CN113313440A

CN113313440A - Storage data processing method and device, computing equipment and medium

Info

Publication number: CN113313440A
Application number: CN202010126164.4A
Authority: CN
Inventors: 蔡爽
Original assignee: Beijing Jingbangda Trade Co Ltd; Beijing Jingdong Zhenshi Information Technology Co Ltd
Current assignee: Beijing Jingbangda Trade Co Ltd; Beijing Jingdong Zhenshi Information Technology Co Ltd
Priority date: 2020-02-27
Filing date: 2020-02-27
Publication date: 2021-08-27

Abstract

The utility model provides a warehouse data processing method, which comprises the steps of obtaining a plurality of goods sets, goods parameters of each goods set in the goods sets and a warehouse parameter constraint range, wherein the goods sets comprise at least one kind of goods; determining a planning model according to the goods parameters and the constraint range of the warehouse parameters; and determining warehouse parameters according to the planning model. The disclosure also provides a storage data processing device, a computing device and a medium.

Description

Storage data processing method and device, computing equipment and medium

Technical Field

The present disclosure relates to the field of computer technologies/internet technologies/electronic technologies, and more particularly, to a method and an apparatus for processing storage data, a computing device, and a medium.

Background

In an actual warehouse sort operation, goods are stored in two areas: a goods picking area and a goods storing area. The goods shelves in the goods picking area are usually small, and the occupied area for storing the same quantity of goods is large; goods in the stock area need to be transported to the goods picking area by a forklift and then picked, and if a large number of goods need to be taken out of the stock area every day, forklift personnel and forklift configuration are additionally added. Therefore, how to distribute the goods in the storage area and the picking area is the key of the warehouse layout.

In the process of implementing the disclosed concept, the related art relies on manual experience to plan the layout of the warehouse, and the inventor finds that the method is highly subjective and the goods distribution in the picking area and the stock area of the warehouse is unreasonable.

Disclosure of Invention

In view of the above, the present disclosure provides a method, an apparatus, a computing device and a medium for processing storage data.

One aspect of the present disclosure provides a method for processing storage data, including: acquiring a plurality of goods sets, goods parameters of each goods set in the goods sets and warehouse parameter constraint ranges, wherein the goods sets comprise at least one kind of goods; determining a planning model according to the goods parameters and the constraint range of the warehouse parameters; and determining warehouse parameters according to the planning model.

According to an embodiment of the present disclosure, the cargo parameter comprises at least one of the following parameters: cargo volume, cargo inventory, and cargo sales.

According to an embodiment of the present disclosure, the warehouse parameter constraint range comprises at least one of the following ranges: inventory quantity constraints, pick-up area constraints, inventory area constraints, and personnel quantity constraints.

According to an embodiment of the present disclosure, the warehouse parameters comprise at least one of the following parameters: the time each collection of goods is stored in the pick-up area, inventory area, equipment count, and personnel count.

According to an embodiment of the present disclosure, determining a planning model according to the cargo parameter and the parameter constraint range includes: determining a total cost function according to the warehouse parameters; and establishing a planning model by taking the warehouse parameters as decision variables, the total cost function as a target function and the parameter constraint range as constraint conditions.

According to an embodiment of the present disclosure, determining warehouse parameters according to the data planning model includes: and calculating the planning model to obtain the value of the warehouse parameter when the total cost is lowest.

Another aspect of the present disclosure provides a warehouse data processing apparatus, including an obtaining module, configured to obtain a plurality of cargo sets, cargo parameters of each of the plurality of cargo sets, and a warehouse parameter constraint range, where the cargo sets include at least one cargo; the first determining module is used for determining a planning model according to the cargo parameters and the parameter constraint range; and a second determining module for determining warehouse parameters according to the planning model.

According to an embodiment of the present disclosure, the first determining module includes: the third determining submodule is used for determining a total cost function according to the warehouse parameters; and the establishing module is used for establishing a planning model by taking the warehouse parameters as decision variables, taking the total cost function as a target function and taking the parameter constraint range as a constraint condition.

Another aspect of the disclosure provides a computing device comprising: one or more processors; storage means for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.

Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.

According to the embodiment of the disclosure, the warehouse parameters are solved through the planning model, and the planning model can obtain the optimal solution in a short time, so that the warehouse parameters which enable the total cost of the warehouse to be optimal are obtained. Compared with the layout of planning the warehouse by means of manual experience, the warehouse planning method is more reasonable and higher in efficiency.

