may further be configured to: establishing the simulation model according to an externally input simulation map and working parameters; the simulation map comprises a plurality of marking points and specifies the type of each marking point and the communication relation among the marking points; the optimal number calculation unit 503 is further operable to: acquiring index data of the operation in a preset dimension from an operation result of each operation; determining the number of target devices corresponding to the index data meeting the preset requirement as the initial selection number; and determining the minimum value of the initially selected number as the optimal number of the target equipment.

As a preferred aspect, the target device may include a handling unit, and the operating parameter may include at least one of: basic parameters, target equipment operation parameters, layout parameters and ex-warehouse parameters; wherein the basic parameters may include at least one of: the area range related to the simulation model, the size of a grid in the area, the number of other devices except the target device, and the number of buffer bits of each picking workstation; the target device operating parameters may include at least one of: the speed, the acceleration, the rotation time, the lifting time and the falling time of the target equipment in the no-load state and the loading state; the layout parameters may include at least one of: the number of goods shelves, the number of storage areas and the storage mode of articles; the storage racks are two-sided multi-layer type storage racks, and the number of the storage racks is smaller than the number of the storage positions determined by the simulation map; each storage area comprises a plurality of storage positions, and different storage areas are divided according to the average distance from the storage positions to the picking workstation; the ex-warehouse parameters may include at least one of: the ratio of ex-warehouse frequency of each storage area, the single-side picking quantity interval of the shelves, the single-piece article picking time of the picking workstation, the face changing probability of the shelves, the picking probability of two sides of the shelves and the ex-warehouse probability of the shelves in the warehouse-returning process.

Preferably, in the embodiment of the present invention, the modeling unit 501 is further configured to: determining a scheduling rule of each target device before running a simulation model corresponding to the number of the target devices; the running unit 502 may also be configured to run the simulation model according to the scheduling rules.

Furthermore, in an embodiment of the present invention, the dimension includes at least one of: average utilization rate of the conveying units, average picking amount per unit time of the picking work stations in a stable state and average picking total amount of the picking work stations; the target device is a handling unit or a picking station.

In the technical scheme of the embodiment of the invention, firstly, a simulation model corresponding to a real article picking system is established according to an externally input simulation map and various working parameters, then the number of target devices in the simulation model is changed for multiple times, the simulation model corresponding to the number of each target device is operated to obtain multiple operation results, and finally, the minimum value is selected from the number of the target devices corresponding to the operation results meeting the requirements to serve as the optimal number of the target devices. Because the simulation map and the working parameters are determined by referring to a real article picking system, the established simulation model has higher similarity with the real article picking system, and target devices in the simulation model have the same mutual interference condition as a real scene; the working parameters use various individualized difference values, the acceleration and the deceleration of the target equipment are considered, and the difference values are respectively input according to the no-load state and the loading state. Through the arrangement, the simulation model which is extremely similar to a real scene can be established, and a credible operation result can be obtained by operating the simulation model, so that the finally calculated optimal quantity of the target equipment has higher accuracy and practical value.

Fig. 6 illustrates an exemplary system architecture 600 of a method for determining the number of target devices or an apparatus for determining the number of target devices to which embodiments of the present invention may be applied.

As shown in fig. 6, the system architecture 600 may include

terminal devices

601, 602, 603, a network 604 and a server 605 (this architecture is merely an example, and the components included in a specific architecture may be adjusted according to the specific application). The network 604 serves to provide a medium for communication links between the

terminal devices

601, 602, 603 and the server 605. Network 604 may include various types of connections, such as wire, wireless communication links, or fiber optic cables.

A user may use the

terminal devices

601, 602, 603 to interact with the server 605 via the network 604 to receive or send messages or the like. Various client applications may be installed on the

terminal devices

601, 602, 603, such as an application that determines the number of target devices (for example only).

The

terminal devices

601, 602, 603 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 605 may be a server that provides various services, such as a calculation server (for example only) that provides support for applications that determine the number of target devices operated by users with the

terminal devices

601, 602, 603. The calculation server may process the received calculation request and feed back the processing result (e.g. the calculated optimal number of target devices — just an example) to the

terminal device

601, 602, 603.

It should be noted that the method for determining the number of target devices provided by the embodiment of the present invention is generally executed by the server 605, and accordingly, the apparatus for determining the number of target devices is generally disposed in the server 605.

It should be understood that the number of terminal devices, networks, and servers in fig. 6 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

The invention also provides the electronic equipment. The electronic device of the embodiment of the invention comprises: 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 number of the target devices provided by the invention.