Drawings

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

FIG. 1 schematically illustrates an exemplary system architecture to which a warehouse data processing method may be applied, according to an embodiment of the present disclosure;

FIG. 2 schematically illustrates a flow diagram of a warehouse data processing method according to an embodiment of the present disclosure;

FIG. 3 schematically illustrates a block diagram of a warehouse data device, in accordance with an embodiment of the present disclosure;

FIG. 4 schematically illustrates a block diagram of a first determination module according to an embodiment of the disclosure; and

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.

Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).

The embodiment of the disclosure provides a storage data processing method and a device capable of applying the method. The method comprises the steps of obtaining a plurality of goods sets, goods parameters of each goods set in the goods sets and a warehouse parameter constraint range, wherein the goods sets comprise at least one kind of goods; determining a planning model according to the goods parameters and the constraint range of the warehouse parameters; and determining warehouse parameters according to the planning model.

Fig. 1 schematically illustrates an exemplary system architecture 100 to which the warehouse data processing method may be applied, according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.

As shown in fig. 1, a system architecture 100 according to this embodiment may include an input data system 101, a prediction system 102, a planning system 103, an output system 104, and a network 105. Network 105 is the medium used to provide communication links between input data system 101, prediction system 102, planning system 103, and output system 104. Network 105 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The input data system 101 may be used to obtain data such as commodity data, shelf information, sales data, and the like.

The prediction system 102 may be used to predict inventory and sales of products based on data such as product data and sales data.

The planning system 103 may be configured to calculate data according to preset algorithms such as a shelf model selection algorithm and a commodity allocation algorithm, and obtain a calculation result.

The output system 104 may be configured to output the calculated data such as the cost area and the product layout.

According to the embodiment of the disclosure, the warehouse comprises a storage area and a picking area, wherein the storage area is an area for storing goods for a long time, and the picking area is an area for picking the goods, and the goods are temporarily stored in the area for picking the goods by a picking personnel. During the operation of the warehouse, part of goods in the storage area are moved to the picking area periodically to supplement the picked goods in the picking area. The safety stock days are the time of the goods stored in the picking area, and the product of the safety stock days of the goods and the daily average sales volume of the goods is equal to the quantity of the goods stored in the picking area. Equipment such as pallets and forklifts may also be included in the warehouse. In addition, the warehouse is provided with staff, including, for example, pick-up personnel and forklift personnel.

According to embodiments of the present disclosure, Stock Keeping Units (SKUs) can be used to differentiate the types of goods, one SKU for one kind of goods and different SKUs for different kinds of goods.

Fig. 2 schematically shows a flow chart of a warehousing data processing method according to an embodiment of the present disclosure.

As shown in fig. 2, the method includes acquiring a plurality of cargo sets, cargo parameters of each of the plurality of cargo sets, and warehouse parameter constraint ranges in operation S210.

According to an embodiment of the present disclosure, a collection of goods may include a good. When the number of the types of goods is large or the goods need to be managed according to different types of partitions, the goods set may also include one type of goods or multiple types of goods. Compared with the calculation process which is executed once for each kind of goods, the calculation is carried out after the goods are divided into goods sets, and the calculation times are fewer.

According to the embodiment of the disclosure, the goods set can be divided according to the sales data of the goods. For example, all the goods may be sorted from large to small according to sales volume, and then divided according to a certain proportion, for example, the goods with the top 20% are selected to form a goods set 1, the goods with the top 20% to 40% are selected to form a goods set 2, the goods with the top 40% to 60% are selected to form a goods set 3, the goods with the top 60% to 80% are selected to form a goods set 4, and the goods with the top 80% to 100% are selected to form a goods set 5. It is understood that other divisions may be used in practice, and the present disclosure is not limited thereto.

According to embodiments of the present disclosure, the cargo parameters may include, for example, cargo volume, cargo inventory, and quantity of goods sold, among others.

According to embodiments of the present disclosure, the volume and inventory of each cargo may be read through an input data system. The historical inventory data and the historical sales data of each kind of goods can be read through the input data system, and the average daily inventory and the average daily sales of each kind of goods in the preset time can be calculated according to the historical inventory data and the historical sales data. Wherein, the average daily stock is more than or equal to daily sales. Illustratively, in this embodiment, the predetermined time is the last month.

According to the embodiment of the disclosure, if there is one cargo in the cargo collection, the cargo parameter of the cargo is used as the cargo parameter of the cargo collection. If there are a plurality of kinds of goods in the goods collection, the average value of the parameter goods of all kinds of goods in the goods collection is calculated as the parameter of the goods collection, for example, the average value of the volume, the average value of the stock quantity, the average value of the sales quantity, and the like.

According to the embodiment of the disclosure, only considering the current inventory and daily sales may result in a smaller warehouse area plan and not meet the storage demand required for the next years. Thus, the predicted growth rate may also be used as a cargo parameter. The growth rate of each good can be predicted from historical sales growth for that good.

According to embodiments of the present disclosure, the warehouse parameter constraint range may be used to limit the value range of each warehouse parameter. For example, warehouse parameters may include the time each collection of goods is deposited in the picking area, the inventory area, the number of equipment and personnel, etc. Accordingly, warehouse parameter constraints may include inventory quantity constraints, pick-up area constraints, inventory area constraints, and quantity of personnel constraints, among others. The warehouse parameter constraint range can be set according to actual needs.

Then, in operation S220, a planning model is determined according to the cargo parameters and the warehouse parameter constraint ranges.

According to an embodiment of the present disclosure, operation S220 may include, for example, determining a total cost function according to the warehouse parameters, establishing a planning model with the warehouse parameters as decision variables, the total cost function as a target function, and the warehouse parameter constraint range as a constraint condition.

For example, warehouse parameters may include the number of safe-stock days per collection of goods, pick zone area, inventory zone area, number of devices and number of people, etc. Wherein, the equipment quantity can include goods shelves quantity and fork truck quantity etc. and personnel quantity can include the personnel quantity of picking up goods and fork truck personnel quantity etc..

According to the embodiment of the present disclosure, for example, programming languages such as MATLAB, C + + and the like may be used to build and solve a planning model.

In operation S230, warehouse parameters are determined according to the planning model.

Operation S230 may include, for example, obtaining, by the computational planning model, values of warehouse parameters at which a total cost of satisfying the constraints is lowest, according to an embodiment of the present disclosure.

As an example shown in operation S220, the calculation planning model may obtain the area of the picking area, the area of the stocking area, the number of safe inventory days of the goods collection, the number of shelves, the number of forklifts, the number of picking personnel, and the number of forklifts personnel.

Operation S220 is further described below with reference to specific embodiments.

According to an embodiment of the present disclosure, first, the following preset parameters are configured: monthly average rent per unit area cost c_zMonthly average cost of goods shelves in goods picking area c_p′Monthly average cost of individual fork lift truck c_tMonthly average cost of storage area shelves c_s′Actual area e of the racks in the individual picking area_p(including shelf area and corresponding aisle area), actual footprint e of individual inventory area shelves_sMonthly average cost of goods picking personnel c_pMonthly average cost of fork lift personnel c_sThe maximum storage number n of the ith goods collected in the goods grid of the stock area_s，iThe maximum storage number n of the ith goods in the goods grid of the picking area_p，iAverage inventory of ith set of items S_iAverage daily sales d of ith cargo_iFuture daily sales df for ith cargo set_iNumber of shelf layers L in stock area_sTotal number of cells per shelf F_pThe number f of the goods grids of each layer of the goods shelf in the stock area, the working time of the personnel per day T, and the efficiency r of the goods picking personnel_p(number of items picked per person per hour), efficiency r of forklift personnel_s(number of trays processed per person per hour), upper limit of area of picking area A_p(optional constraints), upper limit of the storage area A_sUpper limit of storage area N_s. Wherein n is_s，iCan be obtained by the available volume of the cargo grid/cargo volume, n_p，iObtainable from the available volume of the cargo/cargo volume of the cargo grid, F_p＝f*L_s。

Then, warehouse variables are determined. In this embodiment, the warehouse variables may include the following variables:

number of days of safety stock a for ith goods set to be allocated to pick-up area_iShowing the area A of the goods picking area₁And represents the area A of the storage area₁Goods shelves number N in goods picking area₁Number of shelves N in stock area₂The number N of pallets to be handled from the stock area_tThe number of picking personnel n₁The number n of forklift personnel₂。

Wherein N is₁＝(∑_i(a_id_i/n_p，i))/F_p，∑_i(a_id_i/n_p，i) Indicating the total number of pickups required in the pick-up area. N is a radical of₂＝(∑_i((S_i-a_id_i)/n_s，i))/F_sWherein ∑_i((S_i-a_i d_i)/n_s，i) Indicating the total number of cells in the storage area required to be stored.