Referring now to FIG. 7, shown is a block diagram of a computer system 700 suitable for use with the electronic device implementing an embodiment of the present invention. The electronic device shown in fig. 7 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. 7, the computer system 700 includes a Central Processing Unit (CPU)701, which can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for the operation of the computer system 700 are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input/output (I/O) interface 705 is also connected to bus 704.

The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.

In particular, the processes described in the main step diagrams above may be implemented as computer software programs, according to embodiments of the present disclosure. For example, embodiments of the invention 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 main step diagram. In the above-described embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the central processing unit 701, performs the above-described functions defined in the system of the present invention.

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, 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 units described in the embodiments of the present invention may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a modeling unit, an execution unit, and an optimal number calculation unit. Where the names of these units do not in some cases constitute a limitation of the unit itself, for example, a modeling unit may also be described as a "unit providing a simulation model to a runtime unit".

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 the apparatus, cause the apparatus to perform steps comprising: establishing a simulation model corresponding to the item picking system, wherein the simulation model comprises an undetermined amount of target equipment; changing the number of target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain an operation result of each operation; and determining the optimal number of the target devices according to the operation result.

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

1. A method of determining a number of target devices for determining an optimal number of target devices included in an item picking system; characterized in that the method comprises:

establishing a simulation model corresponding to the item picking system, wherein the simulation model comprises an undetermined amount of target equipment;

changing the number of target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain an operation result of each operation;

and determining the optimal number of the target devices according to the operation result.

2. The method of claim 1, wherein establishing a corresponding simulation model for the item picking system comprises:

establishing the simulation model according to an externally input simulation map and working parameters; the simulation map comprises a plurality of marking points and specifies the type of each marking point and the communication relation among the marking points.

3. The method of claim 1, wherein determining the optimal number of target devices based on the operational results comprises:

acquiring index data of the operation in a preset dimension from an operation result of each operation;

determining the number of target devices corresponding to the index data meeting the preset requirement as the initial selection number;

and determining the minimum value of the initially selected number as the optimal number of the target equipment.

4. The method of claim 2, wherein the target equipment comprises a handling unit, and the operating parameters comprise at least one of: basic parameters, target equipment operation parameters, layout parameters and ex-warehouse parameters; wherein the content of the first and second substances,

the basic parameters include at least one of: the area range related to the simulation model, the size of a grid in the area, the number of other devices except the target device, and the number of buffer bits of each picking workstation;

the target device operating parameters include at least one of: the speed, the acceleration, the rotation time, the lifting time and the falling time of the target equipment in the no-load state and the loading state;

the layout parameters include at least one of: the number of goods shelves, the number of storage areas and the storage mode of articles; the storage racks are two-sided multi-layer type storage racks, and the number of the storage racks is smaller than the number of the storage positions determined by the simulation map; each storage area comprises a plurality of storage positions, and different storage areas are divided according to the average distance from the storage positions to the picking workstation;

the ex-warehouse parameters comprise at least one of the following: the ratio of ex-warehouse frequency of each storage area, the single-side picking quantity interval of the shelves, the single-piece article picking time of the picking workstation, the face changing probability of the shelves, the picking probability of two sides of the shelves and the ex-warehouse probability of the shelves in the warehouse-returning process.

5. The method of claim 4,

the method further comprises the following steps: determining a scheduling rule of each target device before running a simulation model corresponding to the number of the target devices;

running a simulation model corresponding to the number of each target device, comprising: and operating the simulation model according to the scheduling rule.

6. The method of claim 3,

the dimensions include at least one of: average utilization rate of the conveying units, average picking amount per unit time of the picking work stations in a stable state and average picking total amount of the picking work stations;

the target device is a handling unit or a picking station.

7. An apparatus for determining a number of target devices for determining an optimal number of target devices included in an item picking system; characterized in that the device comprises:

the modeling unit is used for establishing a simulation model corresponding to the item picking system, and the simulation model comprises an undetermined number of target devices;

the operation unit is used for changing the number of the target devices in the simulation model for multiple times and operating the simulation model corresponding to the number of each target device to obtain the operation result of each operation;

and the optimal quantity calculation unit is used for determining the optimal quantity of the target equipment according to the operation result.

8. The apparatus of claim 7,

the modeling unit is further configured to: establishing the simulation model according to an externally input simulation map and working parameters; the simulation map comprises a plurality of marking points and specifies the type of each marking point and the communication relation among the marking points;

the optimum number calculation unit is further configured to: acquiring index data of the operation in a preset dimension from an operation result of each operation; determining the number of target devices corresponding to the index data meeting the preset requirement as the initial selection number; and determining the minimum value of the initially selected number as the optimal number of the target equipment.

9. 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 1-6.

10. A computer-readable storage 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 1-6.