Wherein v is_iIs the unit cargo volume, sigma, corresponding to the ith cargo group_i(df_i-a_id_i)*v_iIndicating the total volume of goods to be handled from the stock area, v_TThe volume of cargo that a single pallet can carry. Number of picking personnel n₁＝(∑_id_i)/r_pIt can be seen that the picker is now constant, i.e. the picker is equivalent to known data, and n may not be calculated subsequently₁。n₂＝N_t/(T*r_s)。

Next, warehouse parameter constraints are configured. In this embodiment, the warehouse parameter constraint range may include: the quantity of each goods set put into the picking area can not exceed the average total stock, namely 0 ≦ a_i≤S_i/d_i(ii) a The area of the goods-picking area can not exceed a given upper limit A_pI.e. N₁*e_p≤A_p(ii) a The area of the storage region must not exceed a given upper limit A_sI.e. N₂*e_s≤A_s(ii) a The personnel of the forklift truck do not exceed N at most_sI.e. n₂≤N_s。

Next, a total cost function is determined. From the warehouse parameters described above, a total cost function may be determined as

And then, establishing a planning model by taking the warehouse parameters as decision variables, taking the total cost function as an objective function and taking the parameter constraint range as a constraint condition.

The method for obtaining a plurality of cargo collections and cargo parameters for each of the plurality of cargo collections is further described with reference to another embodiment.

Assuming 10 cargo items, the parameters are shown in table 1. For convenience of description, the cargo having SKUs of 0, 1, 2, 3, 4 will be referred to as cargo 0, cargo 1, cargo 2, cargo 3, cargo 4, respectively, hereinafter.

TABLE 1

SKU	Volume of	Average inventory	Sales volume (daily sales volume)	Future sales volume
					0	3	40	20	25
1	4	20	5	15
					2	2	20	15	20
3	5	30	25	30
					4	3	15	10	20

The goods are sequenced according to the sales volume, and the sequence of the goods 3, the goods 0, the goods 2, the goods 4 and the goods 1 is obtained. Then, the goods 3 and the goods 0 which are two top ranks form a goods set band A, and the goods 2, the goods 4 and the goods 1 which are three last ranks form a goods set band B. And calculating the cargo parameters of the band A according to the cargo parameters of the cargo 3 and the cargo 0, and calculating the cargo parameters of the band B according to the cargo parameters of the cargo 2, the cargo 4 and the cargo 1. The calculation results are shown in table 2.

TABLE 2

SKU	Volume of	Average inventory	Sales volume (daily sales volume)	Future sales volume
					band A	4	70	45	55
band B	3	55	30	55

According to an embodiment of the present invention, the picking area and the stock area of the warehouse may include a variety of shelves from which it is necessary to select a shelf actually used. Based on the above, the shelf index can be respectively calculated for all shelf types in the picking area and all shelves in the stock area, and then the optimal shelf can be determined as the actual shelf used in the warehouse. More specifically, the shelf index of each shelf may be calculated according to the following formula:

the shelf index is the total available volume of the shelf/the actual area of the warehouse,

then, the shelf with the highest shelf index is selected as the shelf actually used by the warehouse.

The method of selecting a shelf is further described below in conjunction with another embodiment.

Table 3 schematically shows specification parameters of the shelves in the picking area, including the number of shelves, the number of layers, the number of shelves per layer, the monthly cost, the projection area, and the upper limit of the shelf utilization.

TABLE 3

Table 4 shows specification parameters of the shelves in the stock area, including the shelf number, the number of volume layers, the number of shelves per layer, the monthly cost, the projected area, and the upper limit of shelf utilization. .

TABLE 4

And calculating the shelf index of the goods picking area shelf according to a shelf index calculation formula to obtain that the shelf index of the shelf 1 is 60 × 0.48/10-2.88, and the shelf index of the shelf 2 is 50 × 0.5/7-3.57, so that the shelf 2 is selected. The shelf index of the shelf in the stock area is calculated to obtain the shelf index of 100 x 0.64/12 to 5.33 for the shelf 3 and the shelf index of 150 x 0.64/15 to 6.4 for the shelf 4, so the shelf 4 is selected.

FIG. 3 schematically illustrates a block diagram of a warehouse data device, in accordance with an embodiment of the present disclosure.

As shown in fig. 3, the apparatus 300 includes an obtaining module 310, a first determining module 320, and a second determining module 330.

The obtaining module 310 is configured to obtain a plurality of cargo sets, cargo parameters of each cargo set in the plurality of cargo sets, and a warehouse parameter constraint range, where a cargo set includes at least one cargo.

The first determining module 320 is configured to determine a planning model according to the cargo parameter and the parameter constraint range.

A second determining module 330, configured to determine warehouse parameters according to the planning model.

Fig. 4 schematically illustrates a block diagram of a first determination module according to an embodiment of the present disclosure.

As shown in fig. 4, the first determination module 320 includes a third determination submodule 410 and a setup module 420.

A third determining sub-module 410 for determining a total cost function based on the warehouse parameters.

And the establishing module 420 is configured to establish a planning model by using the warehouse parameters as decision variables, the total cost function as a target function, and the parameter constraint range as a constraint condition.

Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.

For example, any plurality of the obtaining module 310, the first determining module 320 and the second determining module 330 may be combined and implemented in one module, or any one of them may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 310, the first determining module 320, and the second determining module 330 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware. Alternatively, at least one of the obtaining module 310, the first determining module 320 and the second determining module 330 may be at least partially implemented as a computer program module, which when executed, may perform a corresponding function.

FIG. 5 schematically illustrates a block diagram of a computer system suitable for implementing the above-described method according to an embodiment of the present disclosure. The computer system illustrated in FIG. 5 is only one example and should not impose any limitations on the scope of use or functionality of embodiments of the disclosure.

As shown in fig. 5, a computer system 500 according to an embodiment of the present disclosure includes a processor 501, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)502 or a program loaded from a storage section 508 into a Random Access Memory (RAM) 503. The processor 501 may comprise, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 501 may also include onboard memory for caching purposes. Processor 501 may include a single processing unit or multiple processing units for performing different actions of a method flow according to embodiments of the disclosure.

In the RAM 503, various programs and data necessary for the operation of the system 500 are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. The processor 501 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 502 and/or the RAM 503. Note that the programs may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, system 500 may also include an input/output (I/O) interface 505, input/output (I/O) interface 505 also being connected to bus 504. The system 500 may also include one or more of the following components connected to the I/O interface 505: an input portion 506 including a keyboard, a mouse, and the like; an output portion 507 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage portion 508 including a hard disk and the like; and a communication section 509 including a network interface card such as a LAN card, a modem, or the like. The communication section 509 performs communication processing via a network such as the internet. The driver 510 is also connected to the I/O interface 505 as necessary. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 510 as necessary, so that a computer program read out therefrom is mounted into the storage section 508 as necessary.

According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 509, and/or installed from the removable medium 511. The computer program, when executed by the processor 501, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.

According to embodiments 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: 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), 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 disclosure, 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. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include ROM 502 and/or RAM 503 and/or one or more memories other than ROM 502 and RAM 503 described above.

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 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 various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.

The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims

1. A method of processing warehouse data, comprising:

acquiring a plurality of goods sets, goods parameters of each goods set in the goods sets and warehouse parameter constraint ranges, wherein the goods sets comprise at least one kind of goods;

determining a planning model according to the goods parameters and the constraint range of the warehouse parameters; and

and determining warehouse parameters according to the planning model.

2. The method of claim 1, wherein the cargo parameter comprises at least one of: cargo volume, cargo inventory, and cargo sales.

3. The method of claim 1, wherein the warehouse parameter constraint range comprises at least one of: inventory quantity constraints, pick-up area constraints, inventory area constraints, and personnel quantity constraints.

4. The method of claim 1, wherein the warehouse parameters comprise at least one of: the time each collection of goods is stored in the pick-up area, inventory area, equipment count, and personnel count.

5. The method of claim 1, wherein determining a planning model based on the cargo parameters and the parameter constraint ranges comprises:

determining a total cost function according to the warehouse parameters; and

and establishing a planning model by taking the warehouse parameters as decision variables, the total cost function as a target function and the parameter constraint range as constraint conditions.

6. The method of claim 1, wherein the determining warehouse parameters from the data planning model comprises:

and calculating the planning model to obtain the value of the warehouse parameter when the total cost is lowest.

7. A warehouse data processing apparatus, comprising:

the system comprises an acquisition module, a storage module and a processing module, wherein the acquisition module is used for acquiring a plurality of goods sets, goods parameters of each goods set in the goods sets and warehouse parameter constraint ranges, and the goods sets comprise at least one kind of goods;

the first determining module is used for determining a planning model according to the cargo parameters and the parameter constraint range; and

and the second determining module is used for determining warehouse parameters according to the planning model.

8. The apparatus of claim 7, the first determination module, comprising:

the third determining submodule is used for determining a total cost function according to the warehouse parameters; and

and the establishing module is used for establishing a planning model by taking the warehouse parameters as decision variables, taking the total cost function as a target function and taking the parameter constraint range as a constraint condition.

9. A computing device, comprising:

one or more processors;

a storage device for storing one or more programs,

wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1 to 6.

10. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 6.